Revista Română de Statistică Supliment

Transcription

Institutul Naţional de Statistică
National Institute of Statistics
INSTITUTUL NAŢIONAL DE STATISTICĂ
Revista Română de Statistică
B-dul Libertăţii, nr. 16, sector 5,
Bucureşti
Telefon/fax: 0213171110
e-mail: [email protected]
www.revistadestatistică.ro/supliment
ISSN 2359 – 8972
Revista Română
de Statistică
Supliment
Romanian Statistical Review
Supplement
Simpozionul Ştiinţific Internaţional
“România pe calea redresării.
Decizii economico-financiare luate sub risc”
10-11 decembrie 2015
1/2016
www.revistadestatistică.ro/supliment
REVISTA ROMÂNĂ DE STATISTICĂ SUPLIMENT
SUMAR / CONTENTS 1/2016
USING THE DYNAMIC MODEL ARMA TO FORECAST THE
MACROECONOMIC EVOLUTION
Prof. Constantin ANGHELACHE PhD., Prof. Janusz GRABARA PhD.,
Assoc. prof. Alexandru MANOLE PhD.
3
STATISTIC INDICATORS ON THE RELATIONSHIP BETWEEN
ECONOMY AND FOREIGN TRADE OF THE REPUBLIC OF MOLDOVA
(INCLUDING WITH ROMANIA) DURING 2003- 2014
14
Prof. Ioan PARTACHI PhD., Senior Lecturer Natalia ENACHI
USING THE AUTOREGRESSIVE MODEL FOR THE ECONOMIC
FORECAST DURING THE PERIOD 2014- 2018
21
Prof. Constantin ANGHELACHE PhD., Prof. Ioan Constantin DIMA PhD.,
Lect. Mădălina-Gabriela ANGHEL PhD.
MANAGING FINANCIAL INSTRUMENTS BY DEVELOPMENT BANK OF
ROMANIA
32
Prof. Vergil VOINEAGU PhD., Prof. Michal BALOG, PhD.,
Daniel DUMITRESCU PhD. Student,
Diana SOARE (DUMITRESCU) PhD Student
ESSENTIAL ASPECTS REGARDING THE OPTIMAL PREVENTION
38
Prof. Constantin ANGHELACHE PhD., Prof. Mario G.R. PAGLIACCI PhD.
Emilia STANCIU PhD. Student, Cristina SACALĂ PhD. Student
SPECIFIC ELEMENTS OF CORRELATION BETWEEN INFOMATION
AND RISK
43
Assoc. prof. Alexandru MANOLE PhD., Emilia STANCIU PhD. Student .
Alexandru URSACHE PhD. Student
MODEL OF STATIC PORTFOLIO CHOICES
Lect. Mădălina Gabriela ANGHEL PhD., Gyorgy BODO Phd. Student.
Okwiet BARTEK, PhD. Student
49
MODEL OF STATIC PORTFOLIO CHOICES IN AN ARROW-DEBREU
ECONOMY
54
Prof. Gabriela Victoria ANGHELACHE PhD.,
Lect. Mădălina Gabriela ANGHEL PhD., Gyorgy BODO Phd. Student
www.revistadestatistica.ro/supliment
Revista Română de Statistică - Supliment nr. 1 / 2016
PORTFOLIO MANAGEMENT AND PREDICTABILITY
59
Prof. Gabriela Victoria ANGHELACHE PhD., Prof. Vladimir MODRAK, PhD ,
Lect. Mădălina Gabriela ANGHEL PhD., Marius POPOVICI PhD. Student
SIGNIFICANT ASPECTS OF INVESTMENT DYNAMICS
64
Prof. Gabriela Victoria ANGHELACHE PhD.,
Lect. Mădălina Gabriela ANGHEL PhD., Marius POPOVICI PhD. Student
MODEL FOR ESTIMATING FINANCIAL RESULTS AT COMPANY
LEVEL BY USING SIMPLE LINEAR REGRESSION
Andreea-Gabriela BALTAC PhD. Student
Zoica DINCA (NICOLA) PhD. Student
RISK AVERSION AND INDIVIDUAL PREFERENCES MODELLING
Prof. Constantin ANGHELACHE PhD.,
Lecturer Mădălina Gabriela ANGHEL PhD.,
Assoc. prof. Aurelian DIACONU PhD.
70
77
THE MAJOR MACROECONOMIC EVOLUTIONS BY THE
END OF JULY 2015
83
Prof. Constantin ANGHELACHE PhD., Alexandru URSACHE PhD. Student
THE GROSS DOMESTIC PRODUCT EVOLUTION IN ROMANIA
Assoc. prof. Alexandru MANOLE PhD., Prof. Sebastian KOT, PhD,
Marius POPOVICI PhD. Student, Georgiana NIŢĂ PhD. Student
87
THE INFLATION (CONSUMER PRICES) IN THE ROMANIAN ECONOMY 96
Prof. Constantin ANGHELACHE PhD.
Georgiana NIŢĂ PhD. Student, Alexandru BADIU PhD. Student
THE MAIN ASPECTS REGARDING THE DYNAMICS OF THE
INDUSTRIAL PRODUCTION INDICES
100
Prof. Constantin ANGHELACHE PhD., Assoc. prof. Aurelian DIACONU PhD.,
Cristina SACALĂ PhD. Student
PRODUCTION OF SERVICES DURING THE LAST YEAR
104
Prof. Constantin ANGHELACHE PhD., Prof. Radu Titus MARINESCU PhD.,
FINANCIAL INCLUSION, FOCUS ON ROMANIAN MIGRANTS AND
THEIR FAMILIES
110
Prof. Constantin ANGHELACHE PhD., Olivia Georgiana NITA PhD. Student,
Alexandru BADIU PhD. Student
2
Romanian Statistical Review - Supplement nr. 1 / 2016
Using the Dynamic Model ARMA to Forecast
the Macroeconomic Evolution
Bucharest University of Economic Studies
“Artifex” University of Bucharest
Prof. Janusz GRABARA PhD.
Czestochowa University of Technology
Abstract
The ARMA models, provide in the statistical analysis of time series,
a parsimonious description of a (weakly) stationary stochastic process in
terms of two polynomials, one for the auto-regression and the second for the
moving average. We want to estimate, by using the informatics soft Eviews,
the future evolutions of the gross domestic product in Romania, for the period
2009 -2013, obtaining thus, on the ground of the official evolutions during the
period 1991 - 2008, a model AR meant to grasp ex post the evolution of the
economic growth from our country, for the period 2009 -2013. To achieve ex
post the forecast for the evolution of the GDP, during the period 2009 -2013,
we will use as “sample” the data published in the interval 1991 -2008.
Key words: dynamic, model ARMA, validity, prediction, autoregressive
model, correlation, macroeconomic evolution
Introduction
In this paper we shall elaborate an autoregressive model of prediction
ARMA, utilizing at first stage the data published by UNCTAD, for the
variables gross domestic product (GDP), flow of direct foreign investments
(DFI), respectively final consumption (FC) in the case of Romania , during
the statistical period 1991 -2008. Through the informatics soft Eviews, we
undertake to achieve the estimation of the future evolutions of the gross
domestic product in Romania , for the period 2009 -2013, obtaining thus, on
the ground of the official evolutions during the period 1991 - 2008, a model AR
meant to grasp ex post the evolution of the economic growth from our country,
for the period 2009 -2013. This model is developed as a first precursory stage,
which undertakes to validate the prediction model. Taking into consideration
the above submitted aspects, we shall continue our steps meant to build up an
autoregressive model of prediction. In this respect, we shall utilize the data
3
basis published UNCTAD, which is grasping the evolution of the variables
which are going to be used in the model construction, respectively: variables
gross domestic product (GDP), flow of direct foreign investments, respectively
final consumption ((defined in the frame of the model as FC) of the statistical
interval 1991 - 2013. Thus, we shall use as “sample” the data published in the
interval 1991 -2008, in order to achieve ex post the forecast for the evolution
of the GDP, during the period 2009 -2013.
Testing the validity of the prediction model ARMA
Through the Model 1, which defines EQ01, a first step as to building up
the prediction model has been accomplished. Out of the statistical tests utilized for
validating the econometric, we observe that both the autoregressive component
GDP(-1), and the residual variable “C”, which grasps the influence of those
factors not included in the frame of the model, and mainly the dependent variable
final consumption (FC), have a significant contribution to the estimation of the
economic growth at the level of Romania , which is underlined by the outcomes
of the probability of each dependent variable, included in the frame of the model.
Meantime, the value R-squared is indicating the sturdiness of the built
up model, emphasizing as well its validity in proportion of 99.88%. The high
percentage of validity of the built up model is completed by the result of the test
Prob (F-statistic), corroborated with the result of the statistical test Adjusted
R-squared, which confirms once more the correctness of the developed model
(Model 1).
The eequation of the built up model (Table 1 - Model 1) is of the
form:
(EQ01)
GDP = 1.109218*FC - 0.683974*DFI + 0.183799*GDP (-1) - 3319.143
Out of the equation (EQ01) of the developed model (Model 1), based
on both the values of the coefficients of the econometric model, and on the
interpretation of the probabilities related to these coefficients, previously
taken into account, we observe that the variable final consumption has a very
high significance degree as far as inducing the evolution of GDP of our country
is concerned followed, from the point of view of the significance in describing
the evolution of GDP, by the variable flow of direct foreign investments and
the autoregressive variable GDP on the lag 1 (GDP(-l)).
To note also the value taken by the free term C, (-3319.143), which
grasps the impact or the negative influence on the evolution of the gross
domestic product, of the variables not taken into consideration and implicitly
not-comprised in the construction of the prediction model.
4
We shall continue with the test for the existence of the serial correlation
in the frame of the model represented by the equation EQ01, by applying
the test “Breusch-Godfrey Serial Correlation LM Test”, (Table 2), approach
achieved with the help of the informatics soft Eviews 7.2.
Thus, following the previously statements, we observe that the value
of the probability Chi-Square, on the testing lag 1 counts for 0.0247, an
outcome implying the acceptance of the null hypothesis alleging that : « There
is a serial correlation on the lag 1 ».
Model 1. The model defining the first equation noted as EQ01
Table 1
Dependent Variable: GDP
Method: Least Squares
Date: 07/26/15 Time: 16:56
Sample (adjusted): 1992 2008
Included observations: 17 after adjustments
Variable
DFI
FC
C
GDP(-r:
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
Coefficient
-0.683974
1.109218
-3319.143
0.183799
0.998845
0.998578
2002.360
52122793
-151.0771
3747.330
0.000000
Std. Error
t-Statistic
0.439632
-1 5557S6
0.078647
14103Ă0
1285.126
-2.582737
0.076792
2.393466
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwa rz criterion
Hannan-Quinn enter.
Durbin-Watson stat
Prob.
0.1438
0.0000
0.0227
0.0325
6614442
53107.36
18.24436
18.44042
18.26385
0.948488
The fact that the outcomes of the statistical test “Breusch-Godfrey
Serial Correlation LM Test” confirmed the existence of the serial correlation
in the frame of the developed model, will make us achieve a parallel estimation
of the evolution of the gross domestic product at the level of our country,
but this time we shall utilize the component AR , which eliminates the serial
correlation from the statistical series subject of the prediction, the model thus
obtained having normally a bigger power of prediction, consequently a higher
accuracy of the economic growth forecast.
5
The test Breusch-Godfrey for testing the serial correlation for the
autoregressive model (EQ01)
Table 2
Breusch-Godfrey Serial Correlation LM Test:
F-statistic
5.058960
Prob. F{1,12>
Obs*R-squared
5.041475
Prob. Chi-Square(l)
Test Equation:
Dependent Variable: RESID
0.0441
0.0247
Date: 07/26/15 Time: 17:05
Sample: 1992 2008
Included observations:
17
Presample missing value lagged residuals set to zero.
Variable
Coefficient Std. Error t-Statistic
DFI
0.283234
0.403914 0.701224
FC
-0.033152 0.070220 -0.472116
C
354.2602
1132.869 0.312711
GDP(-1)
0.009891
0.067181 0.147223
RESIDf-1)
0.594914
0.264499 2.249213
R-squared
0.296557
Mean dependentvar
Adjusted R-squared
0.062076
S.D. dependent var
S.E. of regression
1747.984
Akaike Info criterion
Sum squared resid
36665395
Schwa rz criterion
Log likelihood
-148.0871 Hannan-Qulnn enter.
F-statistic
1.264740
Durbin-Watson stat
Prob(F-statistlc)
0.336755
Prob.
0.4965
0.6453
0.7599
0.8854
0.0441
4.87E-12
1804.903
13.01024
18.25531
18.03460
1.731636
Referring to the econometric model which submit the serial correlation,
previously represented through EQ01 (Model 1), through the forecasting
function provided in the frame of Eviews 7.2, for the autoregressive models,
we achieved the representation (Table 3), which emphasize ex post the
prediction of the GDP for the period 2009 - 2013. Thus, according to the
previous discussion, the implementation of this prediction on the evolution
of the GDP, of the period 2009 - 2013, bears a purely theoretical role as
to finalizing and validating the prediction model which will be used for the
forecast of the GDP for the period 2014 -2018, at the level of Romania .
6
The representation of the prediction for the GDP {Model 1), during the
period 2009 -2013, in Romania
Table 3
Out of the test achieved for the forecast of the period 2009 - 2013,
evidenced by the equation EQ01, we observe that the value “RootMean
Squared Error”, is grasping a prediction error of 9035.72, between the real
values of the evolution of the GDP and the forecasted ones.
In order to improve the prediction achieved through the equation
EQ01, resulting out of the ModelOl, and to increase the accuracy level of the
prognosis achieved for the evolution of the gross domestic product, we shall
remove the serial correlation, by including the component AR(1), which would
improve the quality of autoregressive model. Thus, by including the component
AR(1) in the frame of the model previously set up (Model 1), according to the
outcomes of the statistical tests generated by the data processing with the help
of the informatics soft Eviews 7.2, we remark a higher qualitative level, which
will generate a higher accuracy of the prognosis, very close to the real values
recorded by evolution of the GDP, during the period 2009 -2013, getting
thus a model which may be applied in practice in the forecasting processes
performed by the institutions in charge.
The autoregressive model
The high validity of the developed model (Table 4 - Model 2) is
underlined by the tests R-squared and Adjusted R-squared, which confirm
the correctness of the model, in proportion of 99.92%, respectively 99.90%.
Meantime the value of the test Prob(F-statistic), is confirming the accuracy of
the developed model.
7
Model 2. The autoregressive model which defines the 2nd equation
EQ02
Table 4
Dependent Variable: FIB
Date: 07/26/15 Time: 17:04
Included observations: 16 after adjustments
Convergence achieved after 10 iterations
Variable
DFI
FC
C
PIFJ(-1)
AR{1)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistlc
Pro b[F-stati stlc)
Inverted AR Roots
Coefficient
-0.164772
1.153688
-3873.434
0.106970
0.744597
0.999283
0.999022
1673.224
30796475
-138.4656
3831.418
0.000000
.74
Sid. Error
t-Statistic
0.309899
-0 531697
0.057659
20.00877
3189.998
-1.214243
0.059576
1.795522
0.233209
3.192828
Mean dependentvar
S.D. dependent var
Schwarz criterion
Hannan-Quinn criter.
Durbln-Watson stat
Prob.
0.6055
0.0000
0.2501
0.1001
0.0086
68980.97
53502.50
17.93320
13.17463
17.94556
1.298047
Another Un alt criterion making us to anticipate that the second
developed model (Model 2), is more performing in order to estimate the
evolution of the economic growth, is given by a cumulus of statistical criteria,
such as Akaike, Schwarz and Hannan-Quinn. Although, when comparing the
two models built up we observe that the value of the test Schwarz, does not
change, the tests Akaike and Hannan-Quinn are underlining better outcomes
for the second model, which contains the component AR(1).
The new developed model, Model 2 is represented through the
equation:
GDP = 1.15368*FC - 0.16477*DFI + 0.10697*GDP (-1) + 0.74459*AR(1)
- 3873.43 (EQ02)
As already previously mentioned, by including the component AR(1),
in the frame of the developed model (Table 4 - Model 2), the serial correlation
will be removed generating thus, a considerable improvement of the quality
of the prediction on the economic growth in Romania . Further on , we shall
test whether the new proposed model is still submit serial correlation, applying
in this respect the method “Breusch-Godfrey Serial Correlation LM Test”, as
proceeded previously. According to the outcomes of the test “Breusch-Godfrey
Serial Correlation LM Test”, (Table 5), we can deduce that the new proposed
8
model of prediction does not submit the serial correlation any more. This
interpretation is confirmed by the percentage of 25.35% of the probability
“Chi- Square” which, according to the statistical methodology, is rejecting
the presumed null hypothesis, according to which « There is serial correlation
on the lag 1 ».
Table 5. The test Breusch-Godfrey for testing the serial correlation for
the model AR (EQ02)
Breusch-Godfrey Serial Correlation LM Test
F-statistlc
0.887300
Prob. F£1,10>
Obs*R-squared
1.303978
Prob. Chi-Square(l)
Test Equation:
Date: 07/26/15 Time: 17:23
Sample: 1993 2008
Included observations:
16
Presample missing value lagged residuals set to zero.
Variable
Coefficient Std. Error t-Statistic
DFI
0.117757
0.335649
0.350835
FC
-0.005331
0.058232 -0.091540
C
1628.094
3642.580 0.446962
GDP(-1)
-0.015195
0.062018 -0.245008
AR(1)
-0.210139
0.323602 -0.649376
RESIDf-1)
0.450249
0.477989 0.941966
R-squared
0.081499
Mean dependent var
Adjusted R-squared
-0.377752
S.D. dependent var
S.E. of regression
1681.862
Sum squared resid
28286604
Schwarz criterion
Log likelihood
-137.7855
Hannan-Quinn enter.
F-statistlc
0.177460
Durbin-Watson stat
Prob(F-statistic)
0.964931
0.3684
0.2535
Prob.
0.7330
0.9289
0.6644
0.8114
0.5307
0.3684
-1.53E-07
1432.864
17.97319
18.26291
17.98302
1.592751
According to the tests achieved for the simulation of the prognosis
for the period 2009 - 2013, (Table 6), evidenced by the equation EQ02, we
observe that the value “Root Mean Squared Error” is grasping a prediction
error much smaller than the one underlined by the equation EQ01, previously
interpreted.
Thus, the prediction error or the difference between the real values
recorded by the evolution of the GDP, during the period 2009 -2013 levels up
to 3 570.03 as against those predicted through the first equation.
9
The representation of the prediction for the GDP (Model 2), during the
Table 6
If analysing comparatively he estimation achieved through the two
models built up, we observe that the model representing the component AR on
the lag 1, which eliminates the serial correlation of the data basis utilised for
the prediction of the GDP, at the level of Romania is more performing, having
a higher accuracy in respect of the economic growth forecast.
The fact that the second equation EQ02, which grasps the second
developed model, Model 2 is valid from the statistical point of view for the
prediction of the evolution of the GDP, on the basis of the data recorded at the
level of our country for the dependent variables dependent: final consumption
(FC) and flow of direct foreign investments (DFI), is confirmed by the
outcomes obtained as a result of the model implementation.
Out of the table submitted in the Table 7, resulting by applying the
models previously developed, on the basis of data which grasp both the
evolution of the final consumption, and that of the flow of DFI, variables
which are considerably influence the evolution of the economic growth in our
country, we observe that the prediction obtained through the utilization EQ02,
is showing a higher accuracy as against the evolution of the GDP predicted
through EQ01.
10
The parallel representation of the outcomes expost the predictions
grasped by EQ01 vs EQ02 for the period 2009 -2013
Table 7
obs
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
EQ01_PIBF
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
178175 .60
175200.1
186931.9
177976.1
190476.7
EQ02_PIBF
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
170754.00
167921.9
180082.9
169891.0
183945.9
GDP I
30593.07
20759.75
27951.94
31887.80
37618.60
36911.15
35616.63
41749.52
35995.56
37305.10
40585.89
45988.51
59466.02
75794.73
99172.61
122695.85
170616 95
204338.60
164344.38
164792.25
182610.67
169396 .06
186438.481
The fact that by utilizing the Modelul 2 and implicitly EQ02 we
shall achieve a prediction of high accuracy is confirmed also by the graphical
representation of the evolution of the GDP over the period 1991 -2013, parallel
with the two estimations achieved for the period 2009 - 2013, through the
models being utilized.
Taking into account the above explanations and proposals, we observe
that by utilizing a model of type AR in the prediction for the GDP, on the case of
Romania, we get outcomes of high accuracy if considering the real evolutions.
In this respect, we can deduce that the implementation of such a model for the
prognosis on evolution of the GDP, for the period 2014-2018, based on the
evolution of the variables final consumption (FC) and flow of direct foreign
investments (DFI), during the period 1991 -2013, can be a successful one.
In the graphical representation of the evolutions generated by the utilization
of EQ01 and EQ02, (Table 8), the blue line is grasping the prognosis resulting
from the implementation of EQ1, the red one evidencing the prognosis
resulting out of the utilization of EQ02, while the evolution grasped by the
green line is marking the real evolution of the GDP, according to the data
bases of UNCTAD.
11
The representation of the prediction on the GDP, through the two
models represented by EQ01 vs EQ02 during the period 2009 -2013, in
Romania
Table 8
Conclusions
The value R-squared is indicating the sturdiness of the built up model,
emphasizing as well its validity in proportion of 99.88%. The high percentage
of validity of the built up model is completed by the result of the test Prob
(F-statistic), corroborated with the result of the statistical test Adjusted R-squared,
which confirms once more the correctness of the developed model. The test for
the existence of the serial correlation in the frame of the model represented by
the equation EQ01, by applying the test “Breusch-Godfrey Serial Correlation
LM Test”, (Table 2), approach achieved with the help of the informatics soft
Eviews 7.2. and we observe that the value of the probability Chi-Square, on the
testing lag 1 counts for 0.0247, an outcome implying the acceptance of the null
hypothesis alleging that : « There is a serial correlation on the lag 1 ».
The implementation of the prediction on the evolution of the GDP,
of the period 2009 - 2013, bears a purely theoretical role as to finalizing and
validating the prediction model which will be used for the forecast of the GDP
for the period 2014 -2018, at the level of Romania. The autoregressive model
is underlined by the tests R-squared and Adjusted R-squared, which confirm
the correctness of the model, in proportion of 99.92%, respectively 99.90%.
Meantime the value of the test Prob(F-statistic), is confirming the accuracy of
the developed model.
12
The prediction error or the difference between the real values recorded
by the evolution of the GDP, during the period 2009 -2013 levels up to 3
570.03 as against those predicted through the first equation.
Using a model of type AR in the prediction for the GDP, on the case of
Romania, we get outcomes of high accuracy if considering the real evolutions.
In this respect, we can deduce that the implementation of such a model for the
prognosis on evolution of the GDP, for the period 2014-2018, based on the
evolution of the variables final consumption (FC) and flow of direct foreign
investments (DFI), during the period 1991 -2013, can be a successful one.
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13
Statistic Indicators on the Relationship between
Economy and Foreign Trade of the Republic
of Moldova (including with Romania) during
2003- 2014
Prof. Ioan PARTACHI PhD.
Senior Lecturer Natalia ENACHI ([email protected])
Academy of Economic Studies, Chisinau, Republic of Moldova
Abstract
The basic component of the national economy system and the
component of the foreign economic relations is – foreign trade. The foreign
trade balance of the country can ensure the balance of the national economy,
economic efficiency, increase of economic potential, benefit from the
experience of economically advanced countries etc. So, the analysis of the
interdependence between foreign trade and economy of a country is natural.
Key words: international openness, annual advance coefficient,
foreign trade contribution to GDP creation, coverage degree of imports by
exports, coverage index of imports by exports, country’s participation intensity
in the international exchange of goods.
Over time foreign trade was considered the centre of each country’s economy.
Thus, foreign trade area is considered not just a transaction of goods, but also a
necessary element of the production process and the national economy as a whole.
For the Republic of Moldova foreign trade plays a very important role,
motivated by a small internal market, which limits the production development,
insufficient quantity of raw material and energy sources needed to meet the
exigencies of the national economy, both for intermediate consumption
(production goods and services) and for the final consumption (private and
government consumption, households), which caused high imports increase.
The relationship between foreign trade and the national economy
has always been discussed in the works of researchers given the important
role of trade in economic growth of the country. Thus, the purpose of the
investigations carried out in the paper is to analyse the statistical indicators
that ground the investigation of this relationship.
As additional information necessary for calculating and analysing the
indicators that describe the relationship between foreign trade and Moldovan
economy, may be presented in the following table:
14
Evolution of statistical indicators underlying the characterization of the
relationship between foreign trade and Moldovan economy of during the
years (2003 - 2014)
Table 1
Indicators
Gross Domestic
Product (GDP),
million MDL (current
market prices)
Average annual
exchange official
rates, in MDL for
USD 1*
Gross Domestic
Product (GDP),
milion USD
The average annual
number of resident
population ( N p ),
thousand persons
2003
2004
2005
2006
2007
2008
227619
332032
337652
444754
553430
662922
113,943
112,3283
112,600
113,1319
112,1362
110,3895
11980,9
22598,2
22988,3
33408,0
44402,5
66056,3
33612,9
33604,0
33595,2
33585,2
33577,0
33570,1
Indicators
2009
2010
2011
2012
2013
2014
Gross Domestic
Product (GDP),
660430 771885
882349
888228
1100312 1111501
million MDL (current
market prices)
Average annual
exchange official
111,1134 112,3663 111,737 112,1122 112,5907 114,0388
rates, in MDL for
USD 1*
Gross Domestic
Product (GDP),
55437,6 55813,0 77016,2
77284,2
77967,2
77942,3
milion USD
The average annual
number of resident
33565,6 33562,1 33560,0
33559,5
33558,6
33556,4
population ( N p ),
thousand persons
Source: Prepared by the author based on information provided by the National Bureau of
Statistics (www.statistica.md) (quoted 02/03/2015)
*Note: Information presented by the National Bank of Moldova (https://bnm.md/md/medium_
exchange_rates) (quoted 03/02/2015)
Therefore, the analysis of the relationship between foreign trade and
the national economy can be performed using the following core indicators:
15
Evolution of statistical indicators on the relationship between foreign
trade and economy of Moldova during the years (2003 - 2014)
Table 2
Source: Elaborated by the author based on information provided by the National Bureau of
The degree of Moldova’s openness or international ventilation in 2014
was 96.4%, which decreased by 3 p.p. compared to 2013. A value below 100%
was recorded both in 2009 (83.9%) and in 2010 (92.8%). For the other years,
during the analysed period, the degree of openness recorded values over 100%,
reaching the maximum 114.2% in 2007. This share is due to the high values
of imports, as the local market is relatively narrow and the domestic base of
raw materials and energy resources is insufficient to cover the country’s needs.
The share of foreign trade between Moldova and Romania in GDP was 15.6%
in 2014, increasing by 1.4 p.p. compared to 2013 and by 6.1 p.p. compared to
2003.
The annual advance coefficient in 2014 was 97%; its value below
16
100% indicates that the contribution of foreign trade of goods is insufficient for
Moldova’s domestic economic processes. Such a contribution is shown in years:
2004 (95.7%), 2006 (97.1%), 2008 (93.8%) 2012 (96.0%), 2013 (98.2%). The
remaining years of the period under review show that the dynamic index the of
merchandise trade volume exceeds the dynamic index of the nominal GDP in
our country. For most years in the period 2003 – 2014 can be stated a sufficient
contribution of the merchandise foreign trade of Moldova with Romania, apart
from 2009 (representing 66.3%) and 2012 (being 99%). The advance of the
dynamics index of country’s foreign trade with Romania to the index of nominal
dynamics of GDP growth registered a peak of 124.4% in 2011.
During the analysed period (2003 - 2014) the share of Moldova’s
trade deficit with all foreign countries in GDP, including Romania, suggests
the idea of a passive role of foreign trade in domestic economic processes. The
negative contribution of merchandise trade with Romania in the creation of
GDP in 2014 was – 4.6%, which, compared to the previous year, increased by
0.7 p.p., due to increased trade deficit with Romanian of 18.7%. The passive
maximum contribution of foreign trade between Moldova and Romania was
reached in 2006, constituting -5.6%.
The coefficient of variation of Moldova’s trade balance in during the
years 2004 - 2008 shows that, on average, for every increase unit in GDP,
the trade deficit of Moldova with Romania increased by 0.02 units, while in
2010 -2013 the trade deficit with Romania on average for each unit of GDP
increased by 0.01 units. In 2014, compared to 2013, for every unit of GDP
decrease corresponded an increase of 0,007 units of Moldova’s trade deficit
with Romania.
Regarding the coverage degree of imports by exports of Moldovan
goods with other countries, it can be revealed that during the last 12 years
there have been recorded values under 100%. Thus, total merchandise imports
in 2014 were covered by exports at a rate of 44.0%, which, compared to the
previous year, this coverage decreased slightly by 0.2 percentage points, while
compared to 2003 (being 56 3%) fell considerably (12.3 p.p.). Imports of goods
from Romania were covered with exports from this country in 2014 at a rate
of 54.0%, which, compared to the previous year, decreased by 3 percentage
points and 38.3 percentage points compared to 2003, when was registered the
highest coverage rate in the analysed period – 92.3%.
The index of Moldova’s imports coverage of total merchandise through
exports in 2014 was 99.5%, showing a slight negative trend explained by the
reduction of imports coverage by exports compared to 2013. This is because,
exports decreases more (by 3.7%) compared to the decrease of imports (3.2%).
Moldova’s index of imports coverage by exports from Romania was 94.7%
17
in 2014, being explained, in this case by a stronger import growth (11.2%)
compared to export growth of the previous year (5.6%). So the value of
imports coverage index with Romania shows that the trade deficit of Moldova
with Romania increased in 2014 (being USD 369.1 million) from USD 58.1
million in 2013. Such adverse circumstances – reduction of imports coverage
payments generated through revenue earned from exports compared to the base
period are emphasised in the years 2005, 2010, 2012, 2013, where the most
considerable reduction was recorded in 2004 of 34.7 p.p. (imports coverage
index was 65.3%). During the years (2006-2009) and in 2011 imports coverage
index with Romania has showed values above 100%, where the highest value
of 135.4% was known in 2009 (caused by the financial crisis in America).
This year is characterized by a more pronounced pace of decline in imports
from Romania (with 47.2%) compared to the pace of decline in exports to the
neighbouring country (28.6%), implying significant decrease in trade deficit
by USD 182.9 million or about 40%.
The intensity of Moldova’s participation in international trade
of goods in 2014 amounted USD 657.8 per capita, which compared to the
previous year decreased by USD 24.6 per capita (or 3.6%) and compared to
2003 with USD 439.2 per capita (or three times). This indicator, being an
indicator of the merchandise foreign trade level of development, represents
an expression of performance outside of the national economy, calculated in
relation to Romania has experienced an upward trend with little deviation, i.e.
recorded decreases for 2009 and 2012 compared to previous year with USD
26.9 per capita, respectively USD 5.5 per capita.
Given the fact that exports and imports have different roles in the
process of reproduction, these trade flows are reported, usually separately to
gross domestic product, forming the following indicators:
18
National economy evolution compared to foreign markets, including
Romania during the years (2003 - 2014)
Table 3
Source: Elaborated by the author based on information provided by the National Bureau of
The share of Moldova’s domestic production for export to foreign
countries in 2014 was 29.5%, which, compared to the previous year decreased
by 1 per cent, and from 2003 it had a more significant decline of 10.4 p.p. It
may be noted that, on average, during the analysed period (2003 - 2014) our
country’s total export share was 31.1%. Extension of Moldovan goods to the
Romanian market in 2014 was 5.5% of GDP; compared to 2013 it increased
slightly by 0.3 p.p. and by 0.9 p.p. compared to 2003. On average, over
the studied period, the share of domestic production for export to Romania
recorded 4.7%. For each unit decrease in GDP in 2014 compared to 2013
exports of goods made on Romanian market increased by 0.92 units. While
in 2013 for every unit increase in GDP compared to 2012, exports of goods
in Romania increased by 0.08 units, and in 2012, compared to the previous
year the unit growth in GDP corresponds to a decrease of exports of goods
made with the neighbouring country. Specific for the year 2009 compared to
2008 is the coefficient of variation of exports to Romania Moldova, which
is characterized by the fact that for every unit of GDP decline lays a 0.5 p.p.
decrease in exports. The share of total merchandise imports in Moldovan
19
GDP is much higher than that of exports in GDP, already a known fact. So
Moldovan national economy was dependent on international markets in 2014
in the proportion of 66.9% of GDP, where the dependence, compared to 2013,
decreased by 2 p.p. When referring to the share of the country’s merchandise
imports from Romania, in 2014 this share was 10.1%, which, compared to
previous year, increased by 1 p.p. Over the years, since 2003 our country’s
dependence on imports of Romanian goods had an increasing trend, registering
a share of 4.9% of GDP in 2003, compared to a share of 10.1% from the
previous year, with some deviations noted in 2008 and 2009. During these
years, compared to the previous year, the share of supplementing domestic
resources rate of imports of goods from Romania has decreased by 0.4 p.p. and
4.1 p.p. respectively. Referring to the coefficient of variation in imports from
Romania to Moldova in 2014 we can state that every unit of GDP reduction
compared to 2013 of imports of goods increased by 3.25 units. From 2003 to
2008 the average growth in each unit of GDP imports from Romanian market
increased by 0.13 units in the period (2010-2013) for every unit increase in
GDP, which corresponds to an increase of imports on average by 0.8 p.p. Only
in 2009, compared to 2008, each unit of GDP reduction registered a decrease
in imports of goods from Romania with 0.45 units.
In conclusion we can mention that considering Romania as Moldova’s
main foreign trade partner in 2014 is a significant event. On top of the partner
countries Romania ranks first both in Moldovan exports and imports. Trade relations
between Moldova and Romania experienced a good evolution, i.e. an upward
trend both in exports and imports of Romanian goods, which was interrupted by
significant falls in 2009. This year, under the impact of the global economic crisis
decreased the intensity of economic flows between the two countries.
References
1. Anghelache C., Anghel M.G. (2015). Statistică. Teorie, concepte, indicatori şi
studii ce caz, Editura Artifex, Bucureşti
2. Anghelache, C. (2008). Tratat de statistică teoretică şi economică, Editura
3. Begu, L.S.. (2009). Statistica internaţională. Bucureşti: Ed. Universitara. 220 p.
ISBN 973-743-617-1.
4. Begu, L.S.. (1999). Statistica internaţională. Bucureşti: Ed. ALL BECK. 167 p.
ISBN 973-9435-86-6.
5. Bădiță, M., Baron,T., Korka, M. (1998). Statistica pentru afaceri. Bucureşti:
Editura Eficient. 591 p. ISBN 973-9366-00-7.
6. Pârțachi, I., Caraivanov, S. (2007). Statistica social-economică. Chișinău: Editura
ASEM. 221 p. ISBN 978-9975-75-174-2
7. www.statistica.md
8. www.bnm.md
20
Using the Autoregressive Model for the
Economic Forecast during the Period
2014- 2018
Bucharest University of Economic Studies, Artifex” University of Bucharest
Prof. Ioan Constantin DIMA PhD.
Valachia University, Targoviste
Lect. Mădălina-Gabriela ANGHEL PhD.
Abstract
The article is based on the analysis of the autoregressive model. The
model will include in its structure a dependent variable represented by the
macroeconomic indicator GDP, to be forecasted and as independent variable,
granting an autoregressive character to our model, by including in the frame of
the built up model of the autoregressive variable GDP (-1), namely the lag 1 of the
variable GDP. Also considered as independent variables are the final consumption
(FC) and the flow of direct foreign investments (DFI) both influencing the tendency
of the evolution of the economic growth in our country.
Key words: autoregressive model, indicators, GDP, correlation,
forecast, representation
Introduction
Using the data published by UNCTAD, for the variables included in the frame
of the model, the evolutions for the period 2014 -2018 are going to be forecasted.
The achieved autoregressive model will include in its structure the
macroeconomic indicator GDP, both as dependent variable, to be forecasted
and as independent variable, granting an autoregressive character to our model,
by including in the frame of the built up model of the autoregressive variable
GDP (-1), namely the lag 1 of the variable GDP. Apart the independent variable,
GDP(-1), included in the frae of the prediction model, the final consumption
(FC) and the flow of direct foreign investments (DFI) are also considered as
independent variables which are influencing the tendency of the evolution of
the economic growth in our country.
We shall develop further on a un model of prediction for the evolution
of the GDP, for the period 2014 - 2018, applied on the case of Romania .
In order to achieve the prediction, we shall utilize the final data published
by UNCTAD during the period 1991 - 2013, for the main macroeconomic
21
indicators which influence the evolution of the GDP in Romania, respectively:
final consumption (FC) and flow of direct foreign investments (DFI), as
previously proceeded, in the predictions achieved for identifying the most
performing model of prediction.
Thus, assuming that the indicators of reference FC and DFI, are going
to increase with 10% from one statistical period to another and utilizing in
the frame of the first model, which grasps the prediction for the GDP, from
the period 2014-2018, the autoregressive variable GDP (-5), we developed,
according to the previously followed up stages, an econometric model of
prediction, defined as Model 1, (Table 1).
The autoregressive model which defines the prima equation noted EQ01
Table 1
Dependent Variable: GDP
Date: 07/26/15 Time: 18
OS
Included observations: V. ] after adjustments
Variable
Coefficient Std. Error
t-Statistic
DFI
-0.563367
0.203530
-2.767977
FC
1.274288
0.021404
59.53490
C
-4495.684
7S5.7157
-5.721769
GDP(-5)
0.027089
0.016453
1.646514
R-squared
0.999459
Mean dependentvar
Adjusted R-squared
0.999343
S.D. dependent var
S.E. of regression
1664.960
Sum squared resid
3S309273
Schwarz criterion
Log likelihood
-156.7951
Hannan-Quinn criter.
F-statistic
8624.014
Durbin-Watson stat
Prob(F-statistic)
0.000000
Prob.
0.0151
0.0000
0.0001
0.1219
104101.1
64969. S9
17.36612
18.06393
17.89340
2.326815
As explained in the previous sub-chapter, where we aimed to test and
validate the model of prediction, this time as well we get a valid and correct
model, both from the statistical and practical point of view, a fact which is
grasped by the outcomes of the tests R-squared and Adjusted R-squared, which
underline a percentage of validity of 99.94%, respectively 99.93%. Meantime,
the result of these tests is pointing out that the variables utilized in the frame
of the model, are exercising a very high influence on the forecasted variable,
respectively, the gross domestic product at the level of Romania .
We notice that the result evidenced by de F-statistic is also confirming
the correctness of the model, the value of 8 624.01, being by far superior
to the tableted level, considered as guide mark in the analyses methodology
aiming the econometric models. Meanwhile, the result evidenced by Prob
(F-statistic), is grasping the degree of null risk of the practical utilization of
22
the developed model, Model 1.
Thus, the developed, Model 1 can be represented through the
equation :
GDP = 1.27428*FC - 0.56336*DFI + 0.02708*GDP (-5) - 4495.68
(EQ01)
As previously done, we shall have to test whether the proposed model
(Model 1), for achieving the prognosis for the evolution of the GDP in Romania
is submitting the serial correlation.
Further to the application of the test “Breusch-Godfrey Serial
Correlation LM Test”, (Table 2), we identified the existence of a serial
correlation on the lag 6. We notice this fact through the result obtained after
testing the serial correlation, for the series of data utilized in the frame of the
built up model, Model 1.
Hence, the value of « Prob. Chi-Square (6) », is indicating the
acceptance of the null hypothesis according to which : « There is serial
correlation on the lag 6 ».
The testul Breusch-Godfrey for testing the serial correlation for the
Model 1 (EQ01)
Table 2
Breuscfi-Godfrey Serial Correlation LM Test
F-statistic
5.638039
Prob. F[6,8)
Obs’R-squared
14.55735
Prob. Chi-Square(6)
Test Equation:
Date: 07/26r15 Time: 18 :05
Sample: 1996 2013
Included observations: 1i
Presample missing value lagged residuals set to zero
Variable
Coefficient Std. Error
t-Statistic
DFI
0.547795
0.272140
2.380368
FC
-0.117421
0.032887
-3.570438
C
2442747
628.5406
3.886379
0.073954
0.024685
2.995896
PIBC-S;
RESIDC-1)
-1.005891
0.244586
-4.112626
RESIDC-2)
-1.426921
0.298148
-4.785940
RESIDf-3)
-1.457765
0.293695
-4.963528
RES ID (-4}
-1.614681
0.417577
-3.866789
RESID(-5)
-1.447887
0.334907
-4.323255
RESID [-6)
-1.239866
0.394708
-3.141220
R-squared
0.808742
Mean dependent var
Adjusted R-squared
0.593576
S.D. dependent var
S.E. of regression
963.2366
Sum squared resid
7422598.
Schwarz criterion
Log likelihood
-141.9079
Hannan-Quinn enter.
F-statistic
3.758693
Durbin-Watson stat
Prob(F-statistic)
0.037847
0.0144
0.0240
Prob.
0.0445
0.0073
0.0046
0.0172
0.0034
0.0014
0.0011
0.0048
0.0025
0.0138
2.75E-11
1510.926
16.87366
17.37331
16.94686
2.OS7003
Thus, in order to improve the proposed model, Model 1, as shown in the
23
previous sub-chapter, the component AR will be included. The improvement
of the proposed model of prediction implies, meantime, the removal of the
identified serial correlation.
In the achieved graphical representation, Table 3, we showed the
future evolution of the GDP, for the proposed prognosis period, respectively
2014 -2018. Out of the statistical tests achieved for the forecasted values,
evidenced through the equation EQ01, of the developed model, Model 1, we
observe that the value “RootMean SquaredError” is indicating a very small
prediction error, counting for 601.28.
The representation of the prediction for the GDP (Model 1), during the
Table 3
Following the stages proposed in the previous sub-chapter, for the
process of validating the prediction model, taking into consideration the
increase of the accuracy level of the estimation of the evolution of the GDP,
achieved through the equation EQ01, resulting out of Model 1, we shall
remove the serial correlation, by including the component AR(1), which
would ameliorate the quality of the dynamic prediction model.
Further on, we shall develop an autoregressive model, containing the AR(5),
called Model 2, which, according to the Eviews outcomes may be represented
mathematically as follows:
GDP = 1.27137*FC - 0.50020*DFI + 0.03072*GDP (-5) - 0.09860*AR(-5)
- 4854.68
24
Similar to the situation of the models previously interpreted, a very
high level of accuracy of the built up prediction model - (Table 4) – is to be
observed. This aspect is underlined in a proportion of 99.91%, respectively
99.87%, by the outcomes of the tests R-squared, respectively Adjusted
R-squared. Meantime, the value Prob(F-statistic) as well, is underlining a
degree of null risk in the situation of the implementation of the built up model,
Model 2, in practice, in the predictions for the evolution of the gross domestic
product at the level of our country``.
Meantime, the achieved model gets a high applicability given by the
individual values of the probabilities obtained by each variable alone, (Table
4), which evidences the fact that it is significant for explaining the evolution
of the gross domestic product in Romania.
Model 2.The autoregressive model which defines the second equation
EQ02
Table 4
As previously specified, the component AR(5) is improving the
proposed model of prediction, eliminating meantime the serial correlation.
Thus, applying the test “Breusch-Godfrey Serial Correlation LM Test”, in order
25
to verify whether the serial correlation is still identified on the lag 6, through
processing in Eviews 7.2, we get the outcomes pointed out in the Table 5.
According to the probability result, “Prob Chi-Square (6)”, obtained for
the assumed null hypothesis, according to which “« There it would exist a serial
correlation on the lag 6 », for the series utilized for the construction of the model,
Model 2, we deduce that this one does not submit a serial correlation any more.
Thus, we observe that the Model 2, which comprise the component
AR on the lag 5, is submitting an accuracy very little improved as against
the Model 3, a fact underlined by the result obtained for the test “RootMean
SquaredError”, which indicates an estimated error between the possible real
values of the evolution of the GDP and those estimated by the prediction
model, counting for 599.10, Table 6.
The test Breusch-Godfrey for testing the serial correlation for the
Model 2 (EQ02)
Table 5
26
We remained that the achieved prediction was grounded on the
presumption that the considered macroeconomic indicators in the construction
of the prediction model (final consumption, respectively flow of direct foreign
investments), would increase by 10%, from one year to another.
Thus, in conformity with the previous statements, the graphical
representation of the forecast achieved with the help of the informatics softs
Eviews 7.2., from the point of view of the Model 4, previously built up, is of
the form (Table 6):
The representation of the GDP forecast (Model 2), during the period
2014 -2018, in Romania
Table 6
In this context, according to the values submitted in the tables below
(Table 7), the forecasted evolution of the gross domestic product in Romania,
for the period 2014 -2018, is grasped in parallel by the equations: EQ1PIBF
(representing the first equation of prediction for the GDP) and EQ2PIBF
(representing the second equation, anticipating the evolution of the GDP for
the considered prediction interval).
As shown by the tests « Root Mean Squared Error » as well, achieved
for both Model 1, and Model 2, (Table 3 – Table 6) there are no major differences
between the comparative evolutions of the values recorded by the GDP, for the
2014 -2018, in both cases considering the assumed supposition concerning the
increase of the consumption and the drawn foreign capitals with 10% in each
forecasted year, Table 7.
27
The parallel representation of the outcomes of the expost predictions
grasped by EQ01 (Model 3) vs EQ02 (Model 2) for the period 2014 -2018
Table 7
Obs
1991
1992
1993
1994
1995
1996
1997
199S
1999
2000
2001
2002
2003
2004
2005
2006
2007
2003
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
GDP
30593.07
2075975
27951.94
31887.80
37618.60
36911.15
35616.63
41749.52
35995.56
37305.10
405S5.S9
45988.51
59466.02
75794.73
99172.61
122695.85
170616.95
204338.60
164344.38
164792.25
182610.67
169396.06
186438.48
205082.33
225590.57
248149.62
272964.59
300261.04
EQ1_PIBF
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
204402.6
224859.3
247331.1
272211.1
299384.6
EQ2_PIBF
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
204367.5
224984.3
247686.7
272190.4
299941.1
Hence, if the macroeconomic indicators: final consumption,
respectively flow of direct foreign investments increase with 10% from
one year to another, over the interval 2014 -2018, according to the built up
dynamic prediction models, the GDP would record the value of 204 367.5
million USD, in the year 2014, reaching the level of 299 941 million USD
in 2018, values estimated through the Model 2 (EQ2) of prediction, which
contains the component AR(5). If we consider the equation EQ01, evidenced
by the Model 1, the evolution would not differ too much, the GDP of our
country going to reach level of 204 402.6 million USD, in the year 2014, so
that in 2018 it records the value of 299 884 million USD. Out of the graphical
representation submitting the dynamic prediction of the GDP evolution (Table
8), during the period 2014 -2018, we notice the very close values estimated
by the two built up models (Model 1 and Model 2). Thus, the red dotted line
of the graphical representation, respectively in the Table 8, achieved with the
help of the informatics soft Eviews 7.2., is showing the prediction estimated
by utilizing the equation EQ 1, namely EQ1PIBF, established as a result of the
28
development of the dynamic model, Model 1, while the green line emphasizes
the estimated prediction through the equation EQ02, respectively EQ2PIBF,
established through the econometric model defined as Model 2.
The representation of the GDP forecast, through the two models (Model
1 and Model 2) represented by EQ01 vs EQ02 during the period
2014 -2018, in Romania
Table 8
Conclusions
The test “Breusch-Godfrey Serial Correlation LM Test”, nidentifies
the existence of a serial correlation on the lag 6. We notice this fact through
the result obtained after testing the serial correlation, for the series of data
utilized in the frame of the built up model, Model 1. The value of « Prob. ChiSquare (6) », is indicating the acceptance of the null hypothesis according to
which : « There is serial correlation on the lag 6 ».
The future evolution of the GDP, for the proposed prognosis period,
respectively 2014 -2018. Out of the statistical tests achieved for the forecasted
values, evidenced through the equation EQ01, of the developed model, Model
1, we observe that the value “RootMean SquaredError” is indicating a very
small prediction error, counting for 601.28.
The component AR(5) is improving the proposed model of prediction,
eliminating meantime the serial correlation. Thus, applying the test “BreuschGodfrey Serial Correlation LM Test”, in order to verify whether the serial
29
correlation is still identified on the lag 6, through processing in Eviews 7.2, we
get the outcomes pointed out in the Table 5.
The Model 2, which comprise the component AR on the lag 5, is submitting
an accuracy very little improved as against the Model 3, a fact underlined by
the result obtained for the test “RootMean SquaredError”, which indicates an
estimated error between the possible real values of the evolution of the GDP and
those estimated by the prediction model, counting for 599.10, Table 6.
According to the values submitted in the tables below (Table 7), the
forecasted evolution of the gross domestic product in Romania, for the period
2014 -2018, is grasped in parallel by the equations: EQ1PIBF (representing
the first equation of prediction for the GDP) and EQ2PIBF (representing
the second equation, anticipating the evolution of the GDP for the considered
prediction interval).
As shown by the tests « Root Mean Squared Error » as well, achieved
for both Model 1, and Model 2, (Table 3 – Table 6) there are no major differences
between the comparative evolutions of the values recorded by the GDP, for the
2014 -2018, in both cases considering the assumed supposition concerning the
increase of the consumption and the drawn foreign capitals with 10% in each
forecasted year, Table 7.
References
1. Andrei, T.; Stancu, S.; Iacob, A.I. (2008). Introducere în econometrie utilizând
EViews, Editura Economică, Bucureşti
3. Anghel M.G. (2011). Analiza evoluţiei unui activ financiar pe baza modelului
Auto Regressive Conditional Heteroskedasticity, Revista Română de Statistică,
Supliment Trim III
4. Anghelache C., Manole A., Anghel M.G. (2015). Analysis of final consumption and
gross investment influence on GDP – multiple linear regression model, Theoretical
and Applied Economics, No. 3/2015 (604), Autumn
5. Anghelache C., Anghel M.G. (2014). Modelare economică. Concepte, teorie şi
studii de caz, Editura Economică, Bucureşti
6. Anghelache C., Anghel M.G. et al. (2013). Using Time-Series Analysis of Economic
and Financial Phenomenon, Revista Română de Statistică – Supliment/Trim III
7. Anghelache, C.; Anghelache, G. (2009). Utilization of the chronological series
within the Stochastic processes, Metalurgia Internaţional Vol. XIV, nr. 4, Editura
ştiinţifică F.M.R
8. Boshnakov, G.N.; Iqelan, B.M. (2009). Generation of Time Series with Given
Spectral Properties, Journal of Time Series Analysis, Vol. 30, Issue 3, pp. 349368
9. Cavaliere, G.; Rahbek, A.; Taylor, R. (2008). Testing for Co-Integration in Vector
Autoregressions with Non-Stationary Volatility, CREATES Research Paper No
2008-50, Univ. of Copenhagen of Economics Paper No. 08-34
30
10. Franco, Ch.; Zakoian, J.M. (2004). Maximum Likelihood Estimation Of Pure
GARCH And ARMA-GARCH Processes, Bernoulli 10(4), 605–637
11. Gujarati, D. (2004). Basic Econometrics– Fourth Edition, The McGraw – Hill
Companies
12. Snowberg, E.; Wolfers, J., Zitzewitz, E. (2012). Prediction markets for economic
forecasting, Working Paper 18222
31
Managing Financial Instruments by
Development Bank of Romania
Prof. Vergil VOINEAGU PhD.
Prof. Michal BALOG, PhD.
Technical University of Košice
Daniel DUMITRESCU PhD. Student
Diana SOARE (DUMITRESCU) PhD Student
Abstract
Eximbank direct empowerment group analysis as administrator
of financial instruments provided by the Managing Authorities under the
Operational Programmes of Romania 2014 - 2020 is one of the options
provided by the EU Regulation 1303 of the current 2013 financial data the
Export - Import Bank of Romania as well and solvency indicators (situated
at a high level) accounted together for portfolio diversification of financial
products designed to assist national economy, important factors in designating
this institution as a financial intermediary of European funds that will be
allocated as financial instruments.
Keywords: financial intruments, funds, guarantees, loans, funding,
profitability, development bank, financial institution, authority
Introduction
Based on the Government Programme into force in 2015, the executive
Romania approved in March 2015, documents relating to the National Bank
for Development, which established the enlargement of the mandate of the
current banks of export - import, Eximbank to achieve benches Development
and to update the legislative framework to fit these new activities (Law
96/2000, republished, Government Decision No. 534/2007 respectively).
The mandate given by the Romanian Government Romanian Development
Bank - Eximbank SA, aims to achieve a number of goals, such as funding
priority areas, supporting export Romanian and Romanian investments in
foreign, all contributing to the implementation of EU policy objectives in
local and alignment with European and international practices in the field of
financing for development and the absorption of EU funds. This institution
is to remedy market failures private financing of SMEs and other actors in
32
Romania. Development Bank will provide various sources of funding to
develop and implement financial products specific to different categories of
eligible beneficiaries. To operate within the law, the Government proposed to
repeal the current law Eximbank (Law 96-2000) in order to regulate a modern
and efficient manner, based on European best practices, work in behalf of the
state of this new institution financial - banking.
Methodology and data
European Commission by Eurostat shows relevant statistical data
on key aspects of business and European funds in Romania and other EU
countries.
Multiannual Financial Framework 2014 – 2020
It offers three options for implementation by Member States of
financial engineering instruments and the choice of the implementing bodies
are financial intermediaries:
a. financial instruments set up at Union level and managed by the
European Commission directly or indirectly, such as for example:
COSME and Horizon 2020, for which the European Investment Fund
and the European Investment Bank received direct mandate from the
European Commission ;
b. Financial instruments established at national or regional administration
in accordance with common rules, which can be existing financial
instruments or financial instruments standardized;
c. loans or guarantees to financial instruments implemented directly by
managing authorities.
Compared to the above, and given the financial market analysis in Romania
during 2007 - 2013 for the Multiannual Financial Framework 2014 - 2020 looming
many variations of European financial instruments, and more choice of institutions
to manage. Thus, in December 2015, when we mark the end of the use of structural
funds and cohesion related BFM 2007-2013 (under rule n + 2), the principle of
complementarity seems to work best for Romania, opts for the formula optimal
management of EU financial instruments to be applied in all 3 versions provided
by the European regulations. In the period 2012-2013, Eximbank has seen an
acceleration of activity, performance reaching than those of other commercial
banks in Romania. In recent years the Bank managed to obtain operating profit,
which in the current financial performance is even more noteworthy since the
case of an institution with public capital. Only in 2014, Eximbank has reached an
exposure of five billion lei. Data released by the institution that since 2008, the
bank’s exposure to an increase continues to the present:
33
Source: EximBank, Financial Report 2014, interpretation Author
On the other hand, the funding is defaulted to a level of 3.1%, well
below the average of other credit institutions in the market. Compared with
existing results in the financial markets - banking in Romania, Eximbank enjoy
better profitability, which are based on a number of generally low-risk assets.
Also, as can be seen below, the indicators of liquidity and solvency are upper
limits. The indicator on the credit institution’s capital adequacy, calculated
according to the statute in force in December 2014, has a robust solvency ratio
amounts to 60%.
Source: EximBank, interpretation Author
Involvement of EXIMBANK in matters regarding European funds
In terms of EU funds absorption Eximbank them engaged in
developing and promoting a package of financial products necessary for
the proper implementation of a European project. As specific product cycle
management of European projects, the institution proposes comfort letters,
pre-financing, co-financing, insurance and other types of collateral.
34
Contribution to European funds absorption was successful by
Eximbank financing and guarantee through specific products. Thus, for 2014,
the institution has mobilized amount of 103 million lei worth in various fields
as you can see above.
a. Letters of Comfort issued by Eximbank are targeting both SMEs
and large enterprises or other legal entities of public law which
submitted projects that have been declared the winner by the
management authority, will sign the financing contract.
b. Pre-financing loan provides the necessary capital for the period
between payment of invoices by the customer and payment of
claims for reimbursement made by management authorities. The
maximum loan period is 2 years and the funding can be given in
euro or lei. This type of loan can cover up to 100% of funding from
AM Repayment of loans is based on the timing of receipts from the
management authority.
c. Loans to finance European projects can cover all project costs
(eligible and ineligible) and working capital. Duration of funding
depends on the period of project implementation, can be short,
medium and long term, and currency lei or euro can be granted.
During project implementation, Eximbank may grant a grace
period. The amount of funding is capped at 85% of the beneficiary’s
own contribution.
After analyzing several European business models, among which we
note HBOR in Croatia, KfW of Germany, BDB in Bulgaria, it was agreed that
the best suited to our objectives of economic development as BGK model,
Gospodarstwa Krajowego Bank of Poland.
Considering the performance that Poland obtained as EU member
state absorption of structural funds and Couze European 2007 - 2013, it is
no wonder that temeatica European funds will be extremely important for the
future Romanian Development Bank Eximbank .
In this respect it is estimated that the Multiannual Financial Framework
2014 - 2020, the institution will provide co-financing of European projects
representing commitments of our country to the European Union with an
estimated impact of Euro 6 billion.
Provisions of the law project
To clearly define the mandate of the Romanian Development Bank
granted by the Romanian state and to provide an appropriate framework for its
specific activities, the bill establishes a clear distinction between commercial
banking entirely subject to regulatory requirements relevant level local and
35
European levels of development and activity of banks operating as state
representative, affirming the principle of complementarity between the two
activities, and support the development Bank of strategic economic sectors in
accordance with national government policies.
Meanwhile, the bill defines the categories of beneficiaries eligible for
various specific banking products development banks, focusing on those types
of clients deemed to have a risk profile higher, but with the potential for creating
added value and the creation of jobs. These categories of beneficiaries may
include, but are not limited to start-ups (start-up), small and medium enterprises,
and associations and foundations (NGOs), intra-community development
associations, LAGs or FLAG- Links to retain private financial market in 2015 a
certain reserve and distance. Development Bank will give the latter a wide range
of financial products - bank most diverse but complementary and necessary to
run any investment project, whether financed from European funds or credits, cofinancing, refinancing, guarantees of loans , insurance policies or reinsurance.
These products require significant financial resources, the legislature
proposing various sources of funding for the institution. The Government will
36
provide resources from Ministry of Finance, revenues that will come from
privatizations of state enterprises, or financial resources to be granted by
mandate from the various public authorities, or the Development Bank may
use external financing lines from international financial institutions and other
development banks.
Conclusions
In this context, the Romanian Bank for Development will receive
management project to promote small and medium enterprises, stages I, II,
III project involves the administration of the financial revolving granted by
the German Government, by Kreditanstald fur Wiederaufbau (KfW) to the
Ministry of Finance during 1998-2008. Although the credit institution will
state capital, however, will opt to supplement funding from the state budget
and by securing sources of additional financing, so as to ensure the soundness
JEL institution and to reach in an efficient way lending targets assumed by
law. These additional sources of funding will enable expansion specific made
in behalf of the state and to other related activities development banks.
References
1. Anghelache, C., Anghel, M.G. (2015). Theoretical aspects concerning the use of the
statistical-econometric instruments the analysis of the financial assets, Romanian
Statistical Review - Supplement, No. 9
2. Anghelache, C., Anghel, M.G. (2014). Using the Regression Model in the Analysis
of Financial Instruments Portfolios, Procedia Economics and Finance, Volume 10
3. Anghelache, C., Anghel, M.G. (2014). Economic modeling: Concepts, theories
and case studies, Economica Publishing House, Bucharest
4. Droj, L. (2014). „Bancabilitatea proiectelor de investitii finantate din Fonduri
Structurale Europene”, Editura Economica, Bucharest
5. Dumitrescu, D.I., Soare, D.V. (2014) – „ Financial Engineering Instruments Financed
from European Structural and Investment Funds and Financial Products issued by
Financial Institutions supporting European Project Implementation”, Romanian
Statistical Review Supplement, Volume (Year): 62 (2014), Issue (Month): 10
(Octomber), pp. 16-39
6. Păunică, M. (2014) - Economic benefits of the infrastructure projects implemented
in the Reservation of the Danube Delta Biosphere, Theoretical and Applied
Economics, Volume (Year): XXI (2014), Issue (Month): 11(600) (November),
Pages: 95-104
7. Vaida-Muntean, C., Voineagu, V., Munteanu, G. (2014) – „Uncertainty And
Sensitivity in Statistical Data,” Romanian Statistical Review Supplement, Volume
(Year): 62 (2014), Issue (Month): 12 (December), pp. 29-36
8. http://ec.europa.eu/eurostat/web/structural-business-statistics
9. www.economica.net
10. mfinante.ro
37
Essential aspects regarding the optimal
prevention
Prof. Mario G.R. PAGLIACCI PhD.
Universita degli Studi di Perugia
Emilia STANCIU PhD. Student
Abstract
It is recurrently very likely for decision-makers to wrongly assess
risk and consequently to ineffectively put effort in diminishing or avoiding
it. Preventing losses or self-protection represent an effort we make in order
to reduce the impact of a probable accident. The question is: what would
be the level that maximizes the estimated utility of an economic agent? In
many cases, the cost-benefit an analysis of prevention is analyzed based on the
risk-neutrality assumption. This means that only losses of average size matter.
There is no intention of reducing the variability of losses if it doesn’t involve a
loss above average. The extreme measure of risk prevention is giving up any
type of risk.
Key words: prevention, assets, risk, aversion, optimization
Introduction
Optimality of activities of prevention against loss was initially
examined by Ehrlich and Becker (1972). They called this type of activity
“self-protection” and proved that insurance and prevention can be either
complementary or substitute each other. Effects of potential risks in activities
of risk prevention were examined by Briys, Schlesinger si Shulenburg (1991).
Dionne si Eeckhoudt (1995) demonstrated that an increase in risk aversion has
an ambiguous effect because prevention does not generate a risk reduction as
Rothschild si Stiglitz stated. Julien, Salanie si Salanie (1999) showed that an
increase in risk aversion led to an increase in optimal level of effort if and only
if the initial optimal probability of loss is smaller than the dependent threshold
of utility. Chiu (2000) showed for the first time that the third derivative of
utility function had an important part in determining the threshold. Jewitt
(1989) and Athey (2002) examined the effect of risk aversion on optimal
decision for general issues, including prevention as a special case.
38
2. Aspects regarding prevention in case of risk neutrality
Let us consider the case in which a risk-neutral agent may have the risk
of losing a quantity L with probability p. The agent may invest in preventive
measures to reduce the loss probability. If e is the amount of money invested
in prevention, the probability of loss L is p(e). Let us assume that P p is a twice
differentiable, decreasing and convex function p’ ˂ 0 and p’’ ≥ 0.The convexity
condition is that the prevention activity should have a decreasing marginal
productivity. The risk-taker should select e so as to minimize the expected net
cost of risk, taking into account the cost of prevention. This can be written:
(1)
Since C is convex in e, the first order condition is necessary and
sufficient for a minimum. In the end, we assume that C’ (0) ˂0, so as the
constraint e≥0 is not binding.
The preventive optimal investment en for a risk neutral agent is defined
by:
-pˈ(en)L=1
(2)
This equation is the classical optimization condition according to which
the marginal cost is equal to marginal benefit. This gives a positive probability
of loss, since a total elimination of risk is usually extremely expensive. It is
possible to obtain a situation with risk 0 only from the technical point of view,
but not from the economic point of view.
Risk aversion and optimal prevention
The hypothesis of risk neutrality is of good approximation when risk
is low or can be diversified on the market. Let us consider an estimated utility
maximize with an welfare degreew0who risks to lose quantity L cu with a
probability p(e). The decision can be written:
(3)
With probability p(e),final wealth isw0 - e – L otherwise isw0 – e.
When u is linear, programs (1) and (3) are equivalent, since V(e) = a-bC(e)
for (a,b ˃ 0). Comparing optimal prevention e*of estimated utility maximizer
withe*, the optimal prevention of risk neutral decision maker, we observe
that risk-aversion agents invest more in risk-prevention and risk takers invest
less.
39
Condition type 2 is not necessarily fulfilled even in the case of riskaversion.
We have:
Where the first term is negative, the second and the third term is positive,
respectively negative, in case of risk aversion. Consequently, we can not take for
a certain fact that V is concave without establishing supplementary restrictions
to u and p.Assessing this derivative, we come to the conclusion that:
where
Using the condition (2) we observe thatV’(en) only if
where z= w0 - en
The equation can be written:
The right part of this inequality is positive under risk aversion. Risk
aversion increases optimal investment in prevention if and only if the loss
probability which is optimal for risk neutral agent is smaller than a critical
threshold where:
Risk aversion does not necessarily increase the optimal investment
in prevention. More prevention would lead to a reduction in wealth in both
cases and risk aversion agents would not like the decreasing welfare.
The critical point is½ if the utility function is quadratic, for instance
if prudence degree is 0.The quadratic agent measures risk by its variant with a
maximum at p=1/2
40
If pn is smaller than ½, an increase in loss prevention reduces p and
which is desirable by risk- aversion quadratic agents. If pn is bigger
than ½, an increase in loss prevention reduces p but increases σ2.
σ2and,
Aspects regarding prudence and optimal prevention
A generally accepted theory is that prudent people invest more in
prevention. Yet, this is contradicted by practice. In order to isolate the effect
of prudence (u’’ ˃0) or imprudence (u’’’˂0), we limit the analysis to the case
in which risk-neutral agents select pn=1/2. Risk aversion does not influence
optimal investment in prevention for quadratic utility, since quadratic
preferences show prudence 0, uˈˈˈ=0. In such a situation, a prudent agent
would invest more in prevention than a risk neutral agent? More prevention is
optimal if condition (5) is fulfilled, for instance
(6)
We assume that agent is imprudent. (u’’’˂0).Using Jensen’s inequality
for each possible value of the integrand below, we get
If prudence (u’’’›0), we obtain the opposite result.
So, in case we assume that a risk-neutral agent takes he effort en so
as the loss probability should be ½, all prudent agents will choose an effort
smaller pn while all imprudent agents choose an effort bigger than en.
Prudence increases the marginal value of welfare so it reduces the
willingness to invest in financing prudence. So, a prudent agent will save more
cautiously as a protection against loss than an imprudent agent.
Conclusions
Risk approach is important when analyzing financial decision set. Any
decision is based on certain evaluations of the probability of risk emergence.
Especially financial decisions need a particular framing given the efforts
of altering the decisional circumstances and the nature of risk itself.
Investments for risk reducing alter risk distribution as opposed to insurance,
which finally alter risks’ consequences financing, normally accepted as loss
control. The way the distribution is altered represents a rather complex
process. Loss prevention is a certain type of loss control, known also as “selfRevista Română de Statistică - Supliment nr. 1 / 2016
41
prevention”, which is the amount of effort made with the view to reduce the
probability of an unwanted event.
References
1. Anghelache C., Anghel M.G., Sacală C. (2015). Optimum analysis model in the
case of deductible insurance, Romanian Statistical Review - Supplement, No. 11,
pg. 9 – 13
2. Anghelache C., Anghel M.G., Niţă O.G., Ursache A. (2015). Unele aspecte privind
modelul de coasigurare optimă, ART ECO - Review of Economic Studies and
Research, Vol. 6/No. 3, pg. 40-47
3. Anghelache C., Anghel M.G., Popovici M., Dumitrescu D. (2015). Utilizarea
modelelor econometrice în analiza comparativă în domeniul coasigurării, ART
ECO - Review of Economic Studies and Research, Vol. 6/No. 3, pg. 75-80
4. Anghelache C. (2011). Analiză actuarială în asigurări. Note de curs, Editura
Artifex, Bucureşti
5. Anghelache C., Mitruţ C., Pârţachi I. şi alţii (2009). Cadrul legal al calculului
primei de asigurare”, Revista Română de Statistică - Supliment, nr. 7, pp. 143145
6. Athey S. (2002). Monotone comparative statics under uncertainty, Quarterly
Journal of Economics
7. Briys, E., H. Schlesinger, and J.-M. Schulenburg (1990). Reliability of risk
management: market insurance, self-insurance and self-protection reconsidered,
Geneva Papers on Risk and Insurance Theory
8. Courbage, C., and B. Rey (2007). Precautionary saving in the presence of other
risks, Economic Theory, 32(2), 417–424. (2012): Optimal prevention and other
risks in a two-period model, Mathematical Social Sciences, 63(3), 213–217
9. Courbage, C., B. Rey, and N. Treich (2013). Prevention and precaution, in
Handbook of Insurance, ed. by G. Dionne, Huebner International Series on Risk,
Insurance and Economic Security, chap. 8. Springer.
10. Dachraoui, K., G. Dionne, L. Eeckhoudt, and P. Godfroid (2004). Comparative
mixed risk aversion: Definition and application to self-protection and willingness
to pay, Journal of Risk and Uncertainty, 29(3), 261–276
11. Dionne, G.and L. Eeckhoudt (1985). Self-insurance, self-protection and increased
risk aversion, Economic Letters
12. Eeckhoudt, L., R. Huang, and L. Tzeng (2012). Precautionary effort: A new look,
Journal of Risk and Insurance, 79(2), 585–590
13. Jewitt, I. (1989). Choosing between risky prospects: the characterization of
comparative statistics results, and location independent risk, Management
Science
14. Lee, K. (2012). Background risk and self-protection, Economics Letters, 114(3),
262– 264
42
Specific elements of correlation between
infomation and risk
Emilia STANCIU PhD. Student
Abstract
The aspects regarding the correlation between information and risk
have been object of scientific research for a log time. Many scientists have
analysed it both from the qualitative and quantitative point of view. This
article aims to present a general model of using information in risk prevention
and emphasize the value of information for risk-aversion decision makers.
Introduction
Although progress in financial decisions has been noticeable in the
latest decades, there are still unsolved or partially-solved issues mainly due to
the complexity of this field
Any type of financial decision has two opposite, yet inseparable
aspects: objective information and subjective opinion on it.
As a consequence, the expected utility of information greatly depends
on the decision- maker’s particularities.
The more society evolves, the bigger is the quantity of information,
the more aware are people of their subjective limitations that influence their
decisions, the less is time for decision-taking. Therefore things are getting
more and more complicated, not simpler.
Risk is always sensitive to new information. For instance, financial
news we find out every day influence our perception on the risk level of the
investments we have to make. It depends only on us whether we take into
account the news and to what extent they influence our decisions.
Notions regarding the role and value of information
The more accurate and available is the information, the more probable
is for us to take the right decision. For instance, if we consider the case of an
insurance.
A person named X, with an intial welfare of 4000 ron has to transport
it over the ocean. If evreything all right, the welfare will increase to 8000
43
ron. As X is a risk-aversion person, he takes into consideration the posibility
of making an insurance. An insurance company offers him only one type of
contract accordind to which, if the ship sinks, X gets 8000 ron. The prize for a
full insurance contract is 4400 ron. If the probablity of loss is ½, this prize has
a corresponding recovering factor of 0,1. X utility function is
If X takes the full insurance contract, the expected utility function is:
Due to the full insurance, the expected utility is independent from
X’s opinion about the probability of an accident. Let us consider P= p0 the
subjective probability of X in absence of any information. /in this case, we let
p0 unspecified. If X decides not to make any insurance, the expected utility is:
His decision can be written:
where V0 is maximum utility of X in absence of any information.
Therefore, if X has no information, the optimal startegy is to let the
ship with no insurance.
Let us now consider the case that X can obtain free information before
making any insurance and the insurance company does not know that the client
knows this information.
X can receive good or bad signals of his success, with q şi respectiv
1-q probabilities. He calculates the posterior probability of success by using
Bayes rule. Before observing the sgnal, the probability of success is as before.
So, I shall assume that po=0.5, q=0.5.
V0 is the maximum expected utility when the probability of success is p0
Fig. 1
44
Expected maximum utility Vi with information
Fig. 2
The curved line V(p) is the maximum expected utility as function of
probability p of success.
Information is relevant for the probability of success. As seen in Fig.
1, the maximum expected utility is a convex function of probability of success
p, information leading to an increase of welfare:
Fig. 2. Shows us how this happens.
The investor has the option of making or not the insurance in case
he receives a good signal. He may insure against risk regardless the signal.
Nevertheless, information is useful and valuable if the person who knows them
takes the consequent decisions in order to prevent or diminish the possible risk.
A general model regarding the use of information in deceasing
risk
A piece of information may be non-negative if the decision taking
process does not depend on it.
An informed decision-taker may behave at least like a not informed
decision-taker if he decides to disregard the information he has. It is worth
mentioning that the linearity of expected utility by respecting the probabilities
is an essential element.
The value of information and risk- aversion
It is commonly believed that decision-takers with a greater riskaversion give information more importance. Yet, this is not a general rule.
45
Let us consider that wb şi wg represent safe levels of the risk-taker’s
welfare when he receives signals pb and pg. We also consider that w0 şi wi are
equivalents of certitude in absence or presence of information. Without any kind
of information, the decision- maker does not take any kid of risk w0=7600 ron.
If he receives a bad signal, he will make an insurance, in which case wb=w0. The
only interesting case is when pg is bigger than pc , case in which a good signal
determines X to make an insurance. We can extend the previous formula to get:
We have a case where information determines the decision-maker to
take a risk he normally won’t take in case of lack of information.
In the picture below, the value of information k is represented as a
function of the degree of relative aversion to risk y. A full insurance is optimal
if the relative risk aversion is bigger than ƴ =2.390.
An ex-ante exposal to risk is bigger when we lack the information,
since an insurance is still optimal when a bad signal is noticed. Simetrically,
a bad signal determines the decision-taker to insure against a risk he wouldn’t
normally have insured if he hadn’t had the information.
Aspects regarding the effect of information on behaviour
I shall consider the following model with two periods:
At the moment 0, the decision taker selects α0, which means a good
state u(w0) for the first period. Then, at the beginning of the next period, he
notices a signal m which influences his opinions s̃. He chooses to maximize
the expected value. The decision in the first period influences the second period
in two ways. Firstly, the set of choises α in the second period may be greatly
46
influenced by initial choice of α0 . Secondly, the initial choice of α0 at time 0
may directly influence utility in the second period.
The decision taker is in a situation of probabilistic incertitude in the first
period. He is confronted with the parameter risk: the incertitude related to the
distribution parameters for the risk he has to take. The time needed for this type of
decision is crucial as incertitude increases in time. In this type of decision-taking
process we have to consider the ireversibility conditions. If the decision-taker
regrets the initial decision and is not flexible along all along the decision-taking
process, the risk management is in danger. The decision of optimal saving is
another kind of decision to be considered. Better information gives the risk-taker the
chance to diversify the future risk. Prudent risk-takers will make prudent savings.
Hirshleifer effect is one of the effects that appear in the risk-taking process. Let us
assume that all risk-aversion makers confront with a risk of damage x̃. Likewise,
we’ll assume that we have a competitive market with no cost of transaction (ƛ=0)
and no asinometric information. For decision-makers an equilibrium state would
be a total insurance against risk at a minimum premium price Ex̃.
The cst of information is equal to the premium of risk associated with
x̃ since the decision-makers would rather take risk x̃ than the middle of E x̃ .
This is Hirshleifer effect.
Conclusions
In an ideal economy, decisons are made based on rationality and
optimality. Decision-takers are considered to act only after they have taken into
account all available information. However, recent research has proved that the
ability of gathering direct infrmation, the time we vae at our disposal, irationality,
biases, personal features play an important role in the risk-taking process. It is
widely accepted that a decision is better if the available information is in a bigger
quantity and of a better quality. But, from this point on, a key element is the
decison-maker’s ability of evaluating the information in complex circumstances.
Using probabilistic and deterministic models is an artificial way to
correlate information in order to facilitate the process of takig better decisions.
A bad decision is not due to the very use of a certain model, but to the wrong
or abusive use of it.
References
1. Anghel, M.G., Ursache Alexandru (2015). Elemente definitorii privind riscul
economic, ART ECO - Review of Economic Studies and Research
2. Anghel, M.G. (2013). Modele de gestiune şi analiză a portofoliilor, Editura
3. Anghel, M.G. (2010). Utilizarea modelelor econometrice în analizele economice,
Simpozionul Ştiinţific Internaţional „Necesitatea reformei economico – sociale a
României în contextul crizei globale”, Editura Artifex, Bucureşti, pg. 145-151
4. Anghel M.G., Pocan O. (2009). Modele de estimare a rentabilităţii şi riscului unui
47
titlu financiar, Revista Română de Statistică – Supliment „Criza economică - efect,
cauze, perspective”, nr. 3, pg. 337 – 340
5. Anghelache C., Manole A., Anghel, M.G., Stanciu Emilia, Soare D.V.(2015).
Aspecte semnificative privind analiza riscului de faliment, ART ECO - Review of
Economic Studies and Research, Vol. 6/No. 3, pg. 15-21
6. Anghelache, C., Anghel, M.G. (2015). Aspectele principale referitoare la unele
modele non-liniare utilizate în analizele economice /Main Aspects Regarding Some
Non-Linear Models Used in Economic Analysis/ Revista Romană de Statistică
Supliment/Romanian Statistical Review Supplement, 9/2015: 3-11
7. Anghelache C., Manole Alexandru, Anghel, M.G., Popovici M., Soare D.V. (2015).
Modele de analiză a riscului finanicar ART ECO - Review of Economic Studies
and Research, Vol. 6/No. 3, pg. 55-63
8. Anghelache, C. (2008). Tratat de statistică teoretică și economică, Editura
Economică, București
9. Anghelache, C., Capanu, I. (2003). Indicatori macroeconomici – calcul și analiză
economică, Editura Economică, București
10. Anghelache, C. (coord.) (2014). Statistical-econometric models used to study
the macroeconomic correlations, Romanian Statistical Review-Supplement,
December 2014
11. Anghelache, C. et alias (2014). Using Linear and non-linear Models in
Macroeconomic Analysis, Revista Romană de Statistică Supliment 1/2014
12. Anghelache, C. et alias (2014). Multiple Linear Regression Models used in
Economic Analysis, Revista Romană de Statistică Supliment 10/2014
13. Anghelache C. (2010). Metode şi modele de măsurare a riscurilor şi performanţelor
financiar-bancare – Ediţia a II-a, Editura Artifex Bucureşti
14. Arrow, K.J and A.C.Fischer (1974). Environmental preservation, uncertainty and
irreversibility, Quarterly Journal of Economics
15. Cremer,J. (1982). A simple proof of Blackwell’s “comparison of experiments”
theorem, Journal of Economic Theory
16. Dixit, A.K. and R.S. Pindyck (1994). Investment under uncertainty, Princeton
University Press
17. Epstein, L.S. (1980). Decision-making and the temoral resolution of uncertainty,
International Economic Review
18. Schlee, E.E. (2001). The value of information in efficient risk sharing arrangement,
American Economic Review
19. Treich, N. (1997). Risk tolerance and value of information in the standard
portfolio model, Economics Letter
20. Malcolm Gladwell (2013). The Tipping Point. Cum lucruri mici pot provoca
schimbări de proporţii, Editura Publica
21. Hăvărneanu, C. Hăvărneanu, G. (2015). Psihologia riscului, Editura Polirom
22. Hirshleifer, J. and J.G.Riley (1992). The analytics of uncertainty and information,
Cambridge University Press
23. Silver N. (2013). Semnalul și zgomotul. De ce atât de multe predicţii dau greș-pe
cand altele reușesc, Editura Publica
24. Taleb N.N. (2010). Lebăda neagră. Impactul foarte puţin probabilului, Editura
Curtea Veche
48
Model of Static Portfolio Choices
Lect. Mădălina Gabriela ANGHEL PhD.
Gyorgy BODO Phd. Student
Okwiet BARTEK, PhD. Student
Abstract
In decentralised economies the financial markets has a key role, being
considered as institutions that transfer entrepreneurial risk to consumers.
The entrepreneurial risk is assumed by the investors as part of the industrial
or infrastructure investment that could be considered as the engine of the
economic growth. The risk related to the investments finally is transferred
from the investors to the tax paying population which statistically can be
considered as risk-averse. The problem of the investors is to determine the
optimum balance between the assumed risk and the expected performance,
but having a limited investment capital.
In this paper we examined a simple version of the problem convincing
risk-averse people to accept the purchase of risky assets by receiving an
additional premium on it. Also, we focus on behaviour of investors who spend
the entire investment at the end of the analysed period, but for simplicity we
detach the time component of the equation.
Key words: portfolio, asset, choice, model, risk
Description of the One-Risky One-Risk-Free Asset Model
For the simple understanding of the model we consider an investor (also
called agent) who has the capital to invest w0. He can invest part of it (noted with
α) in risky assets (e.g. mix of stocks) having the return over the period expressed
as random variable and another part (noted with w0 - α) in risk-free assets (e.g.
government bonds) with the return over the period r. The investor is interested in
maximising the return at the end of period determined by the optimal composition
(α, w0 – α) of his portfolio, which can be written as:
,
(1)
where
is the final value invested in the risk-free
portfolio, and
can be described as “excess return” on the risky
assets. The problem of investor is to choose α in that way that the Expected
Utility (EU) should be maximised:
49
(2)
We can interpret α = 0 as 100% investment in risk-free portfolio,
and as α increase, the share of the risky portfolio increases and consequently
the investor could accept higher exposure to the risk because of the higher
expected net pay-off (earning).
Proposition 1.
Consider problem (1) where
is the optimal investment in the risky
assets, and is the excess return of the risky assets over the risk-free rate. The
optimal investment in the risky asset is positive if and only if the expected
excess return is positive, meaning
. Moreover variation of
can
be interpreted as:
(i) if is decreasing, the risk-aversion of the investor is increasing in
the sense of Arrow and Pratt;
(ii) if
is increasing than the risk-aversion is decreasing, meaning
the investor can accept higher risk.
This is a simplified model which nod details the case why large
proportion of population is risk-averse and does not hold any shares of stocks.
This might be explained with the fact that obtaining information about the
market evolution needs some additional knowledge, and obtaining such
knowledge involve personal effort or cost, cost which the (private) investor
could consider to high compared with the expected net earnings.
Another conclusion we can formulate is related to the fact that riskaverse people hold less risky portfolios, whereas rich people has decreasing
risk-aversion and holds larger amount of stocks. Several empirical studies
confirm this positive correlation between stock holding and wealth, which
offers additional argument in favour of DARA.
In a particular case, let assume that the utility function show constant
relative risk aversion:
for all c, where y is the degree of relative risk
aversion.
In this condition the problem (2) can be written as:
(3)
We can observe that the solution of this equation is
,
where k is a positive constant, such that
, leading to the
conclusion that under constant relative risk aversion, the optimal amount of
investment in risky assets is proportional to wealth.
50
Proposition 2.
Under constant relative risk aversion, the investors’ willingness to
invest in stocks is proportional to their wealth:
.
We approach an approximate solution for determining the optimal
demand by using first-order Taylor approximation to
around w,
we can approximate
as
Finally, for the proportion of wealth invested in stocks, we obtain the
approximation:
(4)
where
is the relative risk aversion degree
evaluated in w, and
are respectively the mean and variance of
the “excess stock” return. We can conclude, that the optimal share of wealth
invested in stock is relatively proportional to the equity premium
and inversely
proportional to the variance of stock return and relative risk-aversion.
In order to sustain the above mentioned conclusion, historical data on
assets returns available in USA (Shiller, 1989; Kockerlakota, 1996) shows that
the average real return on large part of US stocks was ~7% per year, whilst the
average real risk-free rate been ~1%.
The Effect of Background Risk
Beside the riskiness of assets returns there are also other sources of
risk in determining the final wealth of an investor. For example the labour
income (wages) are not fully risk-free, and for determine the effect of such
risk we can introduce a zero-mean background risk to initial wealth w. This
leads to the adjustment of (2) resulting:
,
(5)
Intuition might suggest that
, but we want to identify any
kind of correlation between them which can influence the investors’ decision
making behaviour. The effect of could be considered as “bad-luck” of an
investor, therefor they would try to compensate the extra risk with a more
precautious behaviour compared to . We can rewrite the (5) considering this
approach as:
,
(6)
where value of the function v is defined by
for
all z. In order to identify the difference we have to compare the function u with
51
v, in condition assumed that
(2)). This is true if
(defined in (6)) is smaller than
,
for all
such that
, where
(defined in
(7)
, which is equivalent to requiring
.
Proposition 3.
(i) Any zero-mean background risk reduces the demand for other
independent risks.
(ii) Absolute risk aversion is decreasing and convex.
We have to prove that (ii) is sufficient for condition (i). Noticing A(.)
the absolute risk aversion, than we can write:
.
Since we assumed that the absolute risk aversion is decreasing, results
that the right hand side of this equation is larger than
.
In addition, due to the fact that A is considered convex than results
is larger than
. These three observations together implies condition (7)
which is necessary and sufficient for property (i).
Portfolio of Risky Assets
The above mentioned model can be further refined in sense that the
risky assets could be split in two or more sub-portfolio, in this way the investors
distribute the risk related they risky investments but also the earnings are
distributed between the different portfolios. Let assume for simplicity that these
two assets have the same distribution of returns and . In order to determine
the optimal structure of their portfolio, we should solve the following program:
,
(8)
where
is the amount invested in the first risky asset. Since we
consider the investor as risk-averse, the first-order condition is:
,
The unique rot of this equation is
, meaning that for riskaverse investors, the optimal investment is to perfectly balance their investment.
This result, which is called also as risk-diversification, is reflected also by the
fact that
is distributed as
,
52
where
This means that the return of any portfolio is distributed as return of
the balanced portfolio plus a pure noise which satisfy the condition:
.
This conducts us to the conclusion that accepting unbalanced portfolio
is equivalent to accepting zero-means lotteries. Further, in real words investors
tend to more diversify their portfolio, which is to dislike zero-mean risk.
As conclusion, based on theoretical approach, portfolio management
is a simple problem, in which investors should not spend too much time and
energy. Theoretically we assumed that financial markets are informationally
efficient, meaning that the same information about the risk is available for all
investors, and investors have mean-variance preferences. Investing more in risky
assets investors expect higher return, and by diversifying the risky portfolio they
looking for the optimum balance between assumed risk and expected returns.
References
1. Adam, Al.; Houkari, M.; Laurent, J.P. (2008). Spectral risk measures and portfolio
selection, Journal of Banking & Finance 32, pp. 1870–1882
3. Anghel M.G. (2013). Theoretical Concept relating to the Management Portfolio,
Revista Română de Statistică – Supliment/Trim III, pg. 122 – 126
4. Anghel, M.G. (2012). Statistical indicators used in the analysis of portfolios of
financial instruments, Proceedings of the International Symposium “Romanian
Economy in the Globalization Conjuncture On Crisis Background”, Revista
Română de Statistică – Supliment, pg. 117 – 120
6. Anghelache C., Anghel M.G. (2015). Econometric models utilized for the portfolio
selection, Romanian Statistical Review – Supplement, No. 4, pp. 19-21
7. Anghelache G.V., Anghel M.G. (2013). Specific patterns in portfolio analysis,
Theoretical and Applied Economics, Volume XX, No.11, pg. 7 – 24
8. Hagstromer, B.; Binner, J.M. (2009). Stock portfolio selection with full-scale
optimization and differential evolution, Applied Financial Economics, ISSN 0960–
3107 print/ISSN 1466–4305, pp. 1559–1571
9. Li, J., Smetters, K. (2011). Optimal portfolio choice with wage-indexed social
security, Working Paper 17025
10. Lucas, A.; Siegmann, A. (2008). The Effect of Shortfall as a Risk Measure for
Portfolios with Hedge Funds, Journal of Business Finance & Accounting, 35(1)
& (2), pp. 200–226, 0306-686X
11. Merton, R.C. (1969). Lifetime portofolio selection under uncertainty: the
continuous-time case, Review of Economics and Statistics 51: 247-257
12. Mossin, J. (1968). Optimal multiperiod portfolio policies, Journal of Business,
41:215-229.
53
Model of Static Portfolio Choices in an ArrowDebreu Economy
Prof. Gabriela Victoria ANGHELACHE PhD.
Gyorgy BODO Phd. Student
Abstract
The financial markets and the institutions which are operating in
these markets are essential for the good functioning of any decentralised
economy. Beside the simplest approach where there is no time dependence,
and investment opportunities are limited only to bonds (risk-free assets) or
stocks (risky assets) in a more complex economic model we can consider other
investment opportunities too.
Key words: options, return, portfolio, result, asset
One, frequent used case, is to bet on a future event which has a
probability to happen or not and at the end to collect a premium (return) if
the event occurs. All this, in return of a down payment of a lump sum fee paid
ex ante. As example we can mention any kind of sport bets, where you pay
an initial fee, bet on an event (e.g. who will win a game, or which will be a
final score a.s.o.), and collect a much higher premium if the result you bet it
finally occurs. Or, any lottery type game having a similar approach, you buy
a ticket, put some numbers and if your bet was correct, you will collect a high
premium on it. For example, from economic perspective suppose you can find
a counterparty that is willing to pay you a premium if some specified assets will
have a return higher than an expected value, or some macroeconomic indicator
will work better than initially estimated in a certain moment in time.
Also, since the 1980’s it was developed new type of asset called
“options” which offers highly nonlinear return for the holders. Other case
might be when investors are opting to purchase a “portfolio insurance”, where
the minimum return is guaranteed them.
A more complete modelling of the financial markets, where several
investment opportunities are considered simultaneously for each of them
investors assuming a certain risk factor, theory which was first analysed by
Arrow (1953) and Debreu (1959).
54
Arrow-Debreu Securities and Arbitrage Pricing
The full model consider that are finite number of possible S states of
nature at the end of analysed period (s = 0, 1, …, S-1), but for the simplicity
and easy understanding of the model we consider only two states (S = 2). We
assume that the market is complete, meaning that for each possible state (s
= 0, 1) there is an asses which provides a unit payoff to the investor if and
only if state s occurs and zero otherwise. These are called “Arrow-Debreu
securities”.
We considered the initial price of the risky asset normalised to 1,
therefore the final value of the asset in state s is
, meaning that the
net asset return is
in state 0, and
in state 1.
For the more complete modelling of a financial market, we take into
consideration also a risk-free asset with return r in both states analysed above
(s = 0, 1) and let assume that
, as an equilibrium condition.
For the Arrow-Debreu security associated with state s = 1 we can state
that,
where α is the number of purchased risky-assets, and B is the risk-free
rate used for borrowing.
There are two constraints: first, that in state 0 nor revenue from this
portfolio for the investor and second, that revenue in state 1 is one. Than the
solution of this system is
and
.
The similar approach can be performed for the Arrow-Debreu security
associated with state s = 0. This two states reflect that there are two independent
assets to consider all set of possible risks, but the model can be extended to
more general case with many states. By investing in the appropriate portfolio
of Arrow-Debreu securities investors can structure any set of state-contingent
claims. (for keep simple the understanding of the A-D model, this case is not
discussed in this paper).
If we note
the price of the Arrow-Debreu security, than the price
associated with state s = 1 can be expressed with:
By a simple arbitrage argument,
must be equal to the cost of
building the respective portfolio –purchasing α units of the risky assets
55
whose price is 1, from which we must deduct a loan of B. Let assume that
, than the Arrow-Debreu security associated with state s = 1 is a
call-option with strike price
. This mechanism of the arbitrage pricing
can be further refined in more complex environment, as Black and Scholes
(1973) accomplished.
With this simple approach we wanted to show, how to obtain the price
of this call-option from the characteristic of the risk of its underlying asset,
but also the inverse is valid, once we know the price for the Arrow-Debreu
securities we can calculate the price of any financial assets. One particular case
is a risk-free bond with a payoff of 1 in each state of nature, when the price of
the bond must be the discontinued value of one unit of wealth received with
certainty at the end of period:
Furthermore, having a portfolio of S assets consisting of one unit of
each and every Arrow-Debreu security also would have a risk-free payout of
1. Therefore using no-arbitrage arguments, we must have
Let define
, which is usually referred as the risk
neutral probability for state s. By this interpretation, we can see that the price
of any asset in a complete market is its expected value, discounted as risk-free
rate.
Optimal portfolios of Arrow-Debreu Securities
We noted cs the investment in the Arrow-Debreu security associated
to state s, the problem of the investor having an initial wealth w, is to determine
the demand for each of the Arrow-Debreu securities in order to set-up an
optimal portfolio. The program of the investor can be written as
where the only restriction is related to the budget constraint
.
In other words, if we could consider S number of different goods,
where
is the price of good s, the problem of the investor is that under the
budget constraint how to select a bundle
that maximises his
utility
56
For optimality, the following first-order conditions are both necessary
and sufficient
where ξ is the Lagrange multiplier associated to (2), equal to the
marginal utility of additional wealth. The optimal consumption depends
depend from the state s only through the ratio
of the state price
per unit probability. In the particular case of S = 2, if there are two states with
the same price per unit of probability for the associated contingent claims, the
optimal investment is to buy the same quantity of these claims.
Simple graphical illustration
For a simple understanding of the model we return to the particular
case of two states nature S = 2, and we assume that the risk-free rate is zero
(r = 0) in order to focus on the risk aspects of the model. The set of claims {(
)} presented in the graph, for which
, represent an isoexpected-value locus (i.e. set of claims with mean wealth ), which actually
represent the budget constraint of the investor. We set the EU (expected utility)
equal to constant k in order to analyse investor’s preferences, than results that
which is shown as a curve in the diagram. The set
of claims for which EU is constant defines an indifference curve in state-claim
space
.
We notice that the 45o line is referred as the certainty line, and
represents the locus of claim with equal consumption in both states of world.
57
From the above, we observe that
is the marginal rate of
substitution (MRS) between states. It is the rate of trade-off, at the margin,
between one unit of consumption in state 0 and consumption in state 1 for which
the investor is just indifferent. This lead to the conclusion that individuals are
neutral to the introduction of small risk.
Finally we can conclude that in a complete economy with no arbitrage
opportunity, there exists a “risk-neutral” probability distribution in a way that
the price of any asset can be expresses as the discounted value of the riskneutral expectation of its future payoffs.
References
1. Ameur, H.B.; Prigent, J.L. (2010). Behaviour towards Risk in Structured Portfolio
Management, International Journal of Economics and Finance Vol. 2, No. 5, pp.
91-102
3. Anghel M.G. (2013). Identification of financial instruments – important step in
building portfolios, Romanian Statistical Review, No. 9, pg. 52 – 65
4. Anghel M.G. (2013). Model of Portfolios Analysis, Revista Română de Statistică –
Supliment/Trim IV, pg. 39 – 43
5. Anghel M.G. (2013). Concept of the Portfolio Management, Romanian Statistical
Review – Supplement/December – „Statistical-Econometric Models Used in the
Study of Economic Variables Evolution”
6. Anghel, M.G. (2011). General aspects about management portfolios of financial
instruments, ART ECO - Review of Economic Studies and Research, Nr.3
8. Anghelache, C. (coord.), 2014. Statistical-Econometric Models Used To Studyb
The Macroeconomic Correlations, Romanian Statistical Review-Supplement,
December 2014.
9. Baule, R. (2010). Optimal portfolio selection for the small investor considering
risk and transaction costs, OR Spectrum, v. 32, iss. 1, pp. 61-76
10. Gans, J.; Murray, F.E. (2011). Funding scientific knowledge: selection, disclosure
and the public-private portfolio, Working Paper 16980
11. Kopman, L.; Liu, S.S., Shaw, D. (2009). Using Lagrangian Relaxation to Obtain
Small Portfolios, The Journal of Portfolio Management, pp. 75-79
12. Samuelson, P.A. (1963). Risk and uncertainty: the falacy of the Law of Large
Numbers, Scientia 98: 108-113.
58
Portfolio Management and Predictability
Prof. Vladimir MODRAK, PhD
Technical University of Košice
Marius POPOVICI PhD. Student
Abstract
Future developments of states and states of nature of a system are
predictable. Portfolio management needs predictability techniques In order to
benefit of opportunities. In theory, predictability has no time dimension. Practically,
as opportunity is embedded stochastically, there may appear changes of state that
are predictable, as in the correlation between returns and stocks. A certain resource
reversion might be possible with regard to returns and stock. The predictability
of the optimal portfolio management becomes the objective of any investor who
follows a flexible strategy based on optimal exposure to risk. Thus, investors will
try to anticipate the possible shocks affecting the opportunity set of their investment.
More precisely, they will admit the possibility to hedge any bad news concerning
the future opportunity set, the so called “myopia” relative to time horizon when
predictability is possible. This circumstance is part of the relative risk aversion. We
can affirm that predictability has the same effect as a reduction of risk aversion.
Key words: Portfolio management, predictability, risky assets,
hedging demand, planning horizon, conditional distribution, marginal value
of wealth.
Introduction
Predictability represents the ability of an entity or system to value the
future developments of the states of nature of a system. Especially with regard
to portfolio management, predictability is important as a possibility to detect
future laws of the decision process, but also measures to be taken in order to
re direct the evolution of management process. Predictability is closely related
to opportunity concept, and in correlation with management portfolio it has to
do with time management too.
Theoretically, a problem of portfolio management will deal with
a settled opportunity which is non-variable in time. In the real world, the
opportunity is settled in a stochastic manner, with some changes of state being
59
predictable. For example, predictability is possible in case of the correlation
between incomes and stocks. For example, there is a kind of reversibility
of resources in case of returns relative to stocks and it was lately accepted.
Thus, a large cash-in generated by a risky portfolio implies a smaller cashin tomorrow. In other words, good news of today will bring bad news in the
future regarding opportunities.
Predictability and the optimal dynamic portfolio
When exposing themselves to risky decisions, investors follow a
flexible strategy. This is part of the process of opportunity organizing. They will
try to anticipate any shock inside the opportunity set. More precisely, they will
admit the possibility to hedge any bad news concerning the future opportunity
set. This process is not a difficult one if shifts made are statistically correlated
with the current returns. The demand for stocks due to this anticipation is called
“hedging demand” for stocks. As stocks are considered safer on long terms than
on short terms, intuition suggests that an investor with a longer planning horizon
will take more risks early in life than one with a shorter planning horizon.
In order, to simplify this picture we will limit our analysis to the
constant relative aversion to risk
with a time horizon of two periods.
Constant relative risk aversion implies myopia relative to time horizon when
predictability is not possible.
Suppose the economy has one risk-free asset with zer return, and one
risky asset in period t with return denoted by x̃t, t = 0,1. The opportunity
set in the second period comes from the supposition that distribution of
x̃1 is correlated with x̃0. Suppose that Ex̃0
. Investors invest only with
regard to retirement at the end of the second period, with no intermediary
consumption.
In order to evaluate the first period optimal demand for the risky asset,
particularly the hedging component, we have to follow a certain method. We
will begin by solving the problem that confronted the investors in the second
period for each possible situation. The news are related to not only the wealth
z accumulated by that time, but also by the return generated by the risky asset
x0 in the first period. More exactly, the value function v is defined by
(1)
The optimal solution for this program is a separable function 1(z,x0)
= a(x0)z. This implies that the value function is separable, with v(z,x0) = h(x0)
z1- γ/(1- , where
h(x0) = E[(1 + a(x0)x̃1)1- γ | x0].
60
Let’s get back to the first period decision problem. This can be written
as
(2)
In order to determine the hedging component for risky assets demand,
we compare 0* to the demand for risky assets when there is no predictability,
for example when x̃1 is independent of x̃0. In this specific case, we know that
myopia is optimal. Thus, in the absence of any predictability, investors have
to solve the following:
When returns are somehow predictable, the hedgind demand is 0*m. The hedging demand will be positive if the derivative of H evaluated at
0
m
0 is positive. In other words,
anytime E [x̃0 ( w0 + 0m x̃0 ) 1- γ]=0.
In order to evaluate a specific type of predictability, let us examine
the case of an increase in x0 which deteriorates the distribution of x̃1 in sense
of first order stochastic dominance (FSD). We have a special case when the
stochastic process (x̃0, x̃1) indicates a mean-reversion. Suppose the conditional
distribution of x̃1 may be written as x̃1| x̃0= - x0+ , where is considered
to be independent of x̃0 and where k is a positive scalar. As any shift of
FSD in x̃1 diminishes final wealth EU, this assumption implies that
0 is
1γ
negative. Since v(z,x0) = h(x0)z /(1- γ), it results that h’ should be negative
when γ <1, and positive when γ>1.
Suppose that relative risk aversion γ is larger than unity. As h’ should
be positive in this case, it follows that for all x0,
.
Considering the expectations for both sides, it follows in turn that
.
Thus, the hedging demand is positive when the relative risk aversion
is larger than unity. In case we have a relative risk aversion less than unity, γ
<1, than h’ is negative and the inequality is reversed. This result is rezumed
in the following proposition:
Suppose the increase of return in the first period deteriorates the
distribution of return of the second period in the sense of the first-order
61
stochatic dominance. Then, the hedging demandf or risky asset is positive
(respective negative) if constant relative risk aversion is larger (respectively
smaller) than unity.
An other interpretation of this result is the following: when the relative
risk aversion is constant and larger than unity, a longer time horizon should induce
the wish of investors to take more risks. The contrary is true also if the relative risk
aversion is smaller than unity. Note the fact that when investors have a logarithmic
utility function (γ =1) myopia is still optimal in the presence of predictability.
The choice of an initial risk portofolio is dictated by the fall of the marginal value
of wealth at the end of the first period. This wealth marginal value depends on the
future opportunities set. In case predictability reduces the wealth marginal value
in its abundence states, making it increase where it is low, then predictability has
the same effect as a reduction of the risk aversion: it raises the risk optimal level
in the portfolio. as consequence, we observe that the next step of the analysis is to
determine the effect of the FSD deteriorating-shift in the return of the risky asset
will have on the marginal value of wealth. In the special case of mean reversion,
we have two different effects of x0 increase. The first effect is the effect of wealth:
as the return expected in the second period becomes smaller , the same is going
with wealth, which becomes smaller. This event raises the wealth marginal value,
while v is concave in z. The second effect is a precautionary effect: investors will
invest less in the risky asset thus reducing the risk exposure. Under prudence,
this reduces the wealth marginal value. The global effect of an increase in x0 of
the marginal value of wealth is ambiguous. When the relative risk aversion is
constant and larger unity (and this happens if and only if the absolute prudence
is smaller than twice the absolute risk aversion, that explains why this condition
implies the fact that precautionary effect is dominated by the wealth effect), then
the wealth effect will be always dominant against the precautionary effect, and the
hedging demand is positive. When the relative risk aversion is less then unity, the
wealth efect is dominated by the precautionary effect.
Conclusions
Risky decision regarding portfolio are implied in management activities
with regard to stocks. Let’s consider that an opportunity set is important for any
investor who manages risky assets. In theory, predictability was not an aspect
of interest given the lack of any time horizon assumption. In every day life and
experience, opportunity and its management is possible and it explains the changes
of states of any process in stochastic expression. Thus some changes of state might
be predictable with regard to return and stocks’volume correlation. The predictability
of the optimal portfolio management becomes the objective of any investor who
follows a flexible strategy based on optimal exposure to risk. Thus, investors will
62
try to anticipate the possible shocks affecting the opportunity set of their investment.
More precisely, they will admit the possibility to hedge any bad news concerning
the future opportunity set, the so called “myopia” relative to time horizon when
predictability is possible. This circumstance is part of the relative risk aversion.
When relative risk aversion is larger than unity, hedging demand for risk assets is
positive, that means that the time horizon is larger making investors to wish to invest
more risky. When the relative risk aversion is less than unity, hedging demand for
risky assets is negative, while time horizon is smaller. In presence of predictability,
myopia is the optimal solution of the investors. Considering all these, we can affirm
that predictability has the same effect as a reduction of risk aversion.
References
1. Anghel M.G. (2015). Correlation between BET Index Evolution and the Evolution of
Transactions’ Number – Analysis Model, International Journal of Academic Research
in Accounting, Finance and Management Sciences, Volume 5, No. 4, October 2015,
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Vol. 47 No. 4, Printed in U.S.A., pp. 867-909
12. Pagliacci M.G.R., Anghel M.G., Sacală C., Anton V.L.(2015). Some Models used for
setting up the Futures Price, Romanian Statistical Review - Supplement/No. 6, pg.
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forecasting, Working Paper 18222
63
Significant Aspects of Investment Dynamics
Abstract
The myth of different choices regarding portfolio composition by
taking risks on the long term and on the short term follows to be busted.
Whether the opportunity of taking risky decisions regarding investments in the
future influences or not short term decisions on risky investiments, especially
when retirement is envisaged, always represented a dilemma for those with a
short span of life but still investing. Problems like intermediary consumption,
exposure to risky investments or differences between young and old investors’
portfolio and time horizon issues, are going to be investigated in this article,
finding a solid answer by means of mathematical modelling.
Key words: taking risk, planning horizon, risky stock, investment
problem, absolute risk tolerance, budget constraint, state price per unit of
probability, risk-free rate, investment dynamics
Introduction
The problem of the way the opportunity of taking risks in the future
influences the will of takings risks in the short run is important taking into
account various situations, especially when making decisions such as
investments targeting retirement. This is a special case of investment activities
when intermediary consumption is lacking. Intermediary consumption is
present otherwise in all other kinds of investment circumstances scientifically
known as ”investment problem”. This is when the investor’s objective is
represented by the intention of maximizing the EU of his wealth - already
accumulated, at a specific date.
Of course, the concept of intermediary consumption will be introduced
later in this article, as well as the idea that risks are independent over time.
Taking risks and wealth management
Let’s consider a general situation that allows us to issue general
conclusions regarding this topic. Let us admit the existence of an investor
having a certain 0 accumulated wealth, and who lives for two periods of
64
time. When each of these periods starts, the investor has the opportunity to
invest taking certain risks. Later, at the end of each period, he will observe the
effects of his investment decisions. It is important to see that he will observe
the effects of his investing decions in the first period before being able to
decide how to take risks for the second period. This will be the source of a
certain dynamics of the respective problem, generating a flexibility which is
essential for risk management. Evidently, investors will take less risks in the
second period in case they lost a large amount of their investments in the first
period. This circumstance of second period problem finds a perfect expression
in Arrow-Debreu portfolio decision which we assume.
Suppose that we have S possible states of nature s = 0 ,..., S – 1. There
is an uncertainty dominating the second period described by the probability
vector (p0,... , ps-1). Considering Пs as the unit price of Arrow-Debreu
security associated to state s, we also assume that the rate of risk free is zero.
This means that a claim paying one lei in each state of nature should itself cost
one lei:
So to speak, the investor who doesn’t take any risk in
the second period will end by having the samefinal wealth as at the end of the
first period. Having the wealth z, this investor will choose the portfolio (c0, ...
, cs-1) maximizing EU of his wealth at the end of the period when his budget
was constraint:
(1)
This is equivalent to
(2)
when
1 = (c0,...,cs-1).
During zero period, the investorhas to take a risky decision 0 with
a payoff z( 0,x) depending on the realization x of some random variable x̃ .
This might be another problem of portfolio choice. We are able to obtain the
optimal exposure to risk in zero period by using the following program:
(3)
In order to determine the opportunity of taking risks in the second
period based on the optimal exposure to risk in the first period, we have to
compare the solution obtained previously with theoptimal exposure to risk in
the first period when there is no such an option to take further risk in the a
65
following period. Such an investor with a short span of life similarly to the
myopic investor would selecy level 0 able to maximize the EU of z( 0,x̃ ):
(4)
Given these two programs (3) and (4) based on two periods and on
only one period, we can see the difference between them: the utility function u
in the first program is replaced by the value function v. By this, the effect of the
future is completely comprised in the value function. For our circumstances,
we can say that the opportunity to take risk for the future make our willingness
to take risks todayonly if v is less concave than u with regard to Arrow-Pratt.
And this is a consequence of the case of a one-risky-one-risk-free portfolio
problem we already presented above.
We can affirm that the optimal exposure to risk in the first period is
larger than the myopic exposure in the value function v defined by program (1)
is less concave than the original utility function u. In this case, the tolerance
degree to absolute risk of v is more risk tolerant than u. The degree of absolute
risk tolerance of v can be defined by the following proposition:
”The value function for Arrow-Debreu portfolio problem (1) has a degree of
absolute risk tolerance by the following equation:
(5)
where c* is the optimal solution to the problem (1) and T(·)=
-u’(·)/u’’(·) represents the absolute tolerance to risk for final consumption”.
As a proof of this proposition: The optimal solution to the program (1)
is denoted c*(z). It has the property of satisfying the following condition of
first-order:
er:
(6)
where s = Пs/ps represents the state price per unit of probability
and ξ(z) is the multiplier associated with (1) for a particular value of z. The
condition of a complete differentiating (6) regarding z and eliminating s is
given by:
(7)
A diffentiating budget constraint will result in turn in:
66
(8)
By replacing cs*’(z) by its expression in (8)it will follow that
(9)
Finally, by fully differentiating v(z) , which by definition equals
u(c
*(z)),we
obtain
s
s
v’(z)
su’(cs*(z))cs*’(z)
s*’(z)
What we obtained by associating the Lagrange multiplier to the budget
constraint of the consumer equals the shadow price of wealth. From this result
we can see that v’’(z)
and Tv(z)
(z)/
The proposition
follows immediately from equation (9).
We accept that the risk-free rate is zero, in turn implying that
this eliminates a potential effect of wealth for those who allow themselves to
invest in the second period. In this case, property (5) states that the absolute risk
tolerance of the value function is a weighted average of the risk tolerance degree
of the final consumption. This property allows us to compare the degrees of
concavity of v and u. For example, suppose that u exhibits „hyperbolic absolute
risk aversion” (HARA), i.e. that T is linear in c.
This implies that Tv(z)=
s*)=T(
s*)=T(z). Thus,
when u is in HARA, the value function v has the same degree of concavity
as u: v(·) = Ku(·). The consequence is that the two programs have exactly the
same solution. In other words, the option to take risk for the future has no effect
on exposure to risk today. In this case, myopia is optimal. Ceteris paribus, the
young and old investors should select the same portfolio composition.
Assuming that the opportunity of future risk taking will raise the
tolerance to current risks. in order to do this, we suppose that the utility
function u expresses a convex absolute risk tolerance, and by applying Jensen
inequality, it follows that Tv(z)=
s*)= T(
s*)=T(z). A nonnegative T” is compatible with the intuition that a longer time horizon might
induce a supplementary risk taking which may be found in the fourth derivative
of the utility function. On the other hand, with a T” non-positive, a longer time
horizon for investment should imply a more conservative on short term.
A second proposition is „Suppose that risk-free rate is zero. In the
dynamic Arrow-Debreu portfolio problem with serially independent returns,
67
a longer time horizon raises, respectively reduces the optimal risk exposure on
short term if the risk absolute tolerance T(·) = -u’(·)/u’’(·) is convex, respectively
concave. In HARA, time horizon has no effect on optimal portfolio”.
When the long term investment is targeted for consumption at a certain
date, whether the investor is supposed to modify his own exposure to risk as the
time horizon recedes is an empirical question which relies on convexity, linearity
or concavity of absolute risk tolerance. Of course, none of these conditions should
be maintained for all wealth levels. A tolerance to risk is possible which sometimes
is convex, other times is concave. We cannot be able to envisage or predict the
effect for a longer planning time horizon with regard to an investment strategy.
Depending on circumstances, this investor might invest more money in stocks,
other times he might invest in bonds more than would be invested under myopia.
One might think about convexity/concavity of absolute tolerance
in terms of introspection. Remember that the amount invested in stocks is
approximately proportional to T. It is about a wealth raise. Question is if we
have to do with an increase at an increasing rate as wealth increases. If it is so,
this might mean a convex T and a positive effect for the opening of the time
horizon while taking risks. It is possible that one to assume that this is a forced
assuming in order to simplify things, and not for the realism of approach.
Econometric tests for HARA are rare as literary resources.
Conclusions
Under HARA preferences, choosing to take risk in the future has no
effect on optimal exposure to risk today. That comes to the fact that myopia in
investments is optimal.
The opportunity of taking risks in the future raises the tolerance to
the current risks. A risk tolerance non-negative might be compatible with the
intuition the longer time horizon is, the bigger need of taking risk will be. On
the other hand, when risk tolerance is non-positive, a longer time horizon for
investments should imply a more conservative investment on the short run.
Also, when the long run investment is targeted for consumption at a
certain date, it is a matter of concavity, convexity or linearity of absolute risk
tolerance for the investor to modify his exposure to risk as the time horizon
recedes. None of these conditions should be applied to all levels of wealth.
Sometimes, the tolerance to risk is concave, other times it can be convex. For
such cases and individuals, it is not possible to predict the effect of a longer
planning horizon with regard to investment strategies. His investments will
be directed sometimes to stocks, sometimes to bonds, somehow chaotic, than
would be invested under myopia. Generally, myopia is considered optimal
simplifying the analysis.
68
References
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financiar-bancară şi informatică, Editura Artifex, Bucureşti
2. Anghelache, C. , Anghel, M.G. (2015). Statistică. Teorie, concepte, indicatori şi
3. Anghelache, C., Anghel, M.G. (2015). Main Aspects Regarding Some Non-Linear
Models Used in Economic Analysis/ Revista Romană de Statistică Supliment/
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4. Anghelache, C., Anghel, M.G. (2015). Theoretical Aspects Concerning the Use
of Statistical-Econometric instruments in Analysis of Financial-Assets, Revista
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5. Anghelache C., Anghelache G.V., Anghel M.G. et al (2014). The International
Trade Evolution, Revista Romanian Statistical Review - Supplement/No. 1, pg.
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the foreign investment, Romanian Statistical Review - Supplement/No. 10, pg. 7 –
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7. Anghelache, C., Pagliacci, M., Mitruț, C. (2014). Econometric Analysis through the
Use of Statistical-Econometric Models, Romanian Statistical Review Supplement
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8. Anghelache, C., Anghelache, G.V., Partachi, I., Stanciu, E., Dragomir, B. (2014).
Macroeconomic Analysis based on Econometric Models, Revista Romană de
Statistică Supliment 10/2014.
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intern brut şi a investiţiilor străine directe în ultimul deceniu, Revista Română de
Statistică – Supliment Trim.1, pg. 198-204
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Editura Meteor Press, Bucureşti
69
Model for estimating Financial Results
at Company Level by using Simple Linear
Regression
Andreea-Gabriela BALTAC PhD. Student
Zoica DINCA (NICOLA) PhD. Student
“Artifex” University Bucharest, Bucharest University of Economic Studies
Abstract
The econometric model means a lot of numerical relations that enables
simplified representation of the economic process subject to study. Current
models often involves more than ten relations (equations).
The validity of a model is tested by comparing the results with
statistical observations. In order to study an economic phenomena, he is
represented through a variable behavior. This economic variable depends on
other variables of which is linked through mathematical relationships.
The regression analysis is a statistical technique based on the
identification of two variables and is used to relate variables.
Key words: econometric model, simple linear regression, evolution,
variables, analysis, performance, estimation.
By using the simple linear regression, we predict scores on one
variable from the scores on a second variable. The variable we are predicting
is called the criterion variable and is referred to as Y.
The variable on which we are basing our predictions on is called the
predictor variable and is referred to as X. When there is only one predictor
variable, the prediction method is called simple regression.
In simple linear regression, the topic of this section, the predictions of
Y when plotted as a function of X form a straight line.
In 1990, Bowerman et al, have revealed that the regression analysis is
a statistical technique based on the identification of two variables and is used
to relate variables.
The purpose of this model is to build a mathematical model to relate
dependent variables to independent variables.
In 1981, Draper and Smith, defined a regression model as a single
algebraic equation, which was the next form:
70
Z = f (X1, X2,…. Xk) + u
where:
Z = is a dependent variable than can be explained by the variables
(X1, X2,…. Xk)
u = is a random variable
Comparing the results with statistical observations is the way in which
the validity of a model is tested. An economic phenomena, in order to study,
is represented through a variable behavior.
This economic variable depends on other variables of which is linked
through mathematical relationships.
In this article, my purpose is the analysis of two variables by using the
simple linear regression.
I have made a model of simple linear regression used in measuaring
financial performance.
The purpouse is the estimation of the financial results frm TRANSGAZ
S.A. during 11 years, meaning the period 2004-2014, through simple linear
regression method.
In order to highlight practically how to use simple linear regression
analysis of financial results at Transgaz S.A., I will start from a series of data
on the evolution of the turnover and personnel expenses over the past decade.
The evolution of turnover and personnel expenses durring 2004 – 2014
Table 1
(thousand lei)
Year
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
Turnover
772.277.441
770.115.066
909.017.487
1.038.866.794
1.119.389.990
1.187.350.293
1.308.103.000
1.336.979.000
1.327.987.000
1.484.710.000
1.618.090.000
Personnel expenses
104.882.303
123.213.024
141.326.802
175.292.350
208.185.701
224.549.127
300.132.000
322.471.000
364.884.840
344.869.390
351.858.470
For each of the 11 years, I have presented the values of the turnover
and the personnel expenses, in order to see the influence of the two indicators
over the eachother.
71
The two indicators registered positive values increasing from year to
year in the economic analysis. Practically every year of the period investigated,
the turnover increase, which also increase the personnel expenses.
Since 2004 and untill 2014, the values of the two indicators, turnover
and personnel expenses grown from one year to another, which is the result of
the performance registred by the economic agent that we have studied.
We note that in 2008, the year when the financial crisis unleashed
in Romania, we are witnessing a modest turnover growth once again
1,119,389,990 lei, unlike in 2007 when it totaled 1,038,866,794 lei.
The following year, 2009, the turnover is still low but positive, being still
under the impact of the financial crisis. In 2010, the turnover is 1,308,103,000
lei and reach by 2014, the last year covered a total of 1,618,090,000 lei.
For a better view I have also made a graphic representation of the
evolution of turnover and personal expenses during 2004-2014.
Figure 1
From the figure we can see that the evolution of the expenditures
of personal development is somewhat similar to that of turnover, showing a
strong link between the two variables analyzed.
The strong link between the two variables is similar of the developments
highlighted by the data series.
Through Eviews statistical software for data analysis, we examine
the evolution of two variables analyzed to estimate the parameters of the
72
regression model. We defined the resultant turnover variable, and the variable
factor is the amount of time personnel expenses.
Eviews, gaved us information about the values of R-squared,
Adjusted R-squared, S.E. of regression, Sum squared resid, F-statistic, Prob
(F-statistic).
The values are presented in the table below.
(Eviews)
Table 2
C (1) = turnover
C (2) = personnel expenses
The recorded value of the personnel expenses has a major influence
on the development of turnover, any modification at this level increasing the
turnover value. Free factor taken into account in the regression model, has a
significant influence.
Factors that were not taken into account when building econometric
model lead to a significant reduction of the turnover.
Recorded values of statistical tests R and R2 are close to 85%,
respectively 90.64% and R-squared = 90.64% and Adjusted R-squared =
89.61%, which verifies the accuracy of the econometric model considered.
We can say that the model is correctly analyzed, showing a level of
risk acceptable if an economic analysis.
Fot the Test F-statistic value, 87.24677 is higher than reference table
(4.6), which emphasizes the idea that the econometric model is properly
considered, it can be used in economic analysis and forecasting of the
turnover.
73
Conclusion
The econometric model means a lot of numerical relations that enables
simplified representation of the economic process subject to study.
In statistics, simple linear regression is the least squares estimator of
a linear regression model with a single explanatory variable.
Simple linear regression refers to the fact that this regression is one
of the simplest in statistics.
The purpose of this article, was the analysis of two variables by using
the simple linear regression. For this analysis the two variables used where:
turnover and personnel expenses. For each of the indicators I have considered,
I presented the values during the years 2004-2014.
I have made a model of simple linear regression used in measuaring
financial performance.
The purpouse is the estimation of the financial results frm TRANSGAZ
S.A. during 11 years, meaning the period 2004-2014, through simple linear
regression method.
The model for estimating financial results at TRANSGAZ S.A. by
using simple linear regression reveals the existence of a linear connection
between the amount of turnover and personal expenses.
This points to the possibility of using a linear regression model to
study the relationship between the two variables.
The results have shown that the value of the personnel expenses has a
major influence on the development of turnover, any modification at this level
increasing the turnover value.
The two indicators registered positive values increasing from year to
year in the economic analysis. Practically every year of the period investigated,
the turnover increase, which also increase the personnel expenses.
Since 2004 and untill 2014, the values of the two indicators, turnover
and personnel expenses grown from one year to another, which is the result of
the performance registred by the economic agent that we have studied.
We note that in 2008, the year when the financial crisis unleashed
in Romania, we are witnessing a modest turnover growth once again
1,119,389,990 lei, unlike in 2007 when it totaled 1,038,866,794 lei.
The following year, 2009, the turnover is still low but positive, being still
under the impact of the financial crisis. In 2010, the turnover is 1,308,103,000
lei and reach by 2014, the last year covered a total of 1,618,090,000 lei.
The strong link between the two variables is similar of the developments
highlighted by the data series.
74
Through Eviews statistical software for data analysis, we examine
the evolution of two variables analyzed to estimate the parameters of the
regression model. We defined the resultant turnover variable, and the variable
factor is the amount of time personnel expenses.
Eviews, gaved us information about the values of R-squared,
Adjusted R-squared, S.E. of regression, Sum squared resid, F-statistic, Prob
(F-statistic).
The recorded value of the personnel expenses has a major influence
on the development of turnover, any modification at this level increasing the
turnover value. Free factor taken into account in the regression model, has a
significant influence.
Factors that were not taken into account when building econometric
model lead to a significant reduction of the turnover.
Recorded values of statistical tests R and R2 are close to 85%,
respectively 90.64% and R-squared = 90.64% and Adjusted R-squared =
89.61%, which verifies the accuracy of the econometric model considered.
We can say that the model is correctly analyzed, showing a level of
risk acceptable if an economic analysis.
Fot the Test F-statistic value, 87.24677 is higher than reference table
(4.6), which emphasizes the idea that the econometric model is properly
considered, it can be used in economic analysis and forecasting of the
turnover.
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Financial Performance Reporting: The Statement Of Comprehensive Income”,
The Annals Of University Of Oradea Economic Science Series Tom Xviii;
17. Spearman C. ‚’’The proof and measurement of association between two things’’,
Am J Psychol 1904; 15:72–101;
18. Thomas, R.L. – “Modern econometrics – an introduction”, Editura „Financial
Times – Prentice Hall”, 1997;
19. http://scholarbank.nus.edu.sg/bitstream/handle/10635/14854/09Chapter%202.
pdf?sequence=8
20. https://www.spl.harvard.edu/archive/splpre2007/pages/papers/zou/
REGRESSION.pdf
76
Risk Aversion and Individual Preferences
Modelling
Bucharest University of Economic Studies, “Artifex” University Bucharest
Lecturer Mădălina Gabriela ANGHEL PhD.
Artifex” University Bucharest
Abstract
Confronting risky situations is a feature of any decision making, in
general, but, especially for specialized decision making contexts. Risk aversion
concentrates the problems of modeling individual preferences. Models can
grasp with accuracy and largely enough the fundamental human tendencies.
As consequence, understanding economic behavior confronted to risk might
be understood. Modeling market forecasting activities is possible based on
“marginal consumer”. Thus, the vague use of mathematical instruments is
necessary, but not enough as condition to develop an economic analysis.
A historical approach of risk analysis might bring an idea regarding the
development of the concept instruments used today when analyzing “risky
choices”
Key words: risk theory, diversification, risk aversion, risk premium,
absolute risk aversion, relative risk aversion, utility function, marginal
consumer, marginal utility, DARA, CARA, CRRA.
A short presentation
In 1738, Daniel Bernoulli published “Specimen theoriae novae
de mensura sortis”, a paper in Latin, or “Exposition of a new theory on
measurement of risk”. The English translation made in 1854 seemed to be
non-technical and focused on the way two people confronted to the same
risky situation might choose depending on psychological differences. While
presenting his hypothesis, Bernoulli used three examples. In this sense, “Sankt
Petersburg paradox” is quite famous and still present today in academic
circles, and shows the importance of psychological aspects with regard to
risky choices. Unfortunately, the celebrity of this paradox has overshadowed
the other two examples. They show that the value of a risky situation is not
equal to its mathematical expectation. One is known as “Sempronius” and it
anticipates in an exemplary manner the contributions brought 230 years later
in the risk theory by Arrow, Pratt et alias.
77
Quoting Bernoulli, Sempronius owns goods at home worth a total of
4 000 ducats and in addition he possesses 8 000 ducats worth commodities
in foreign countries from where they can be transported only by sea. Our
common experience shows that from two ships one perishes.”
Sempronius is confronted with a risk on his wealth. x̃ represents his
wealth on a value of 4 000 ducats with probability
(if ship is sunk), or 12
000 ducats with probability . We denote this lottery as x̃ being distributed as
Its mathematical expectation is given by:
(4000,
Ex̃
4000 +
12000 = 8000 ducats.
Sempronius comes with a big idea. Instead of trusting all 8 000 ducats
of goods to one ship, he “entrusts equal portions of his commodities to two
ships”. Assuming that the ships follow independent but equally dangerous
routes, Sempronius now faces a more diversified lottery ỹ distributed as
(4000, ; 8000, ; 12000, )
In case Sempronius looses all his ships, he still has 4000 ducats wealth.
AS the two risks are independent, their probability equals the result of the
individual events, exactly ( )2 = .Thus, there is the probability of that the
two ships to be back home, and thus the final wealth of Sempronius is 12000
ducats. Evidently, there is also the other chance for him to have only one
ship back with the final result of half of the previous wealth. The final wealth
would be of 8000 ducats. The probability of this event is of completing the
other two events with probability of .
The common experience leads to the idea that diversification is good,
so that we can expect the value attached to the event ỹ to exceed the one
attached to the event x̃ . Even so, calculating the expected profit, we may
obtain:
Eỹ = ¼ 4 000+ ½ 8 000+ ¼ 12 000 = 8 000 ducats,
which means the same value as for Ex̃. Thus, diversification seems
useless!
Using intuition, Bernoulli’s demonstration will lead to the conclusion
that lottery ỹ might bring a satisfaction value larger than lottery x̃, thus the
satisfaction level generated by lottery ỹ will make Sempronius choose the
first of them. Thus, Bernoulli shows that diversification generates a transfer
conserving means to gain wealth, from extremes to the middle. Thus, transferring
78
probability from x=4 000 to x=8 000 a, increase of expected utility is obtained.
Each transferred probability unit generates an increase of expected utility equal
to u(8000) - u(4000). On the contrary, transferring a certain probability from x
= 12 000 to x = 8 000, it will reduce the expected probability. Each transferred
probability unit will generate a reduction of the expected utility equal to u (12
000) – u (8 000). But concavity of u implies that
u (8 000) - u (4 000) u (12 000) - u (8 000),
(1)
With the positive aspect that these transfers combined should dominate
the negative effect.
Definition and characteristics of risk aversion in investments
Supposing agents live only one investment cycle, investing their
wealth purchasing and consuming goods and services. The final wealth is
formed of the initial wealth w plus the outcome of any risk borne during the
period.
Definition 1: An agent has risk aversion if, at any level of wealth w
he dislikes every risk with expected payoff of zero. Thus, he always prefers
receiving the expected outcome of a risky decision with certainty, rather than
the risk itself. For an expected-utility maximize with a utility function u, this
implies that, for any risky situation z̃ and for any initial wealth w,
E u ( w + z̃ ) u ( w + E z̃).
(2)
In Sempronius’ case, his risk aversion is obvious. This is true anytime
the utility function is concave. If marginal utility decreases, the possible
loss reduces more the utility generated by potential gain. The preference
for diversification is intrinsically equivalent to risk aversion, at least under
the model of expected utility of Bernoulli. Reversely, if u is convex, the
inequality will be reversed. That is why the agent will prefer risky decision
to any mathematical expectation revealing his inclination to taking risks. This
behavior is known as loving risk behavior.
Finally, u is linear, then the welfare Eu is linear in the expected payoff
of lottery. If u(x) = a + b x for all x, then we have
Eu ( w +z̃) = E [a + b (e + z̃ )] = a + b ( w + Ez̃) = u ( w + Ez̃).
This implies that the agent would rank the lotteries according to their
expected outcome. This behavior is called risk-neutral.
A decision maker is risk averse if inequality (2) holds for all w and z̃ if
and only if u is concave. This proposition is in fact a rewriting of the famous
Jensen inequality with the conclusion that the decrease of the marginal utility
means an increase of the income. Lately, many researchers consider that risk
aversion is generated by marginal utility decrease. Other authors consider that
there is no connection between the two concepts.
79
Risk premium and the equivalent of certainty
An agent is risk averse in case he dislikes zero-mean risks. The degree
of risk aversion might be evaluated by asking him how much he is inclined
to pay in order to avoid a zero-mean risk z̃. The answer to this question will
be defined as risk premium associated to that risk. The agent will end up with
the same wealth either he will accept the risk or he will pay the risk premium.
When risk z̃ has a expectation other than zero, we usually use the concept of
the certainty equivalent. This is the sure increase in wealth that has the same
effect on welfare as having to bear the risk z̃.
The cost of risk, as measured by the risk premium, is proportional to
the variance of payoffs. Thus, the variance seems to be a good measure of the
degree of riskiness of lottery. This is why many authors used mean-variance
in order to model the behavior under risk. The validity of these models is
accurate only when the risk is small. In such cases, mean-variance approach
can be seen as a special case of the expected- utility theory. The mean-variance
played a very important role in the development of the finance theory.
The degree of risk aversion
Considering the small risk only, we can see that agents with a larger
absolute risk aversion are more reluctant to accept small risks. In their cases,
the minimum expected payoff is larger. Technically, the measure of the degree
of risk aversion is a measure of concavity of the utility function. It measures
the speed at which marginal utility is decreasing. In small risk cases, we need
to know to determine whether a risk is desirable is the degree of concavity
of u locally at the current wealth level w. For larger risks, we need to know
much more in order to make a decision, meaning needing to know the degree
of concavity of u at all wealth levels. So, the degree of concavity must be
increased at all levels of wealth to guarantee that a change in u makes the
decision maker more reluctant to accept risks.
Decreasing absolute risk aversion and prudence
As we could see, risk aversion is driven by the fact that one’s marginal
utility is decreasing with wealth.
Arrow considers that wealthier people are generally less willing to pay
in order to eliminate fixed risk. Thus, the risk premium associated to any risk
is decreasing in wealth if and only if absolute risk aversion is decreasing; or
if and only if prudence is uniformly larger than absolute risk aversion. Notice
that Decreasing Absolute Risk Aversion (DARA), a very intuitive condition,
requires the necessary condition that the utility function to be positive, or that
marginal utility to be convex.
80
Relative risk aversion
Absolute risk aversion is the rate of decay for marginal utility. More
particularly, absolute risk aversion measures the rate at which marginal utility
decreases when wealth is increased by one monetary unit. The index of
absolute risk aversion is not unit free, as it is measured per monetary units.
Economists prefer unit-free measurements of sensitivity. Defining the
index of relative risk aversion R as a rate at which marginal utility decreases when
wealth is increased by one per cent. In terms of standard economic theory, this
measure is simply the wealth-elasticity of marginal utility. Finally, the measure of
relative risk aversion is simply the product of wealth and absolute risk aversion.
The relative risk premium is equal to half of the variance of the
proportional risk times the index of relative risk aversion. This can be used to
establish a range for acceptable degrees of risk aversion.
There is no argument for or against decreasing relative risk aversion.
Arrow considered that relative risk aversion is likely to be constant or perhaps
increasing, but the intuition is not clear as was for decreasing absolute risk
aversion. Under the intuitive DARA assumption, becoming wealthier also
means becoming less risk-averse. This effect tends to reduce risk premium.
On the other hand, becoming wealthier means facing larger absolute risks, that
tends to raise risk premium. It is not clear if the first effect or the second will
dominate, as well as there is no a priori reason to believe that the dominant
effect will not change over various wealth levels.
Some classical utility functions
The expected utility (EU) theory is not accepted by everyone in
economic theory. Some researchers consider EU criterion satisfying for those
who find expected utility too restrictive. Economics and finance researchers
consider EU theory as an acceptable paradigm for decision making under
uncertainty. EU theory has a long “career” and a prominent place in the
development of decision making under uncertainty. Even those who reject EU
theory use it as a standard by which to compare alternative theories.
Some researchers often restrict the EU criterion by considering a
specific subset of utility functions in order to obtain solutions to different
problems. Of course, particular utility functions have different implications.
WE have to note that utility is unique only up to a linear transformation.
We note that during the 1960s the theory of finance considered the
subset of utility functions that are quadratic of the form
u (w) = aw - w2,
for w a.
u should satisfy the requirement of not being decreasing, which is true
only when w is smaller than a.
81
Above wealth level a, marginal utility becomes negative. Since
quadratic utility is decreasing in wealth for w a, many people might feel
this is not appropriate as a utility function. However, it is important that we
try to obtain models of human behavior with mathematical models. Quadratic
utility functions exhibit increasing absolute risk aversion and this is why they
are not in fashion anymore.
The second set of classical functions is the so-called constant-absoluterisk-aversion (CARA) utility functions, which are exponential functions,
whose domain is the real life. They exhibit constant absolute risk aversion
with A (w) =a for all w. The fact that risk aversion is constant is often useful in
analyzing choices among several alternatives. This assumption eliminates the
income effect when dealing with decisions to be made about a risk whose size
is invariant to changes in wealth. This is a reason for criticizing this function,
since absolute risk aversion is constant than decreasing.
Finally, the constant-relative risk-aversion (CRRA) class of preferences
is a set of power utility functions which eliminate any income effects when
making decisions about risk whose size is proportional to one’s level of wealth.
The assumption that relative risk aversion is constant simplifies enormously
many of the problems encountered in macroeconomics and finance.
References
1. Bernoulli,D. (1954). Exposition of a new theory on the measurement of risk.
(English Trans. by Louise Sommer.) Econometrica 22:23-36
3. Anghelache C., Anghel M.G., Manole A. (2015). Modelare economică, financiarbancară şi informatică, Editura Artifex, Bucureşti, 290 pg., ISBN 978-606-8716-00-8
4. Anghelache C., Anghel M.G. (2015). Statistică. Teorie, concepte, indicatori şi
5. Anghelache, C. (2008). Tratat de statistică teoretică şi economică, Editura
6. de Finetti, B. (1952). Sulla preferibilita. Giornale degli Economisti E Annali Di
Economia 11 :685-709.
7. Nachman, D.C.(1982). Preservation of ‘more risk averse’ under expectation.
Journal of Economic Theory, 28:361-368.
8. Pratt, J. (1964). Risk aversion in the small and in the large, Econometrica 32:122136.
9. Ross, S.A. (1981). Some stronger measures of risk aversion in the small and in the
large with applications, Econometrica 3:621-638.
10. Segal, U. and A. Spivak, (1990). First order versus second order risk aversion,
Journal of Economic Theory, 51:111-125.
11. Yaari, M.E. (1987). The dual Theory of Choice under Risk, Econometrica, 55:95115.
82
The major Macroeconomic Evolutions by the
end of July 2015
Bucharest University of Economic Studies, Artifex” University of Bucharest
Abstract
This paper describesthe main macroeconomic evolutions recorded at
the level of the Romanian economy during the recent period. Considering the
data published by the National Institute of Statistics, the authors present the
results describing the status and dynamics of the economy of Romania.
Key words: economy, evolution, outcome, politics, country
In 2015, Romania tries to cope with the effects triggered by the
economic-financial crisis, which either as develipong phenomenon or as a
new wave, will be prolongued with effects in the next years.
The presence of Romania within the European Union is underlining
the fact that an adequate program of steps meant to secure a unitary framework
is required in order to facilitate the implementation in practice of the postadhesion actions, mainly the absorption of the communitarian funds put at the
disposal of Romania.
But our country is holding a bi-factorial major problem, synthesized
in programs and projects of substance and, mainly, in its incapacity of
co-financing. Under the circumstances, we may recognize ourselves as
contributing-country to the European Union.
The outcomes recorded for 2010-2014 should be analyzed through the
angle of the fact that the outburst of the financial and economic crisis, during
the spring of the year 2007 in the USA and in 2008 in Europe also, including
Romania as well within the last part of the year 2008, has generated a number
of global and individual negative effects, of different intensity degree from
country to country and, meantime, approaches in respect of over-climbing the
crisis, both of global character and at national level.
Coming later to us, or being too deeply psychologically stimulated,
in 2009-2012, the crisis has produced economical-financial effects, beyond
imagination.
83
After this year, the concerns of the government aiming to rectify some
of the austerity steps started to give outcomes despite the fact that they were
altered by the lack of the required financial sources.
The major macroeconomic evolutions
The indicator the most synthetic for Romania, concerning the outcomes
recorded in 2010, is given by the Gross Domestic Product which counted for
513,640.8 million of lei, expressed in the current prices of the year 2010. In
2011, the increase of GDP was some 1.1% as against 2010, which, adjusted
at the level of 2011, reveals a value of 519,290.8 million lei. The year 2012
emphasized an increase of GDP by 0.6% as against 2011 and by 4.9% as
against the same year, deflated data, reaching the level of 596,681.5 million
lei, definitive data. In 2013, based on semi-definitive data, there is an annual
increase by 3.5% of GDP as against 2012. In absolute figures, the value of
GDP in 2013 counted for 617,565.4 million lei, non-deflated data.
In 2014, the evolution of GDP kept on increasing, if considering
the current provisional, reaching a level of 102.8% as against 2013. The
macroeconomic results indicators have recorded a negative evolution over the
period January 2009 - January 2012 as a result of the effects of the economic
and financial extended crisis, the lack of efficiency of the government activity
and lack of a coherent anti-crisis program, based on pro-active steps.
The GDP recorded decreases of during the period 2010-2012.
Meantime, the inflation target could not be hit, the direct foreign investment
diminished. In the first year 2014, the foreign debt increased, the domestic public
debt multiplied, the foreign payments balance is recording a cumulated deficit,
the population income stalled, some national economy branches recorded
stagnations, the consolidated budget became volatile due to reduced certain
incomes as a result of an adverse or unconcerned collection etc. Since 2013, the
situation recovered, as there is a better collection of incomes to the consolidated
budget. GDP/capita, calculated on the basis of the purchasing power parity
counted in 2013 for 10,759 units standard purchasing parity (the monetary unit
of reference at the level of the European Union, as conventional currency which
excludes the influences of the differences between the national prices). In 2014,
this synthetic indicator reached the level of possible 11,060 purchasing units.
Out of the data analysis we see, first of all, that for almost all the cases,
the quarter to quarter evolution is a relatively positive one, both in respect of
the comparison with the previous quarter and as against the corresponding
quarter of the previous year, emphasizing an increase at the level of EU28.
We notice that 7 countries recorded slight, insignificant increases.
The rest of the countries, including Romania as well, are still facing
in 2013 the effects of the crisis triggered by recession. In 2012, the growth
84
rhythms tempered, and make way for the interpretation of the crisis effects’
comeback, in the second wave. The problem of the Euro union, triggered
by the situation in Greece, Italy, Spain, Portugal and Ireland, stirring many
analyses that lead towards pessimistic perspectives.
Growth rates of GDP (seasonally adjusted data)
: Data not available.
(1) Percentage change compared with the same quarter of the previous year calculated from
non-seasonally adjusted data.
(2) Percentage change compared with the same quarter of the previous year calculated from
working-day adjusted data.
(3) Growth rates are calculated using the trend component.
(4) All growth rates based on ESA2010 methodology.
Note: The seasonal adjustment does not include a working-day correction for Ireland, Portugal,
Romania and Slovakia.
Data source: Eurostat.
If considering the European context, from the point of view of the
recession phenomenon the situation is showing that there is only one country,
i.e. Poland, managed to stay out of this condition, recording subsequent
increases. Other 14 EU member countries, among which Germany (the
engine of the European economy) with a noticeable increase in 2013-2014 too
have recorded increases. Some of them, such as France, Holland, Slovakia,
Denmark, have recorded consolidated increases during the two quarters, which
forecasts a positive evolution expected at the level of the entire year 2014.
Other countries, such as Belgium, Spain, Hungary, have marked
85
labile comings out at least for one of the two quarters, which denotes a certain
uncertainty for the end of 2013, the recession being any time possible. The
situation occurring in this respect implies certain discussions which, briefly,
may resume to the following basic aspects:
• The imports decreased comparatively to the previous year. The
imports kept on being profitable since, even if sometime an
appreciation of the national currency against Euro and USD has
been recorded, the wholesale and retail prices were not adjusted
by cutting-off, the companies considering that they are a gained
position which is not advisable to give up;
• On the other side, the exports increased during the period 2012
- 2014 and the first six months of the year 2015, because this
fluctuation of the exchange rate against the two currencies of the
foreign exchange panel of reference did stimulate the domestic
production for export, this becoming much more profitable
at export, the situation will maintain on the same trend in the
following period;
• Normally, one country deficit, including the Romania one, is not
alarming to the extent it makes part of a program of external loans,
directed by projects, which do not harm the national wealth;
• In the context of the imports propensity towards the consumer
goods, the situation can be evaluated as a negative one.
References
1. Anghel M.G. (2015). Analysis on the Indicators related to the structuring of the
Monetary Mass in Romania after the adhesion to the European Union, Romanian
Statistical Review - Supplement/No. 6, pg. 26 – 33
2. Anghel M.G. (2014). Econometric Model Applied in the Analysis of the Correlation
between Some of the Macroeconomic Variables, Romanian Statistical Review Supplement/No. 1, pg. 88 – 94
3. Anghelache C. (2015). România 2015. Starea economică în continuă creştere,
Editura Economică
4. Anghelache C., Manole A., Anghel M.G. (2015). Macroeconomic Evolutions in
Romania by ohe End of The Year 2014, Romanian Statistical Review - Supplement,
No. 2, pg. 46 – 54
5. Anghelache C., Anghelache G.V., Anghel M.G. (2015). The monetary evolution,
placements and resources, Romanian Statistical Review – Supplement, No. 4, pp.
72-80
7. Anghelache C., Manole A., Anghel M.G. (2014). The business environment and the
foreign investment, Romanian Statistical Review - Supplement/No. 10, pg. 7 – 14
8. *** www.insse.ro – official site of the National Institute of Statistics of Romania
86
The Gross Domestic Product evolution in
Romania
Prof. Sebastian KOT, PhD
Georgiana NIŢĂ PhD. Student
Abstract
In this paper, the authors study the characteristics of the Romanian
economy from the viewpoint of the Gross Domestic Product. The analysis
focuses on the overall evolution of the indicator, especially during the interval
2015-July 2015, the modification by categories of resources and utilizations.
Key words: GDP, utilizations, resources, interval, growth, value
As to the GDP evolution comparatively with the corresponding
periods of the year 2009, in the case of Romania it is resulting, first of all, that
the decrease of -1.3 compared to 2009 was reasonable. In 2012, an increase of
GDP by some 1.1% was recorded. GDP recorded in 2010 a value of 522,561.1
million lei, reaching 578,551.9 million lei in 2011, and 596,681.5 million lei,
definitive, deflated data, in 2012. The GDP value in 2013 increased by 3.5%,
reaching 617,565.4 million lei, in the context of the crisis which, on both
internal and international plan, continued to affect the economic evolution.
Meantime, in 2014, the GDP reached a value of 634,857.2 million lei while,
by 30.06.2015, the GDP value counted for 351,281.2 million lei.
Quarterly GDP evolution, over the period 2013-2015
1st quarter 2nd quarter 3rd quarter 4th quarter
In % as against the corresponding period of the previous year
2013
102.1
101.4
104.2
105.2
Raw series
2014
104.1
101.5
103.0
102.7
2015
104.3
103.3
2013
101.9
102.0
104.0
104.7
Seasonally
2014
103.7
102.4
102.9
102.6
adjusted series 2015
103.8
103.7
In % as against the previous quarter
2013
101.1
101.5
100.8
101.2
Seasonally
2014
100.2
100.2
101.3
100.9
adjusted series 2015
101.4
100.1
-
Year
103.4
102.8
-
Data source: National Institute of Statistics, press release no. 249/07.10.2015.
87
Over the period 2001-2008 GDP progressed in leaps, recording
positive evolutions. Starting in 2009, under the influence of the economicfinancial crisis, the decrease of the economic growth triggered.
The GDP evolution over the period 2001- 2015*
(The corresponding period of the previous year = 100)
*)
provisional data, estimate for 2015
Data source: National Institute of Statistics, press release no. 249/07.10.2015.
By comparing the Romania GDP increase level in 2012 with some
other countries out of the European Union, we shall see that it counts as almost
the lowest.
The analysis will get a more significant outline if we follow the way
in which the GDP developed in 2014 and the first two quarters of 2015.
Thus, in Q1 2015 GDP grew by 4,3% as gross series (3,8% adjusted
series) and in the second trimester grew by 3,8% gross series (3,4% adjusted
series) as against the corresponding quarter of the previous year.
88
The GDP evolution – seasonally adjusted
Data source: National Institute of Statistics, Press release no. 249/07.10.2015.
For the first semester of 2015, the total volume of GDP, in current
prices, counted 351,181.2 million lei.
The GDP alteration factors by categories of resources
In 2014, as well as during the first six months of 2015, the GDP has
been achieved on the account of the activity carried out in the frame of the
main branches of the national economy.
The contribution differed from the point of view of the gross added
value recorded at the level of each branch.
The net taxes on product brought in the first semester of 2015 a positive
contribution, representing some 12.9% out of the GDP, the constructions
increased by 0.2%, while the industry recorded an increase of 0.2%.
Also, in 2014 the contribution of the agriculture, forestry and fish
breeding was reduced, and during the first six months of 2015 they represented
2,4% of the GDP.
89
Contributions to the GDP evolution, by categories of resources 2015 /
2014 (first semester)
In 2015, for the first six months, the same trends persisted, with the
mention that agriculture marked a slight recoil.
For the first half of the year 2015, there is slight increase to be noted
for the economy evolution.
The agriculture kept on maintaining within normal parameters of
influence, recording a slim evolution.
Contribution of the main categories of resources to GDP increase during
the first six months of 2015 (%)
Indicator
Sem. I
Gross Domestic Product
3.4
Agriculture, forestry and fish breeding.
-0,1
Industry, including energy
0,7
Constructions
-0,2
Trade, cars and household appliances repairs; hotels
and restaurants, telecommunications
1.1
Financial, real estate, renting and services to
companies activities
0,2
Other services activities
0,1
Total gross added value
2.8
Net taxes on product
0,9
90
Relevant as regards the GDP forming by categories of resources (as
alteration factors) is also the structural evolution during the period 2003-2015
which is described by the following table.
Weight of the main categories of resources to the GDP forming (%)
Indicator
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015*
Agriculture, hunting,
forestry, fishing and fish 11,6
breeding.
Industry, including
24,7
energy
Constructions
5,7
Trade, cars and
household appliances
repairs; hotels
20,3
and restaurants,
telecommunications
Financial, real estate,
renting and services to
companies activities
12,3
12,6
8,4
7,8
5,8
6,7
6,3
6,0
7,3
4,7
4,8
3,0
2.4
24,9 24,8 24,5 24,3 22,9 23,8 26,4 25,0 25,2 25,1 32,5 23.1
5,9
6,5
7,4
9,1 10,6
9,8
7,3
7,2
7,3
7,4
4,8
4.3
20,6 21,7 22,2 22,7 21,9 21,2 20,9 21,2 20,9 21,0 15,6 18.0
12,3 13,2 13,3 13,7 14,0 15,1 16,2 14,1 14,0 13,2 16,9 13.4
Other services activities 14,3
13,0 13,7 13,1 13,0 13,0 13,8 12,0 12,8 12,8 12,7 14,0 14.0
Net taxes on product
10,7 11,7 11,7 11,4 10,9 10,0 11,2 12,4 13,9 10,8 13,2 12.9
11,1
*) provisional, estimate data
The activities carried out by services, industry, constructions and the net
taxes on product, together, brought in a decisive contribution to the GDP decrease,
which means a negative feature for the Romanian economy which, although
restructured gave up a number of industrial sub-branches committing itself on
the way of developing the services production, constructions and so on, but failing
to cope with the effects of the crisis, correlated also with the non-existence of an
appropriate governing plan, set up at the beginning of the phenomenon.
The weight of the main categories of resources in the formation of
GDP in the first half of 2015 reveals that the industry keeps on remaining on
the first place, having a slight trend of increase as against the corresponding
period of the previous year.
The GDP evolution by categories of utilizations
From the point of view of the utilizations in the GDP forming during
the year 2013, there have contributed: the stocks variation, the net export, the
gross forming of fixed capital, the final collective consumption of the public
administration, the final individual consumption of the households.
91
When analyzing the data available for 2014, we have to consider as
starting point the actual situation being recorded by our country during this year.
Thus, for instance, the stocks variations recorded a lower contribution, while the
net export, namely the difference between exports and imports, recorded a more
reduced effect, following the reduction of the deficit of the foreign trade balance.
Under such circumstances, we find out that, from the point of view
of the utilizations, the GDP formation has been achieved by the contribution
of the following factors: gross forming of the fixed capital, final individual
consumption of population with a decrease of -0.4%, which implies the following
conclusions:
• From the point of view of utilizations, positive influences on the GDP
achievement have been recorded by the final collective consumption
of the public administration, stocks variation and net exports;
• Negative influences on the GDP forming have been recorded by the by
the final individual consumption of households, and the gross forming
of fixed capital.
The analysis of the influence factors of the GDP forming by categories
of utilizations may be emphasized by the analysis of rhythm at which, the
categories of utilizations considered for the GDP achievement have influenced
this achievement in 2014 comparatively with 2013.
The same tendency is stated out also for the first six months of the
year 2015. Thus, the individual consumption of households and the collective
consumption of the public administration, together, have been reduced.
A more marked decrease, has been recorded by the net export. Another
negative effect has been recorded by the rhythm of increasing of the gross forming
of fixed capital.
The weight of the main categories of utilizations in GDP
Indicator
Actual individual
consumption of the
households
Actual collective
consumption of the
public administration
Capital gross forming
Stocks variations
Net export
Year
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015*
75.7 77.5
78.5
77.9
75.3
74.0
72.7
8.3
7.7
7.6
7.7
8.2
21.5 21.8 23.7 25.6 30.2 31.9
0.6 1.8 -0.3
0.9
0.8 -0.6
-7.6 -9,0 -10.2 -12.1 -13.9 -13.0
25.6
-0.6
-5.9
9.8
7.9
72.6 72.4 72.7
7.1
7.3
7.1
22.5 22.3 22.2
3.5 3.9 4.1
-5.7 -5.9 -5.7
73.7 74.0 72.2
6.1 8.8
7.2
20.0 19.0 21.4
3.5 1.1 -0.9
-3.3 -0.5 0.1
*) provisional estimated data
Data source: National Institute of Statistics. Press release no. 249/07.10.2015.
The GDP evolution during 2014 follows the line of going on the
recovery road from the process of recession. During the first six months of the
92
year 2013, the “un-accounted” negative effects of the year 2010-2012 have
been taken over and then being followed by a slight increase, maintained in
2013-2014 and during the first six months of the year 2015. Thus, the GDP
has not yet reached the level recorded in 2009; most of the branches recorded
negative contributions, which implies the entrance into a macroeconomic
managerial mess; the structure by branches and utilizations has been negative.
In 2012, GDP grew by 1.1% as against 2011 and follows an oscillatory course
in 2013, recording, during the first six months of the year an increase of 1.8%
as against the same period of the previous year. The growth kept on being
maintained in 2014 and the first six months of the year 2015.
The survey on the economic evolution, considering the modifications
of the GDP in the European Union countries, emphasizes the extremely critical
situation existing on the European and, at a larger extent, international plan.
The achievement of the Gross Domestic Product by ownership forms
Out of the performed analysis, it results that for the period 20092014, the private sector contributed with 72.4%-76.9% to the GDP forming.
The weight of the private sector, still low, has been generated mainly by the
gross added value in the agriculture. Such an influence is a normal one if to
consider that the agriculture has to face negative natural conditions.
If comparing the weight of the private sector in the GDP achievement
with the figures recorded for the previous periods, we find out that this weight
is superior to all the periods being analyzed as from the year 2000, even as
from the year 1990, up to date.
In 2010-2014, for which we are actually performing a complete
analysis, we find that the weight of the private sector in the gross added value
increased as for the constructions field.
What is really important is the fact that the weight of the private sector
in the achievement of the gross added value by branches of the national economy
and, eventually, to the GDP forming, kept on maintaining at a high level.
93
Gross Domestic Product weight of the private sector in 2001 – 2015
*)
Semi-final data. **) Estimate data.
Data source: National Institute of Statistics, Statistical Bulletin no. 7/2015.
It is obvious that the privatization of other administrations or extending
the privatization at the level of branches already privatized will have the
targeted effect.
Here we have to underline the fact that such an analysis is not always
pertinent since there will be and remain sectors of activity absolutely important
for the national economy for which the state must keep its attributes of sole
owner.
References
1. Anghel M.G. (2015). Monedă. Teorie şi studii de caz, Editura Artifex, Bucureşti
Editura Economică
3. Anghelache C., Anghel M.G., Manole A. (2015). Modelare economică, financiarbancară şi informatică, Editura Artifex, Bucureşti
4. Anghelache C., Anghel M.G. (2015). GDP Analysis Methods through the Use of
Statistical – Econometric Models, Economica, Volume 7, No. 1 (91), martie 2015,
pg. 124 – 130
94
5. Anghelache C., Manole A., Anghel M.G. (2015). Analysis of final consumption and
gross investment influence on GDP – multiple linear regression model, Theoretical
and Applied Economics, No. 3/2015 (604), Autumn, pg 137-142
6. Anghelache C., Manole A., Anghel M.G. (2015). The analysis of the correlation
between GDP, private and public consumption through multiple regression,
Romanian Statistical Review - Supplement, No. 8, pg. 34 – 40
7. Anghelache C., Manole A., Anghel M.G. (2014). Analysis on the Gross Domestic
Product Evolution, Romanian Statistical Review - Supplement/No. 4, pg. 7 – 15
8. Anghelache C., Anghel M.G., Sacală Cristina (2014). The Gross Domestic Product
Evolution, Romanian Statistical Review - Supplement, No. 12, pg. 12 – 20,
95
The Inflation (Consumer Prices) in the
Romanian Economy
Bucharest University of Economic Studies, “Artifex” University of Bucharest
Georgiana NIŢĂ PhD. Student
Abstract
This paper describes the effects that the inflation phenomenon
exerted on the Romanian economy during the recent period. The valuation
of the inflation magnitude is based on the characteristics of consumer price
evolution, measured through appropriate indices,on the overall recordset, but
also on groups of products/goods and services.
Key words: inflation, consumer prices, indices, income, evolution.
An important element to consider when evaluating the economic
evolution of a country over a period of time consists of the way the consumer
prices developed, both on an overall basis and by groups of goods and services,
as well as of the dual comparison with the planned, forecasted target and the
outcomes of the previous year.
In the context of the steady concern as regards the adjustment of the
system of the income collecting, based on the unique quota of taxation, as
well as bringing the Fiscal Code to the level of correlative terms, in line with
the actual situation of the country, in 2010-2015 there are a number of events
occurring and worth to be outlined.
First of all, the discussions between the Romanian Government and
the I.M.F., have been finalized and the installments out of the granted credit
were allocated. Practically, all of them, over 20 billion euro, were integrally
transferred in 2011.
There have been a number of elements which the I.M.F., intransigent
and willing to see a market economy in action, did not agree with. Thus,
for instance, there have been many concerns in respect of how to convince
the I.M.F. to agree with a higher deficit of GDP or to keep on accepting the
situation of having certain subsidies at the level of the national economy.
The second essential phenomenon of the years 2010-2015 is given
by the divergent evolution between the consumer price index, as an overall
and in structure, in comparison with the evolution and appreciation of the
96
national currency, the new leu, against the two currencies which are forming
the foreign exchange basket, respectively Euro and USD.
Since a couple of years, as a consequence of the policy run by the
National Bank of Romania, which undertook the responsibility of targeting
and fixing the inflation at certain levels, the foreign exchange evolution of the
national currency followed a trajectory which, from a economic and financial
point of view, proved to be a positive one but, meantime, generated a negative
effect on the Romanian exports, or for those working abroad and those living
in the country, being meantime non-conform with the actual economical
situation of the country.
On this back-ground, during the period 2010-2015, we register also
time intervals of slight appreciation of the national currency, in contradiction
with the increase of the inflation rate, both on an overall basis and in structure
by goods and services. We can outline two contradictory evolutions which
we could identify from this point of view. On the one side, the increase of
the consumption propensity of the population and, hence, the imperative
requirement for steps meant to stop this tendency. Thus, at a first stage, the
interests for the population deposits have been reduced after which, in order
to improve the attractiveness of saving, they have been increased again
aiming a sole purpose, respectively tempering the population propensity to
consumption.
The austerity steps being taken have stopped, in a natural way, the
population consumption with immediate effect on the economic growth and
deterioration of the standard of life. The revival measures of salaries and
pensions, but also other social attempts, did not succeeded to improve, upon
expectations, the incomes and subsequently the quality of life. On the other
hand, in its concern as to targeting the inflation, the National Bank aimed to
implement and control, permanently, the evolution of the foreign exchange
rate, consequently the position of the national currency against the two foreign
currencies – euro and dollar. Another typical element is given by the steady
concern of the Executive and, mainly, of the National Bank, to observe the
goals declared as regards the inflation targeting. Despite all steps being taken
targeting slipped out of an actual control, lining up outside the forecasts, from
2010 until June 2012.
97
Price increase in July 2015
– Percent –
Increase of consumption
prices in July 2015, as
against:
June December July
2015
2014
2014
-0,2
-1.4
-1.7
-1.2
-7.1
-7.3
-0.5
2,1
1.3
0,1
1,3
2.2
Indicators
Monthly average increase of
consumption prices during the
period
1.I- 31.VII.
1.I- 31.VII. 2015
2014
0,2
-0,2
0,0
-1.0
0,3
0,3
0,2
0,2
Total
Foodstuff *)
Non-foodstuff
Services
*) Including beverage.
After 2013, a reverse trend of inflation evolution begins, kept under
control. Among the non-foodstuffs recording a high average increase there
are the natural gas, the thermo energy, tobacco and cigarettes, electric energy,
water – sewage – sanitation, hygiene and cosmetics, postal services, interurban transport.
Consumption price indexes, 2001-2015
- December previous year= 100 135
-%130
125
130,3
117,8
120
115
110
105
114,1
109,3
108,6
104,9
106,6
109
107,9
104,7
104,2
105,33104,5
101,7
100,2
20
01
20
02
20
03
20
04
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12
20
13
20
14
20
15
100
*)
Provisional data.
A comparative survey on the annual average inflation in the EU
member countries during the period 2010-2015 shows that, along with
Hungary, Romania was recording a high level of the inflation annual average
level.
98
The annual average rate of inflation at the EU level,
in 2014* measured on the harmonized indices basis (IAPC)
*)
Provisional data
Data source Eurostat.
At this point, there are a lot of other comments to be done but for a
synthetic picture of the consumer price index we are holding present analysis
only.
References
1. Anghel, M.G. (2015). The Inflation (Consumer Price) Evolution, Romanian
Statistical Review - Supplement/No. 1, International Symposium „Programs for
Romania’s Economic Recovery in the Horizon 2020 Perspective”, pg. 128 – 132
2. Anghel M.G. (2014). Econometric Model Applied in the Analysis of the Correlation
between Some of the Macroeconomic Variables, Romanian Statistical Review,
Supplement/No. 1
Editura Economică
4. Anghelache, C. (2014) – „Romania 2014. Starea economică pe calea redresării”,
Editura Economică, Bucureşti
5. Anghelache, C.; Gheorghe, M.; Voineagu, V. (2013). Metode şi modele de măsurare
şi analiză a inflaţiei, Editura Economică, Bucureşti
6. Karanassou, M., Snower, D. (2007). Inflation Persistence and the Philips Curve
Revisited, Kiel Institute for the World Economy in Kiel Working Papers series
7. www.insse.ro – official site of the National Institute of Statistics of Romania
99
The main aspects regarding the dynamics of the
Industrial Production Indices
Abstract
In this paper, the authors valuate the industrial production indices,
measured for the Romanian economy. The rhythms of increase in the industrial
field diminished and were different so that as against the increase recorded by
the manufacturing industry, the decreases recorded by the extractive industry
and the electric and thermo-energy, gas and water sector should be underlined;
however, there have been increases for certain categories, such as the industry
of durable goods, recording an increase of the industry of capital goods with,
the industry of current usage goods with increase.
Key words: Industry, indice, production, manufacturing, branch
In 2010-2015 the industrial production indices are reflecting a slight
increase as comparatively to the similar periods of the previous year, being
largely influenced by the restructuring of the extractive sector, as well as by
the decrease recorded at the level of the lohn production, which generated a
slower rhythm of development at the level of the manufacturing industry.
The fact that these other activities or branches had small weights
within the total industrial activity from our country is to be noted.
However, there are several other branches which recorded diminished
indices, such as: textile production, clothes, shoes and leather articles
production, rubber and plastics products, production of building materials and
other non-metallic minerals and production of equipments and machinery.
The first three categories, i.e. textile production, clothes, shoes and
leather articles carried out their activity in the form of lohn production and
recorded a tempering rhythm which might generate effects during the periods
to come as well.
On an overall basis, the industry kept on remaining on a positive
position, meaning that it recorded a slight increase of the contribution to the
GDP achievement in 2014 and 2015.
100
The privatization process could lead within the forthcoming periods
to new decreases, both for the rhythm and the volume of the production of
certain branches, but also of the total contribution which the industry might
bring to the GDP achievement, by the obtained gross added value.
The labor productivity by an employee in the industrial field followed
a slow increase trend over the period 2010-2015. In the first seven months of
2015, the resources of prime energy increased.
The evolution of the industrial production represents the sectors which
have positively marked the evolution process during the period 2009-2015.
This characteristic is specific to the manufacturing industry which, by
the over-helming weight hold in the total industry production generated the same
trajectory to the entire industrial production. This is significant, despite the fact that
the production of electric and thermo energy, is following a practically opposite
trend while the trend of the extractive industry is recording a flat evolution.
The data show a fluctuating evolution of the production volume,
compared to previous periods. The decrease is stronger in the extractive
industry as well, the manufacturing industry and electric and thermo energy,
recording decreases.
The decreases have been stronger at the level of the large industrial
groups, structured upon the goods destination. Here we have to mention the
marked decrease of the production of capital goods and by almost a quarter
for the production of intermediary goods. The decreases recorded by the
import and the export of intermediary goods are going to jeopardize this sector
production which is already a confirmed fact by the recorded decreases.
The biggest decreases being recorded during the first seven months of
2015, comparatively with the corresponding months of the previous year are
shown up by the metallurgical industry
A similar trend of a significant magnitude is stated out in the case of
the production of auto-vehicles for road transportation from a relatively slight
reduction in October to a marked decrease December.
A similar trend of a significant magnitude is stated out in the case of
the production of auto-vehicles for road transportation from a relatively slight
reduction in October to a marked decrease December.
In July 2015, the prices of the industrial production increased by
1.03% as comparatively to the corresponding month of the previous year, on
an overall basis.
The industrial production is one of the few sectors which are marking
a certain recovery at the EU level and that of many of the EU member states.
The evolution of the Romania industrial production is also included in this
allegation.
101
Certainly, the positive evolution at the EU level is marked by the
evolution of the industrial production in Germany, France, Italy and other
countries, among which Ireland, Hungary, Denmark, Holland, with a relatively
smaller weight.
For 2014 and 2015, Romania recorded increases of the industrial
production computed as seasonally adjusted series, atrend which was
maintained in the first seven months of 2015 also.
If considering the distribution by large groups, the increase of the
industrial production in Romania has been more marked for the group of the
capital goods industry and significantly lower for the group of the current usage
goods. From the point of view of the industrial production increase recorded
by Romania in 2013 and 2014, it can be seen that the production has been
significantly higher as against the corresponding period of the previous year.
During the first seven months of 2015, overall, industry recorded a growth by
3.3%. It is worthy to note that the increase recorded by Romania in September
2010 is higher comparatively with all the other European states which, most
of them, excepting Poland, Slovenia and Holland, recorded decreases, in same
cases quite significant.
The situation keeps on being more or less the same in January 2011,
comparatively with 2010 when the increase recorded by Romania is exceeded
by Poland and Czech Republic only. In 2012, 2013 and 2014 the industry
followed a slightly growing trend.
However, these increases by groups have been counter-weighted the
decreases recorded by the groups of current usage goods and durable goods.
The indices of the industrial production, in 2010-2014 comparatively
with 2009 and 2008, are showing, both on the overall and for the extractive
and manufacturing industries branches, for all quarters as far as the first one is
concerned.
The indices for the first half of 2015 have manifested increases in all
months. The manufacturing industry, electric energy, gas and water, as major
sections, on one side and the capital goods industry, the intermediary goods
industry and the power industry on the other side, with important increases are
responsible for the mentioned increases at the industry level.
The industrial production indices, as adjusted series, are also indicating
a positive trend, although is circumscribed within increase levels relatively
modest as to the overall industry, but nevertheless significant for the subbranch electric and thermo energy, gas and water and, respectively, the capital
goods industry.
The value indices of the new orders marked decreases in 2009, 2010,
2011 and 2012, and during the period 2013-2015 the trend reversed. In 2014
102
comparatively with 2013, the situation changed in respect of the new orders
within the manufacturing industry from Romania, mainly in the situation of
the manufacturing industry working on orders basis.
Significant increases have been recorded in the case of orders value
index for the manufacturing of chemical substances and products group,
metallurgical industry, and manufacturing of road conveyance auto-vehicles,
out of which absolutely remarkable was the increase recorded for the external
market, as a consequence of the well known evolution of the Dacia cars
exports to west-European countries, mainly to Germany. The actual supplies
of goods established on the basis of the turnover indices are reflecting an
uncertainty tendency as regards the producers’ capability to capitalize the
achieved production, on one hand and the payment difficulties of the buyers,
on the other hand.
The labor productivity increased yearly over the period 2002 – July
2015, simultaneously with the decrease of the occupied population, so that
during the first seven months of 2015, the productivity of labor grew by 2.7%
(provisional data) per total industry.
References
Editura Economică
3. Anghelache, C. (coord.) (2014). Statistical-Econometric Models Used To Study
December 2014
4. Anghelache C., Mitruţ C., Voineagu V. (2013). Statistică macroeconomică:
Sistemul Conturilor Naţionale, Editura Economică, Bucureşti
5. Anghelache, C. (2009). Indicatori macroeconomici utilizaţi în comparabilitatea
internaţională, Conferinţa a 57-a „Statistica – trecut, prezent şi viitor”, ISBN 97890-73592-29-2, Durban, articol cotat ISI
6. Anghelache C. (2008). Tratat de statistică teoretică şi economică, Editura
103
Production of Services during the Last Year
Prof. Radu Titus MARINESCU PhD.
Abstract
This paper is dedicated to the analysis of services delivered for the
population, as reflected in the data relevant for the Romanian economy.
Among the aspects analyzed, there can be observed: the weight of services
in GDP, the structure of service indicators, electronic commerce and sales by
correspondence, tourism.
Key words: production, services, influences, sales, tourism
Comparatively to the previous year (2008), during the period 20092015, the indices of the market services supplied to the population, as well as
the indices of the retail trade have recorded a decrease.
This decrease is generated by the domestic demand (an element of
the GDP utilization), which recorded an underlined decrease generated by the
income reduction.
Meantime, the services reached a signifncant weight in the GDP, out
of which the retail trade only recorded a slow rhythm.
The services contribution to the GDP achievement by the gross added
value achieved in the frame of this sector means a positive development which,
at this stage of the integration, means a lot for Romania.
Out of the analysis of the structure of the carried out services activities,
we note the fact that the retail trade recorded diminished rhythm of increase.
Although the activity of sales by correspondence and virtual shops
get developed and permanent, this type of trade recorded decrease of 2.9% as
against the year 2009.
But, as from 2012 on, the services production showed a significant
increase.
The dynamics of the services carried out to the population has been
supported mainly by the activity of hotels and restaurants, which recorded no
increase.
Briefly, 2014 is a significant year as far as the production of services
is concerned, by the following major guide marks:
104
• The increase of the weight services hold as for the GDP achievement;
• The structural balancing of the services carried out to the
population;
• Differentiated decreases, for certain fields as already mentioned,
quite significant, of the production of services spread on various
zones, reflecting in fact the cynical effects of the economic crisis;
• The employment of a large number of persons in activities of carried
out services, which tendency should mark a similar evolution during
the forthcoming period;
• Maintaining the quality of the services carried out to the
population;
• Diminishing of the hotel activity;
• Exceeding a high weight of the contribution which the production of
services brings to the GDP achievement;
• There has been a development of the financial and banking services
as well as of capital market service, including thus a series of
employees.
Another aspect concerning the analysis in the field of the production of
services in our country is given by the volume of the turnover figure achieved
by the wholesale and retail trade of auto-vehicles, the retail trade with fuel etc.
As comparatively the year 2010 in 2011 this field of activity recorded
a significant decrease, continued in 2012.
Here we have another element which denoted a negative evolution of
the activity run in the field of the production of services in our country.
Generally speaking, the turnover figure decreased for all sectors of
activity, as a consequence of the alarming cut off of the population income.
In 2013, a slight up warding trend is noticed, which becomes more stressed
during 2014 and the first six months of 2015.
As for the international tourism, the period 2010-2015 shows that the
number of foreign visitors coming in Romania was low as against the touristic
capacity of the country.
Basically, the persons having friendship or kinship connections in
Romania kept on visiting them.
In this respect, the most numerous visits have been paid by citizen
from Germany, United States, Israel, France, Republic of Moldova as well as
from other countries where there is a significant number of Romanian natives.
During the year we are analyzing, the departures of the Romanian
visitors abroad decreased as comparatively with 2009.
The negative rhythm of departures has accentuated in 2011, 2012 and
2013, with a slight increasing course in 2014 and 2015.
105
In 2014 and 2015 we notice a positive trend in this field as well.
Comparatively with the corresponding month of the previous year,
in July 2015 both the arrivals and benighted to the structures of touristic
accommodation functions recorded increases of 24.8%, respectively 20.8%.
In comparison with the month of July 2014, in July 2015 the border
checking points recorded increases for both the arrivals of the foreign visitors,
by 13.3% and the departures abroad of the Romanian visitors, by 14.8%.
The arrivals registered in the structures of touristic reception for July
2015 amounted 1281.3 thousand, meaning an increase of 24.8% as against
those of July 2014.
Out of the total number of arrivals, the Romanian tourists in the
structures of touristic reception with accommodation functions represented in
July 2015 79.8%, while the foreign tourists represented 20.2%, these weights
being close to the ones recorded for July 2014.
As for the arrivals of foreign tourists in the structures of touristic
reception, the highest weight has been held by those coming from France
(74.4% of the total of foreign tourists), while the tourists coming from European
Union countries held 86.3% of the total. The benighted records in the structures
of touristic reception for July 2015 counted for 3606.4 thousand, evidencing n
increase of 20.8% as comparatively with the month of July 2014.
Out of the total number of benighted those of the Romanian tourists
arrived in the structures of touristic reception with accommodation functions
counted for 85.5% in July 2015, while the benighted of the foreign tourists
counted for 14.4% for the same period.
As far as the benighted of the foreign tourists in the structures of
touristic reception, the bigger weight went to those coming from Europe
(73.7% of the total foreign tourists) and out of these ones 83.7% were coming
from European Union countries.
The average duration of the sojourn in July 2015 was of 3.0 days for
the Romanian tourists and 2.0 days for the foreign tourists.
The index of the net utilization of the accommodation places in July
2015 counted for 38.8% of the total structures of touristic accommodation,
increasing by 4.1 percentage points as against the month of July 2014.
Higher indices of utilization for the accommodation places in July
2015 have been recorded by hotels (48.6%), touristic villas (32.1%), pupils
and children under school age camps (31.4%), touristic halting places (31.0%),
hostels (29.6%) and holyday villages (29.0%).
The arrivals of the foreign tourists in Romania, registered with the
border points, in July 2015, counted for 1143.2 thousand, with an increase of
13.3% as against July 2014.
106
The majority of the foreign visitors originate in countries placed in
Europe (92.6%). Out of the total arrivals of the foreign tourists in Romania,
55.3% originate in the European Union countries.
Out of the European Union states, the most arrivals have been registered
from Hungary (28.6%), Bulgaria (24.1%), Poland (11.7%), Germany (7.9%),
Italy (4.9%) and Austria (3.8%).
The departures of the Romanian visitors abroad, registered at the
border points, in July 2015, counted for 1363.1 thousand, increasing by 14.8%
comparatively with July 2014.
The road conveyance means have been the most utilized for departures
abroad, representing 81.0% of the total departures.
The arrivals registered in the structures of touristic reception during
the period 1.01-31.07.2015 amounted 5308.9 thousand, increasing by 16.0%
as against those of the period 1.01-31.07.2014.
Out of the total number of arrivals, the arrivals of the Romanian
tourists in the structures of touristic reception with accommodation functions
represented, during the period 1.01-31.07.2015, 76.7%, while the foreign
tourists represented 23.2%, these weights being close to the ones of the period
1.01-31.07.2014.
As far as the arrivals of the foreign tourists in the structures of touristic
reception, the bigger weight has been held by those originating in Europe
(74.8% of the total foreign tourists), out of which 85.2% were coming from
European Union countries.
Arrivals and benighted in structures of touristic reception with
accommodation functions – period 1.01 – 31.07.2015
Arrivals
Benighted
Period 1.01Period 1.0131.07.2015
31.07.2015
Period 1.01- Period 1.01Period 1.01- Period 1.01as against
as against
31.07.2014 31.07.2015
31.07.2014 31.07.2015
Period 1.01Period 1.01-thousand- -thousand-thousand- -thousand31.07.2014
31.07.2014
(%)
(%)
Total
4540.5
5305.9
116.9
10562.2
12073.2
116.2
Romanian tourists 3491.8
4079.3
116.7
8456.3
9512.1
115.6
Foreign tourists
1048.7
1085.6
117.8
2075.8
2461.1
118.6
off which:
-Europe
815.2
924.1
113.4
1980.8
1792.3
113.4
- European Union
696.1
787.6
113.1
1328.8
1496.0
112.6
- Asia
118.1
178.9
147.2
286.0
363.6
142.0
-North America
73.6
85.0
115.5
135.9
168.4
123.9
-South America
8.6
9.2
107.0
17.9
19.0
106.1
- Africa
10.2
9.7
95.1
43.2
59.7
138.2
Data source: National Insitute of Statistics, Press release 249/07.10.2015.
The benighted registered with the structures of touristic reception
107
during the period 1.01-31.07.2015, counted for 12273.2 thousand, increasing
by 16.2% as against those registered for the period 1.01-31.07.2014.
Out of the total number of benighted, the benighted of the Romanian
tourists in the structures of touristic reception with accommodation functions
represented, during the period 1.01-31.07.2015, 79.9% while the foreign
tourists benighted represented 20.1%.
As far as the benighted of the foreign tourists in the structures of
touristic reception, the bigger weight has been held by those originating in
Europe (72.8% of the total foreign tourists), out of which 83.5% were coming
from European Union countries.
The average duration of the sojourn during the period 1.01-31.07.2015
was of 2.4 days for the Romanian tourists and 2.0 days for the foreign
tourists.
The index of the net utilization of the accommodation places during
the period 1.01-31.07.2015 counted for 26.8% of the total structures of touristic
accommodation, increasing by 2.8 percentage points as against the during the
period 1.01-31.07.2014.
Higher indices of utilization for the accommodation places
during the period 1.01-31.07.2015 have been recorded by hotels (33.6%),
accommodation facilities on ships board (22.6%), touristic villas (20.1%) and
hostels (19.6%).
The most arrivals of foreign tourists accommodated in the structures
of touristic reception with accommodation functions* originated in Germany
(142.1 thousand), Italy (115.8 thousand), Israel (114.7 thousand), France (76.8
thousand) USA 972.9 thousand)
The arrivals of the foreign tourists in Romania, registered with
the border points, during the period 1.01-31.07.2015, counted for 5077.7
thousand, with an increase of 10.8% as against during the period 1.0131.07.2014. The majority of the foreign visitors originate in countries placed
in Europe (92.8%). Out of the total arrivals of the foreign tourists in Romania,
57.4% originate in the European Union countries. Out of the European Union
states, the most arrivals have been registered from Hungary (31.9%), Bulgaria
(26.9%), Germany (8.3%), Poland (6.7%), Italy (6.0%) and Austria (3.5%).
The departures of the Romanian visitors abroad, registered at the
border points, during the period 1.01-31.07.2015, counted for 7598.9 thousand,
increasing by 11.7% comparatively with the period 1.01-31.07.2014.
The road conveyance means have been the most utilized for departures
abroad, representing 78.3% of the total number of departures.
References
1. Anghel M.G. (2014). Evoluţii în domeniul construcţiilor şi transporturilor, ART
108
ECO - Review of Economic Studies and Research, Vol. 5/No. 1, pg. 54-62, ISSN
2069 – 4024
Editura Economică
3. Anghelache, C. (2014). Romania 2014. Starea economică pe calea redresării,
4. Anghelache C., Anghel M.G. (2014). Serviciile turistice în România, ART ECO
- Review of Economic Studies and Research, Vol. 5/No. 2, pg. 90-95, ISSN 2069 –
4024
5. Anghelache, C. (coord.) (2014). Statistical-Econometric Models Used To Study
December 2014
6. Anghelache C., Anghel M.G., Bardaşu G., Popovici M. (2014). Evoluţia serviciilor
turistice în România, ART ECO - Review of Economic Studies and Research, Vol.
5/No. 4, pg. 176-191
7. Anghelache C., Fetcu A.E., Anghel M.G. (2012). Considerations Regarding the
Evolution of Tourism in the Last Decade, Revista Română de Statistică – Supliment
Trim II, pg. 265 – 270
8.*** www.insse.ro – official site of the National Institute of Statistics of Romania
109
Financial Inclusion, Focus on Romanian
Migrants and their Families
Olivia Georgiana NITA PhD. Student
Abstract
This paper aims to analyze financial inclusion of a particular segment
that struggles most: the unbanked migrants, focusing on Romanian migrants
and their families.
It’s a common understanding that financial inclusion plays an
important role in poverty reduction, economic development and growth, job
creation, innovation and infrastructure.
Banks have shown themselves unable or unwilling to provide relevant
services to this particular segment of people and are acting to push this
segment away from financial inclusion, indirectly condemn them to long-term
poverty through financial exclusion.
Keywords: financial inclusion, unbanked people, Romanian migrants,
migrants ‘families, banks, poverty, rural areas
Introduction
Financial exclusion was defined as “a process whereby people
encounter difficulties accessing and/or using financial services and products
in the mainstream market that are appropriate to their needs and enable them
to lead a normal social life in the society in which they belong”.1
This article is part of a wider research on migrants and their needs
with a focus on Romanian migrants and their families. First part of the article
presents studies about unbanked people at global level, financial services
available for them and solutions to financial exclusion. The second part of
the article concentrates on Romania, the country with the highest degree of
unbanked people from Europe.
1. L. Anderloni, E. Carluccio, “Access to Bank Accounts and Payment Services”, in Anderloni
L., Carluccio E. e Braga M., New Frontiers in Banking Services: Emerging Needs and Tailored
Products for Untapped Markets, Berlino, Springer Verlag, 2006.
110
Financial inclusion – global approach
According to World Bank the unbanked population in 2014 was 2
billion people – nearly one in three. The 2014 Global Findex database of the
World Bank shows that, globally, 38% of adults remain unbanked for a range
of psychological, cultural and educational reasons – but also due to the lack of
interest in this segment from traditional financial institutions (banks).
An absence of financial education, lack of awareness about financial
services, unaffordable products, high transaction costs, paper work involved,
travel distances, post-2009 distrust in the banking system – all these factors
contribute to financial exclusion. As a result, 20% of the unbanked adults
(over 400 million people) receive wages in cash and another 23% (440 million
people) receive payments for agricultural products in cash.
In Europe, according to European Commission, 30 million Europeans
above 18 do not have a bank account - roughly 7% of all EU consumers.
Among these unbanked adults, 6.4 millions are actually restricted or afraid
to ask for a bank account. The situation varies greatly across the EU, with
many Western and Northern EU countries showcasing over 90% account
holding among the adult population, while at the other extreme Romania has
the highest percentage of unbanked population.
Romania stands out with the highest percentage (39%1) of unbanked
population. In Romania, banks have approximately 5 million customers,
representing 61% of the country’s active population. According to the
National Bank of Romania, Romanians “own” 14.5 million bank cards, but
just 11 million are actively used. The number of cards increased when people
started to receive their wages in current accounts, not in cash. According to
Global Findex Database, only 61% of Romanians aged over 15 years have a
bank account, of which 46% have a debit card and 12% a credit card, again
Romania ranks last (Figure1)
1. World Bank, Global Findex (http://www.bancherul.ro/romania-este-codasa-europei-si-lagradul-de-bancarizare-avem-cele-mai-putine-conturi-bancare-si-carduri-de-debit;-poatepentru-ca-sunt-prea-scumpe--14972)
111
Percentage of Romania population over 15 years
Figure 1
(Source: World Data Bank (http://databank.worldbank.org/data/home.aspx)
In developed countries from Eurozone almost all adults have a bank
account (95% of respondents aged over 15 years), while 81% have a debit
card and 42% a credit card.
Migrants also prefer cash, for several reasons:
status, they need to proof their identity identify in order to open a
bank account, and because they migrate apparently just for a few
years, need just to send money to their families for financial support
and some of them are not legally registered in the foreign country,
the demand for banking account is low.
language, represents a barrier for migrants in adapting abroad and
request financial services (bank documents that should be filled in
foreign countries, talking with tellers in bank branches) and also the
lack of experience in using financial services in origin countries,
which makes migrants to be reluctant on banks’ services.
remittances, cash transfers from migrants to their families in origin
countries.
According to the study “Finacial inclusion and new means of payment”
conducted by European foundation for Financial Inclusion in May 2013,
migrants could use modern payment instruments but because of poverty, low
income and unstable employment they are forced to mange with cash. Migrants
have problems to store money securely and that’s why they usually carry cash.
The key industry drivers of financial inclusion growth are technology
and mobile money. Technology is playing a pivotal role in the process of
112
increasing financial inclusion by reducing the cost of delivery and increasing
bank’s efficiency and productivity.
Options to improve financial inclusion
Innovative technologies and attractive financial services to meet
unbanked people needs;
Increase financial literacy;
Extension and better affordability of traditional financial services,
including greater access to bank branches and lending;
Mobile money: facilitating remittances or payments;
Remittances contribute to economic and human development,
remittances increase household income and demand on financial
services, by making recipients more inclined to join the formal
financial sector.
Romania - case study on unbanked people
In a 2012 report1, Romania was identified as the country with the
lowest degree of unbanked population (Figure 2). At that time, 56% of the
Romanian population was banked, well below the Central and Eastern Europe
average, which was 80%.
Bank population in Central and Eastern Europe
Figure2
Average 80%
A special case is represented by the population from rural areas
(according to Romanian National Institute of Statistics, 46% of Romanian
population live in rural areas), where banks have fewer banking outlets
(branches, ATMs) and people consequently don’t have access to the financial
services offered by banks. The financial crisis in 2009 worsened the situation,
1 GFK Survey 2012, http://www.nocash.info.ro/desi-56-dintre-romani-au-relatie-cu-cel-putino-banca-romania-este-cea-mai-slab-bancarizata-tara-din-centrul-si-estul-europei/
113
as more bank branches closed. Where banks have kept branches open in rural
areas, the costs of banking services are perceived by many in the unbanked
population as being too high.
In Romania, large differences in wealth, opportunity, education, skills,
health are seen in many areas, and in the last decade they have intensified, particularly
in rural ones. Nearly 40.2% of Romanian population in 2014 was exposed to the
risk of poverty and social exclusion, given that, Romania occupied last place in EU,
even if at EU level 122 million people, 24.4% of the European population were in
this situation, according to data released by Eurostat, Oct 2015.1
In rural areas, incomes are relatively low compared to urban areas (for
the year 20
11-503 euro / rural household compared to 621 euro / urban
household). Revenue ratio represents 42% of total gross income/household
in rural areas, while salaries are around 26%, according to 2011 Romanian
Census, National Institute of Statistics.
Rural areas are affected by the lack or deficiency of infrastructure,
which has a negative impact on economic development and quality of life.
County and communal roads have a length of 67,298 km (10.6% of national
infrastructure modernized) of which 48% are paved and 29% of land (often
impassable in the rainy periods).2 Although the length of water distribution
networks and sewerage increased, the access to them remains low, only 13.6%
of rural settlements were connected to the water supply in the year 2012.3
With its large unbanked population, Romania presents the most
effective case study. The country ranks last in Europe regarding the usage
rate of mobile banking, less than a fifth of Romanians using mobile devices
to make payments, while the European average is close to 40%. The trend
is toward more banked people – but slowly. On the other hand, Romania is
the origination point of one of the largest migration flow in the EU – the
“inbound” (from abroad to Romania) remittance market accounts for over
92% of the total remittance market; the outbound market, from Romania top
other countries is comparatively insignificant at 8%.
2015 statistics indicate that an estimated 5 million Romanian
migrants4 work abroad. Of these, 68%5 remit money to their families in
1. http://www.realitatea.net/40prc-dintre-romani-expu-i-riscului-de-saracie-i-excluderesociala_1811392.html
2. Transport routes, National Institute of Statistics 2011
3. Utilities activities of local interest, National Institute of Statistics 2011
4. The Profit Foundation, 2015, http://www.masapresei.ro/cinci-milioane-de-romani-sunt-plecati-in-strainatate-unde-traiesc-si-muncesc-acestia/
5. According to 2015 TR Ltd surveys in main countries of remittance to Romania: Italy, Germany and Spain
114
Romania – leading to an estimated 3.4 million Romanians sending money
to their families in Romania. According to National Bank of Romania 2013
statistics, Romanians in Italy have sent to their families in the home country a
total amount of €925 million, followed by Romanians in Germany with €595
million, USA €460 million and Spain €393 million. According to Transfer
Rapid research, each sender remits money to roughly 1.525 beneficiaries,
meaning that 5.2 million people in Romania receive money from remittances.
Based on recent World Bank Global Findex Database, 39% of the 5.2 million
people receiving remittances are unbanked
Per World Bank statistics, Romania has been among the top inbound
remittance markets in the EU, with an average €6 billion annual remittances
sent over the past 10 years. 1 In 2013, Romania was on the third position in the
top of ECA countries at receiving remittances with $3.6 billion, after Ukraine
with $9.3 billion and Tajikistan with $4.1 billion. 2
According to the National Bank of Romania, remittances from
Romanians working abroad in the first nine months of 2015 reached 3.3 billion
EUR, slight increase of 148 million EUR over the same period in 2014 and
with 68 million more than in January- September of 2013.3 (Figure3)
Remittances from Romanian working abroad (Source: National Bank of
Romania and)
Figure 3
1. World Bank, Migration and Remittance Flows: Recent Trends and Outlook, 2013-2016
2. World Bank, Migration and Remittance Flows: Recent Trends and Outlook, 2013-2016
3. http://www.bancherul.ro/romanii-care-muncesc-in-strainatate-au-trimis-acasa-3,3-miliarde-euro-in-primele-noua-luni-din-2015,-mai-multi-decat-anul-trecut--15191
115
The identified problem on the market is that banks are not able
to offer appropriate services to a large segment of the population, leading to
significant number of unbanked individuals.
While banks have shown themselves unable or unwilling to provide
relevant services to this particular segment of people (usually poorer and
primarily in rural areas), at the same time banks are not pleased with lots of
unbanked people coming into their branches just to make cash withdrawals
over the counter – and are acting to push this segment away from financial
inclusion, indirectly condemning them to long-term poverty through financial
exclusion.
However, according to National Bank of Romania, Romanians prefer
cash, not saving money and all their cash goes into consumption. Romanian
Banks’ concern is in finding a way to address all the 19 million Romanians to
enter into a commercial relationship with a bank.
“Banks are trying to rebuild trust in the customer relationship, which
has been hit hard in recent years due to financial crisis” declared Radu Gratian
Ghetea, President of the Romanian Association of Banks.
Even if mobile money was another solution for increasing financial
inclusion, due to the presented data, Romania is not the case, being the last
country in Europe, by the financial inclusion degree and usage rate of mobile
banking.
Banks perceive the unbanked individuals receiving remittances at their
teller windows as bottlenecks to effective banking activity as these individuals
generally receive small amounts of money while occupying teller’s time (an
estimated 9 to 10 minutes of teller time for each unbanked remittance service).
In Romania, the company Transfer Rapid is known for developing
innovative technological services and products for Romanian migrants and
their families, now launching an innovative solution for unbanked migrant’s
families in the home country. This service, entitled TR3A (“Anyone,
Anywhere, Anytime”), seeks to provide access of unbanked individuals to
financial infrastructure, acting towards financial inclusion and a higher quality
of life for a vulnerable segment of society.
This solution symbolizes the connection bridge of these two apparently
irreconcilable market segments: banks and the unbanked population, by
offering an innovative cash withdrawal service from ATMs, without the need
of a credit card or a bank account.
Once adopted, the solution will offer a unique way for unbanked people
to access the bank networks, providing a valuable service while rebuilding
trust and touch-points between banks and the unbanked population – the
solution seeks to meet an increasing need of both the unbanked population in
116
Romania receiving remittances from abroad, as well as the need of banks to
reduce servicing costs at the teller window in the case of unbanked individuals
with perceived low Life Time Value (LTV) to the bank.
Conclusions
Financial inclusion of unbanked individuals into the financial system
via alternative, more cost-effective channels than those traditionally offered
by banks represents a long-term interest of all countries.
This article showed a proportion of the vulnerable unbanked segment,
represented by migrant families, but of course solutions have to be extended
to migrants working abroad and to all unbanked people.
Migrants and remittances should be favored in order to increase
development impact and financial inclusion. And for this to be possible a
series of actions should be held: financial education for migrants and their
families in origin countries, financial services and products customized on
their needs, innovative technologies in term of access to financial services,
consumer protection and cost reduction. In the same time, banks should
be open to new technologies provided to attract this particular segment of
population: unbanked people; improve tellers’ attitude towards unbanked
individuals when introduce services different from the basics ones.
References
1. Anghelache, C. (2015) – “România 2015. Starea economică în continua creştere”,
2. Anghelache, C. (2008) – “Tratat de statistică teoretică şi economică”, Editura
3. The World Bank, the Global Findex Database 2014, “Measuring Financial Inclusion
around the World”, by Asli Demirguc-Kunt, Leora Klapper, Dorothe Singer, Peter
Van Oudheusden, April 2015
4. The World Bank, “Migration and Remittance Flows: Recent Trends and Outlook,
2013-2016”, October 2013
5. The World Bank, “Financial Inclusion Strategies Reference Framework”, June 2012
6. IMF Working Paper, Statistics Department “Assessing Countries’ Financial
Inclusion Standing—A new Composite Index”, prepared by Goran Amidžić,
Alexander Massara, and André Mialou, authorized for distribution by Luca Errico,
February 2014
7. European Commission, “Financial inclusion: ensuring access to a basic bank
account. Consultation Document”, Brussels 2009
8. European Foundation for Financial Inclusion”Financial inclusion and new means
of payment. Qualitative review in five European countries”, May 2013
9. EMN Magazine, “Migrant’s financial inclusion in the EU:which role for the
microfinance sector?”, March 2013
10. “Migrants and financial services”, Luisa Anderloni, Daniela Vandone, University
of Milan
117
11. United nations conference on trade and development(UNCTAD), “Remittances
and Financial Inclusion”, February 2015, Mina Mashayekhi Head Trade
Negotiations & Commercial Diplomacy Branch DITC/UNCTAD
12. “Migration and Development in the policies of the European Union: trends toward
a cosmopolitan approach”, by Flavia Piperno, researcher CeSPI, November
2014
13. National Observatory for financial inclusion of migrants, “Good practices for
financial inclusion. A European overview”, June 2013
118
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site-ul www.revistadestatistica.ro, secţiunea „Procesul de recenzare”.
Conditions for the articles designated for the Romanian Statistical Review
The original scientific or technical works can be sent to be published either
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translation).
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ISSN 1018-046X
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COLEGIUL ŞTIINŢIFIC
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