Manajemen Bisnis

Modelling Financial Time Series and
Its Application as an Alert System
by:
Dr. Heri Kuswanto, M.Si
Department of Statistics- Laboratory of Economic, Financial Statistics and Actuarial Science
Institut Teknologi Sepuluh Nopember (ITS)- Indonesia
Presented at the Research Methodology Seminar- Prince Songkla University-Thailand 12 May 2016
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Brief. Introduction
Education:
• 2010
• 2009
Germany
• 2005
• 2003
Appointments:
• 2016
• 2016
• 2011-2015
• 2013- now
:Postdoctoral Research Associate at the Laval University Canada
:PhD at the Institute of Statistics, School of Economics and Management- Leibniz Hannover Univ.,
:MSc. in Statistics- ITS Indonesia
:BSc. in Statistics-ITS Indonesia
: Head of Postgraduate Study Program in Statistics-ITS Indonesia
: Coord. of Climate Change-Expertise at the Research Center for Earth, Disaster and Climate Change
: Deputy Head of the International Relation and Coopertaion Office-ITS
: Lecturer and Researcher at the Department of Statistics-ITS
Research Interest :
Financial Econometrics, Time series forecasting, (Extreme) weather and climate events, Risk Modelling
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Outline of the Presentation
1. Financial Time Series Models : Definition and scope
2. Developments in Financial Time Series Modelling
3. Some Applications for Indonesia’s case
• Alert System for Crisis Management Protocol
• Market Surveillence System
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What Experts says?
http://www.stern.nyu.edu/rengle/
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Another expert
www3.stat.sinica.edu.tw/statistica/J17N1/editorial_2.pdf
Journal of Business and Economic Statistics,
Journal of Financial Econometrics,
Journal of Applied Econometrics and the
Econometrics Journal
Etc.
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Scope of Financial Time Series
Financial
Theory
Time Series
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Financial time series concerns with
Theory and Practice of Asset Valuation
over time
What modelling financial time series for?
•
•
•
•
monitor price behaviour
to understand the probable development of the prices in the future
Many traders deal with the risks associated with changes in prices.
Forecasts of future standard deviations can provide up-to-date
indications of risk, which might be used to avoid unacceptable risks
perhaps by hedging.
Aas and Dimakos, Statistical modelling of financial
time series: An Introduction, Norwegian Computing
Center 2014.
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Stylize Fact about statistical properties of financial
time series
•
Excess Volatility
•
Heavy Tails
•
Absence of autocorrelation in returns
•
Volatility clustering
•
Volatility autocorrelation
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Scope of financial time series modelling
•
Mean response  mean equation  ARMA
•
Conditional variance  price of stock depends on volatility
of stock returns with high excess kurtosis and serially
uncorrelated  GARCH
The development on financial time
series modelling grows extremely
fast new facts in the data
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Assumption of Classical financial time series models (log returns)
•
Normality assumption
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Acta oeconomica pragensia 9: (4), 2001.
FINANCIAL TIME SERIES AND
THEIR FEATURES
Josef ARLT, Markéta ARLTOVÁ*
Linearity assumption
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Data availability
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Data Analysis Support
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Available package in R
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Applications
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Study on The Factors Influencing the Determination of
Level and Indicators of Crisis Management Protocol
(CMP) in Indonesia
Joint work with
Prof. Nur Iriawan (ITS), Dr Suhartono (ITS), Dr. Brodjol SU (ITS)
and
The Ministry of Finance-Indonesia
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Introduction : background
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Why CMP is necessary?
 Global crisis in Europe and US gives significant impact to the stability of financial system in
Asia.
 Crisis Management Protocol (CMP) is required to minimize the impact of global financial
crisis
 To give direction towards actions has to be carried out when there is movement on
the domestic financial market as a result of crisis in global financial market.
 As an early warning system towards possibility of crisis in domestic financial market
 To recommend a standard procedure the to be carried out by the management of
SUN* (Government Securities/GS) to set up policy to encounter crisis of GS market.
*Surat Utang Negara (SUN) is a securities in the form of debt certificate issued by the Government of Indonesia in the form of State Bonds with 12 (twelve)
months tenor and regular coupon payment with a minimum transaction of Rp. 250,000,000,-.Type of SUN: Fixed Rate (FR Series), Variable Rate (VR Series)
Determination of CMP level needs variabes which can be a trigger significantly  Econometrics Modeling
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Reseach Methodology: Data (daily series)
1.
2.
3.
4.
5.
Yield of GS benchmark series  response variable
IDX Composite
CDS (Credit Default Swap) 5Y dan 10Y
Exchange Rate (IDR/USD)
Foreign Asset
This research is done by analyzing three differents data periods, i.e. starting from 2008
(data1), 2009 (data2) and September 2011 (data3)
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Econometric models
•
Transfer function : used to know the factors/ secondary indicators
which significantly influence the yield movement, as well as the
time delay
•
ARIMAX : used to model the mean level and identify outliers
•
ARMA-GARCH:
used to model the movement of mean and volatility of primary and
secondary indicators as the basis for determination of CMP level
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Basic Statistical models
•
ARIMA Model (Wei, 2006)
•
The Box-Jenkins approach typically comprises four parts:
- Identification of the model
- Estimation, usually OLS
- Diagnostic checking (mostly for autocorrelation)
- Forecasting
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Autoregressive Conditional Heteroskedasticity (ARCH)-Engle 1982
- used to model the volatility
- case of nonhomogoneous variance of the residual model
GARCH (Bollerslev(1986))

q
2
t
     i
i 1
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p
2
t i
   j
j 1
2
t j
ARIMAX : Determination of CMP level for the primary indicators
•
•
This study recommends to use sigma of
0.07 as the threshod for primary
indicator, obtained from the standard
error estimate of 5Y tenor with alpha
0.1%
Compared to the other values, threshold
0.07 listed as the hihgest among the
models, which means also that the
sensitivity is good.
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No.
Alpha
SIGMA limit
1.
1%
 2.57583 
2.
0.5%
 2.80703 
3.
0.1%
 3.29053 
4.
0.01%
 3.89059 
5.
0.27%
3
6.
0.00633%
4
Schwart, 1931
ARIMAX Models
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Defined levels (using 3sigma, 4.5sigma and 6 sigma):

o
o
o
o
•
On primary indicators:
Level normal
Level alert
Level pre-crisis
Level crisis
: < 21 bps
: 22 bps – 31.5 bps
: 32 bps – 42 bps
: > 42 bps
The levels above are significantly different with the levels specified by
Ministry. Furthermore, validation to data 1 shows that the ARIMAX levels
are very reasonable.
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Backtesting for levels developed by data3 (Sept 2011)
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Secondary indicators
Level
Indicator
Alert
Pre-Crisis
Crisis
Decreasing Foreign
Asset
1.2% per day or
2.463T IDR per day
1,8% per day or
3.6945T IDR per day
2.4 % per day or
4.926T IDR per day
Decreasing IDX Index
3.228% per day or
123.63 per day
4.842% per day or
185.445 per day
6.456% per day or
247.26 per day
Decreasing Exchange
Rate
1.287% per day or
94.548 per day
1,9305% per day or
141.822 per day
2.574% per day or
189.096 per day
Decreasing CDS 10Y
9.414% per day or
20.814 per day
14.121% per day or
31.221 per day
18.828% per day or
41.628 per day
Decreasing CDS 5Y
9.945% per day or
16.473 per day
14.917% per day or
24.709 per day
19.89% per day or
32.946 per day
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Indicator
Yield
IDX Composite
Exchange Rate
Foreign Asset
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Weight
Indicato
r
0.7
0.3
Level of the
weight
normal
0.25
1
1
0.493
0.38
0.127
1
1
1
alert
0.25
2
pre crisis
0.25
3
crisis
0.25
4
2
2
2
3
3
3
4
4
4
Composite Index
Indicator
Yield
IDX Composite
Exchange Rate
Foreign Asset
Index
Yield
IDX Composite
Exchange Rate
Foreign Asset
Index
Yield
IDX Composite
Exchange Rate
Foreign Asset
Index
level
normal
normal
normal
normal
0.25
alert
alert
alert
alert
0.5
precrisis
preciriss
precrisis
precrisis
0.75
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level
normal
normal
normal
alert
0.2595
alert
alert
alert
pre crisis
0.509525
preciriss
precrisis
precrisis
crisis
0.759525
level
normal
normal
alert
alert
0.288025
alert
alert
precrisis
precrisis
0.538
preciriss
precrisis
crisis
crisis
0.788025
level
normal
alert
alert
alert
0.325
alert
Precrisis
Precrisis
Precrisis
0.575
precresis
crisis
crisis
crisis
0.825
level
alert
normal
normal
normal
0.425
precrisis
alert
alert
alert
0.675
crisis
precrisis
precrisis
precrisis
0.925
level
alert
alert
normal
normal
0.461975
precrisis
precrisis
alert
alert
0.711975
crisis
crisis
precrisis
precrisis
0.961975
level
alert
alert
alert
Normal
0.490475
Precrisis
Precrisis
Precrisis
Alert
0.740475
Crisis
Crisis
Crisis
Precrisis
0.990475
Final Composite Index of CMP
Condition
I
II
III
IV
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Index
0.25 ≤ index < 0.325
0.325 ≤ index < 0.575
0.575 ≤ index < 0.825
Index ≥ 0.825
level
normal
alert
Pre-crisis
Crisis
Original Series
Percentage of change
volatility
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Development of Market Surveillence System
Indonesia Securities Investment Protecton Funds
(Indonesia SIPF)
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Goals of Market Surveillence
1.
2.
3.
4.
To ensure that the members run ther activities normally
To detect any possibility of market abuse,
To protect investors,
To ensure that the market is normal, efficient and transparent to reduce
systematic risk
Alert system has to be able to detect unusual events
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Component of the Survival System
1. Feature to perfomr the investor profile using some relevant statistics 
Reporting
How : Benchmarking, intensive discussion
2. Models which are able to perform and predict the fluctuation of changing in
assets  Alert system
How:
Alert system to detect the mean level and volatility, if they are still on a normal
postion, or alert.
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Illustration of Alert System
Financial time
series model is
used to costruct
the band
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ARIMA and GARCH
•
ARIMA  ARIMAX (ARIMA with outlier)
Introduced by Box & Jenkins (1976): Identification, estimation, diagnostic
checking, forecasting
AutoARIMA dari package “forecast” di R  Minimize user specification
•
Outlier Detection
Package “tsoutliers”  to detect Innovative Outlier (IO), Additive Outlier
(AO), Level Shift (LS) dan Temporary Change (TC)
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Thank you
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