Chasing the Ghost of Global Vagabonding Bubbles

Transcription

Universität Bayreuth
Rechts- und Wirtschaftswissenschaftliche Fakultät
Wirtschaftswissenschaftliche Diskussionspapiere
Bubblebusters
Chasing the Ghost of Global Vagabonding Bubbles
Christian Drescher
Discussion Paper 07-12
June 2012
ISSN 1611-3837
c 2012 by Christian Drescher. All rights reserved. Any reproduction, publication
and reprint in the form of a different publication, whether printed or produced
electronically, in whole or in part, is permitted only with the explicit written
authorization of the author.
Abstract
The paper identifies rational asset bubbles in corporate equity, real estate, fixed income
and commodity markets in order to draw a roadmap of global vagabonding bubbles.
The employed quasi real-time procedure dates the beginning and ending of rational asset
bubbles using right-sided forward recursive ADF tests. The empirical analysis yields the
following conclusions. Firstly, rational asset bubbles appear frequently worldwide in all
asset classes. Secondly, the average length of rational asset bubbles varies across all asset
classes and countries. Thirdly, rational asset bubbles tend to be clustered in corporate
equity, real estate and commodity markets.
Key words: asset bubbles, unit root tests, simulation method
JEL classification: C15, F21, G12
Author:
Christian Drescher
([email protected])
University of Bayreuth
Germany
Acknowledgements: The author would like to thank Bernhard Herz, Jane Sander and
the participants at the 5th Postgraduate Seminar in Bayreuth for helpful comments.
1 Motivation
The notion of global vagabonding bubbles describes the frequent emergence of asset bubbles worldwide, which are often supposed to have the same underlying macroeconomic
causes. The most prominent explanation attempts refer to an excess of macro liquidity, a saving glut and a shortage of assets, respectively. The excess liquidity hypothesis
points to failures of monetary policy by responding asymmetrically to bursting asset
bubbles (see Hoffmann and Schnabl, 2009; Taylor, 2009). Due to asymmetric behavior
in the wake of asset bubble collapses, monetary policy is blamed for sowing the seeds for
the formation of subsequent asset bubbles by providing too much macro liquidity. The
saving glut hypothesis famously pioneered by Bernanke (2005) stresses that current low
long-term interest rates are caused by increased desired savings relative to investments.
In this vein, Ferguson and Schularick (2007) argue that the historically low interest rates
led to a wedge between the return on and cost of capital, which triggered asset prices to
rise. The shortage of assets hypothesis claims the existence of a global asset shortage.
Caballero (2006) refers to the problem of emerging markets to generate financial assets
due to underdeveloped financial systems, while Rajan (2006) points to the lack of collaterizable real assets due to a chronic shortage of real investments. Both point of views
imply that asset bubbles are a natural equilibrium phenomenon in the wake of a market
mechanism that attempts to reduce the gap between demand for and supply of storage
of value. Regardless of the predominant cause, all three explanation attempts result in
an easing of monetary conditions for asset markets and thus provide a medium for the
formation of asset bubbles.
Indeed, the discussion on global vagabonding bubbles relies on theory (see Bernanke,
2005; Caballero, 2006; Taylor, 2009) and is fed by anecdotal evidence (see Hoffmann and
Schnabl, 2008), but it is poorly supported with empirical evidence. To overcome this
shortage, the paper draws a roadmap for rational asset bubbles to detect the empirical
footprints of asset bubbles. The detection of asset bubbles is no trivial task since the
3
underlying asset price process is unobserved. For that reason, in the last few decades,
many empirical approaches have been proposed to detect asset bubbles. Among them are
the variance bounds tests (see Shiller, 1981; LeRoy and Porter, 1981), the two step test
(see West, 1987) as well as the cointegration and unit root tests (see Diba and Grossman,
1987, 1988a). The application of these approaches for the detection of asset bubbles are
criticized due to their specification and interpretation. In this vein, the following analysis
employs the most recent proposition of right-sided forward recursive unit root tests (see
Phillips et al., 2009; Phillips and Yu, 2009; Homm and Breitung, 2012). This estimation
method mainly accounts for two issues: on the one hand, the theoretical property of
rational asset bubbles to have explosive roots by using a right-sided unit root test and
on the other hand, the phenomenon of periodically collapsing bubbles by using forward
recursions.
The paper is organized as follows. Section 2 explains the theoretical background of
rational asset bubbles. Section 3 reviews some milestones in empirical methods of asset
bubble detection. Section 4 introduces the right-sided forward recursive ADF test. Section 5 assesses its power to detect rational asset bubbles using Monte Carlo simulations.
Section 6 describes the dataset and dates the beginning and end of rational asset bubbles worldwide. Finally, Section 7 draws a roadmap of global vagabonding bubbles and
makes some conclusive remarks.
2 Theory of rational asset bubbles
An asset bubble exists if the asset price deviates from its fundamental value (see, e.g.,
Flood and Hodrick, 1990; Gurkaynak, 2008). Generally, these deviations can either result
from rational or irrational asset bubbles. In the case of rational asset bubbles, investors
know the underlying structure of the asset price process so that on average expectations
are correct (see, e.g., Blanchard and Watson, 1982; Tirole, 1982). By definition, rational
bubbles can be detected ex interim since investors acknowledge that the asset price
4
is driven by an explosive bubble process. In contrast, in the case of irrational asset
bubbles, investors do not know the underlying structure of the asset price process so
that on average expectations are incorrect (see, e.g., Kindleberger, 1996; Shiller, 2000).
By definition, irrational asset bubbles can only be detected ex post when asset prices
have adjusted toward fundamental values.
2.1 Asset pricing framework
The literature on rational asset bubbles starts with the Lucas asset pricing model (Lucas Jr., 1978). Given the no-arbitrage condition, risk neutrality and a constant discount
rate the two-period asset pricing formula is written as follows:
Pt =
Et (Pt+1 + Dt+1 )
,
1+r
(1)
where Pt and Pt+1 denote the current and future asset price (after payoffs), respectively.
Dt+1 represents the future stream of payoffs, r is the constant discount rate with 0 <
(1+r)−i < 1 and Et [·] denotes the conditional expectation operator at time t. A forward
iteration to infinity gives the multi-period asset pricing formula:
Pt =
∞
X
Et [Dt+i ]
i
i=1
(1 + r)
+
Et [Bt+i ]
= PtF + Bt .
1+r
(2)
The asset pricing formula consists of two basic components. The first component represents the fundamental value PtF and the second component represents the bubble value
Bt . The fundamental value is the discounted sum of the expected stream of payoffs,
whereas the bubble value is the discounted expectation of next period’s bubble value.
For rational asset bubbles, the bubble value is no mispricing but a rational component;
it prices in its resale value. Given the transversality condition, asset prices have a unique
solution determined by its fundamental value:
5
P = Pf =
∞
X
Et [Dt+i ]
i
i=1
(1 + r)
+ lim
i→∞
Pt+i
(1 + r)i
< ∞.
(3)
The transversality condition ensures that the bubble term is zero. Otherwise all investors
would be better off selling the asset so that the asset price would immediately fall to
its fundamental value. In this vein, Tirole (1982) argues that rational asset bubbles
can not exist in rational expectation models with an infinite planning horizon. But the
fundamental solution is just one class of possible solutions. Tirole (1985) also shows that
rational asset bubbles can not be ruled out in overlapping generations models with finite
planning horizons. As the bubble component can take every positive value1 the asset
price has an infinite number of possible solutions depending upon the expected future
bubble value:
Bt =
Et [Bt ]
.
(1 + r)
(4)
Equation (4) shows that a rational asset bubble can not re-emerge after a burst if the
expected future bubble value once becomes zero. Diba and Grossman (1987, 1988b)
deduce that when a rational asset bubble exists, it must have been always existed.
2.2 Periodically collapsing bubbles
The conclusion drawn by Diba and Grossman (1987, 1988b) allegedly contradicts with
anecdotal evidence, which says that asset bubbles emerge on a frequent basis. Theory
and anecdotal evidence can be brought together by modeling periodically collapsing
bubbles, whose bubble component does become zero in case of its burst. Following
Evans (1991, p. 924) perodical collapsing bubbles can be modeled as follows:
Bt =
1



(1 + r)B
if Bt−1 ≤ α,


−1
[δ + π −1 (1 + r)Θ (B
t
t−1 − (1 + r) δ)]ut ,
if Bt−1 > α.
t−1 ut ,
Following Tirole (1982), the rational asset bubbles have to be positive.
6
(5)
The initial bubble is set to be B0 = δ. The parameters satisfy 0 < δ < (1 + r)α.
For a sufficiently small bubble Bt−1 ≤ α the bubble component Bt grows slowly with
r adjusted by the random value ut . The variable r is a constant interest rate and the
random variable ut is an i.i.d. process with Et [ut ] = 1 and ut > 0. For a sufficiently big
bubble Bt−1 > α the bubble component Bt grows more explosively but is additionally
subject to the possibility of bursting with probability 1 − π. The existence of the bubble
is determined by Θt . The variable Θt follows an i.i.d. Bernoulli process that depicts the
value 1 with probability π and 0 with probability 1 − π. It holds that 0 < π ≤ 1. If the
bubble bursts then the bubble component falls back to the non-zero value of δut . The
calibration of the parameters δ, α and π determines the frequency, average length and
scale of periodically collapsing bubbles.
2.3 Theoretical properties of rational asset bubbles
As has been shown, rational asset bubbles can be decomposed into a fundamental and
bubble value. The time series of each component has a different statistical property.
These differences can be used to detect rational asset bubbles in the asset price time
series. That is because the relevance of the fundamental and bubble value for the asset
price determines the statistical property of the asset price time series. If the asset price
is close to its fundamental value, then the statistical property of the fundamental value
should be predominant and hence be detected in the asset price time series. In contrast,
if the asset price is driven by a sizeable asset bubble, then the statistical property of the
bubble value should outweigh and be detected in the asset price time series. But what
are the differences in the statistical properties of these values?
The fundamental value follows an unobserved process. But empirical stylized facts
show that over long periods of time, asset prices usually behave like a random walk with
a drift component (see West, 1988). Given a constant interest rate, it is reasonable to
assume that the expected stream of payoffs D also behaves like a random walk with a
drift component:
7
Dt = µ + Dt−1 + t
with
t ∼ N (0, σ 2 ).
(6)
In this case, conditional expectations of the fundamental value should grow in a linear
manner with the increase of the forecasting horizon (see Evans, 1991, p. 923). In the
short-term, the drift component is negligibly small so that asset prices should have unit
root characteristics in the absence of a sizeable bubble (see Phillips and Yu, 2009, p. 7).
The theory of rational asset bubbles claims that the bubble value should grow at a
rate equal to the discount rate r. This proposition becomes obvious by transforming
Equation (4) with respect to its expected value for the next period:
Et [Bt+1 ] = (1 + r)Bt .
(7)
Conditional expectations of the bubble component have to grow in a nonlinear manner
with the increase of the forecasting horizon (see Evans, 1991, p. 923). The reason is that
investors demand compensation for the additional risk caused by the deviation from the
fundamental value. This reflects the “self-fulfilling prophecy” of investors when the asset
price is not equal to the fundamental value (see Diba and Grossman, 1988a, p. 522).
This nonlinearity implies that the bubble value has an explosive root characteristic (see
Phillips and Yu, 2009, p. 7).
3 Milestones of asset bubble detection
This section reviews some milestones in empirical methods of asset bubble detection
to rationalize the use of right-sided forward recursive unit root tests in the following
analysis. For a comprehensive review of empirical methods see Flood and Hodrick (1990)
and Gurkaynak (2008).
8
Among the earliest empirical methods used to detect asset bubbles are the variance
bounds tests (Shiller, 1981; LeRoy and Porter, 1981).2 These tests build upon the idea
that ex post rational prices (present value of actual payoffs) should be more volatile
than observed prices (present value of expected payoffs) since these include next to the
volatility of payoffs also the volatility of forecasting errors. The variance bounds tests
conclude, in the case of the violation of the imposed bounds, that asset prices follow an
alternative model. Although variance bounds tests were originally designed to test the
validity of the present value model (see, e.g., Shiller, 1981; Grossman and Shiller, 1981),
they have also been used to test for asset bubbles (see, e.g., Tirole, 1985; Blanchard and
Watson, 1982). The main point of criticsm in the use of these tests is the interpretation
in the case of a violation of the variance bounds. The variance bounds tests do not
distinguish between an incorrect specification of the model, an asset bubble or other
plausible reasons for a deviation from the fundamental value.3
The two step test of West (1987) departs from this point of criticism and tests the null
hypotheses of a correct specification and of no asset bubble, sequentially. In case the
first null hypothesis is not rejected (correct specification) and the second null hypothesis
is rejected (no asset bubble) then the test concludes the presence of an asset bubble. In a
first step, the discount rate and payoff process is estimated to construct the relationship
between the fundamental value and its stream of payoffs. In a second step, the presence of
an asset bubble is tested by a comparison of the relationships between the constructed
fundamental value and payoffs with the asset price and payoffs. Flood et al. (1994)
remark that the two-step test is still subject to the point of criticism that evidence
of different relationships may be due to an asset bubble or other plausible reasons for
deviations.
2
A literature survey on variance bounds tests can be found in Gilles and LeRoy (1991).
3
The existence of asset bubbles is just one of many plausible reasons for the asset price to deviate from
the fundamental value. For instance, one could think of over-reactions to news (Capelle-Blancard
and Raymond, 2004, p. 1).
9
Diba and Grossman (1987, 1988a) encounter this challenge in proposing cointegration
and unit root tests to impose a more strict criterion for the detection of asset bubbles.
These approaches make use of the theoretical property of rational asset bubbles to have
explosive roots. The tests build upon the rationale that if rational asset bubbles exist,
then they have always existed.4 The idea of cointegration tests is that if the linear
combination of the asset price and its fundamental value is stationary, then no rational
asset bubble should be present since deviations are only found to be temporal. The
idea of unit root tests is that if asset prices are integrated of some order, then no
rational asset bubble is present since its explosive root should also be detectable in the
differences (see Phillips et al., 2009, p. 3). Evans (1991, pp. 926) disagreed by arguing
that periodically collapsing bubbles — probably the most relevant bubble phenomenon
— are typically not unveiled by these standard tests. The reason is that the entire time
series is most likely to look like a non-explosive process since periodically collapsing
bubbles are characterized only by temporal explosive behavior (see Phillips et al., 2009,
pp. 4,7). Based upon this criticism, a number of authors attempted to avoid this pitfall
by analyzing different regimes — bubble expansions and contractions — using Markovswitching unit root tests (see, e.g., Hall and Sola, 1993; van Norden, 1996; Hall et al.,
1999). van Norden and Vigfusson (1998) conclude that this approach is quite sensitive
to the exact modeling of periodically collapsing bubbles so that the detection of rational
asset bubbles seems to be to some degree arbitrary.
From the author’s point of view, probably the most promising strand of empirical
methods to detect rational asset bubbles has recently emerged along with the works of
Phillips et al. (2009), Phillips and Yu (2009) and Homm and Breitung (2012). These
authors propose right-sided forward recursive unit root tests to detect rational asset
bubbles. This estimation method mainly accounts for two issues: on the one hand, the
theoretical property of rational asset bubbles to have explosive roots by using a right4
The reasons are the no-arbitrary condition and the impossibility of negative prices (see Diba and
Grossman, 1987, 1988b).
10
sided unit root test and on the other hand, the phenomenon of periodically collapsing
bubbles by using forward recursions.
4 Empirical procedure
4.1 Estimation method
The empirical analysis follows Phillips et al. (2009), Phillips and Yu (2009) and Homm
and Breitung (2012) and uses right-sided forward recursive ADF tests to detect rational
asset bubbles. The application of right-sided instead of standard left-sided ADF tests
(Dickey and Fuller, 1979, 1981) has the advantage of assessing the presence of a rational
asset bubble already in level data.5 The forward recursions focus on subsamples s whose
period length of analysis increments with one at every recursion step.6 More specifically,
the following autoregressive asset price process is estimated:
∆pt = α + (ρ − 1)pt−1 +
k
X
βi ∆pt−i + t
with
t ∼ N (0, σ 2 ).
(8)
i=1
The italic letters pt refer to logarithmic asset prices.7 The logarithmic asset prices are
de-averaged (and de-trended) by regressing them on a constant (and a trend). The
optimal lag length of the AR(k) process is chosen in every recursion step using the
Akaike information criterion (Akaike, 1974). In line with Phillips et al. (2009) and Shi
(2009), the maximum lag length is set to be 12. The null hypothesis of each right-sided
ADF test claims ρt to be a non-explosive process:
5
The application of the standard left-sided ADF test requires the analysis of level data and most
probably of their differences until an order is detected to be stationary.
6
The initial sample size is set to be s = 40 to ensure estimation efficiency by incorporating sufficient
observations. This setting is in line with Phillips et al. (2009) who choose sinitial = 39 and Shi (2009)
who restricts the smallest window to be sinitial = 40.
7
Time series are not deflated by any kind of price index as is sometimes done in other literature (see,
e.g., Phillips et al., 2009) since the analysis is motivated by the issue of global vagabonding bubbles
and its monetary explanation attempts. If the affirmation of Friedman (1963, p. 17) that “inflation
is always and everywhere a monetary phenomenon” holds, then changes in nominal asset price indices
should also reflect monetary conditions.
11
H0 :
ρt ≤ 1
for
t = {1, 2, ..., s}.
The null hypothesis is motivated by the efficient market hypothesis, which states that
asset prices approximately follow a martingale (see Fama, 1970). Instead, the alternative
hypothesis of each right-sided ADF test claims ρt to be an explosive process:
H1 :
ρt > 1
for
t = {1, 2, ..., s}.
The alternative hypothesis is motivated by the theory of rational asset bubbles, which
claims that in the presence of a rational asset bubble, asset prices should follow a submartingale (see Diba and Grossman, 1987, 1988b).
4.2 Dating of rational asset bubbles
Following the theory of rational asset bubbles, the following analysis defines rational
asset bubbles to be characterized by an explosive autoregressive behavior (see Phillips
et al., 2009, p. 2). Based upon this definition, the beginning and end of a rational asset
bubble is detected by a comparison of the ADF test statistics with the corresponding
right-sided critical values. In line with Phillips and Yu (2009, p. 13) the beginning of a
rational asset bubble is dated to the point in time t̂beginning , when the test statistic of
the asset price time series with sample size s intersects its corresponding critical value
cv ADF (s) from below:
t̂beginning =



t < t̂beginning : supADF (s) < cv ADF (s),


ADF
t ≥ t̂
(s).
beginning : supADF (s) > cv
Equivalently, the end of a rational asset bubble is dated to the point in time t̂end , when
the ADF test statistic of the asset price time series with sample size s intersects its
corresponding critical value cv ADF (s) from above:
12



t < t̂end : supADF (s) > cv ADF (s),
t̂end =


ADF
t ≥ t̂
(s).
end : supADF (s) < cv
4.3 Computation of critical values
The critical values are obtained based upon own calculations since conventional tables
for unit root tests give only left-sided critical values and do not provide critical values
for every forward recursive step (see, e.g., Fuller, 1996, Table 10.A.2). Moreover, critical
values are generated using simulation methods to take account of the following two
issues (see, e.g., MacKinnon and Smith Jr., 1998; Gourieroux et al., 2000). Firstly, it
is well known from finite sample theory that small samples might have other properties
than large samples. Secondly, the estimation of an autoregression using OLS produces
a downward coefficient bias (see Phillips et al., 2009, p. 10). In more detail, critical
values are estimated using Monte Carlo simulations with R = 10, 000 runs. In each run
a random walk yt is simulated over T = 500 periods:
yt = βyt−1 + t
with
t ∼ N (0, σ2 ).
(9)
The parameter β is set to be 1 and the initial value y0 is chosen to be 0. The right-sided
critical values are inferred from the simulation outcome across all runs. The 1%, 5% and
10% significance levels are the first, fifth and tenth percentile of the resulting distribution
of the test statistic values. Figure 1 illustrates the right-sided critical values for different
significance levels. The results for the Monte Carlo simulation indicate that simulation
imprecisions of the test statistics are higher, the lower the calculated significance level.
The following analysis uses the 5% significance level to balance the trade off between
the error of the first kind and simulation imprecisions.
13
Figure 1: Right-sided critical values
5 Power analysis
The following analysis asks in how many cases the existence of rational asset bubbles is
correctly identified using right-sided forward recursive ADF tests. To make these results
comparable with different versions of ADF tests each time series of artificial asset prices
is generated using the same model structure as in Evans (1991) and Phillips et al. (2009).
Asset prices are assumed to be an additive composition of a fundamental value PtF and
a bubble value Bt :8
Pt = PtF + 20Bt .
(10)
In line with the asset pricing formula of Equation (1), the fundamental value is determined by the discount rate r and the payoff process D:
PtF = µ(1 + r)r−2 + Dt r−1 .
8
(11)
Following Evans (1991, p. 926) the bubble component is scaled by the factor 20 to ensure that the
bubble variance ∆B is about three times the fundamental variance ∆F .
14
Based upon the empirical estimations of West (1987) for the S&P 500 Index, parameters
of the fundamental value model are set to be µ = 0.0373, r = 0.05, σ 2 = 0.1574 and
D0 = 1.3. Following Equation (6), the payoff process is a random walk with a drift
component:
Dt = µ + Dt−1 + ut
with
ut ∼ N (0, σu2 ).
(12)
Periodically collapsing bubbles are generated using the model structure of Equation (13):
Bt =



(1 + r)B
t−1 uB,t ,
if


−1
[δ + π −1 (1 + r)Θ (B
t
t−1 − (1 + r) δ)]uB,t ,
if
Bt−1 ≤ α,
(13)
Bt−1 > α.
Following Evans (1991) and Phillips et al. (2009), the parameter values of the periodically
collapsing bubbles model are set to be α = 1, δ = 0.5, π = 0.5, r = 0.05 and uB,t =
eyt −τ
2
/2
with τ = 0.05 and yt ∼ i.i.d.(0, τ 2 ). Figure 2 illustrates an example of an
artificial asset price with its fundamental and bubble component. Figure 3 shows the
corresponding test statistics and the right-sided critical values.
Figure 2: Periodically collapsing bubble
Figure 3: Bubble identification
The Monte Carlo simulations are conducted with R = 1, 000 runs for 100 periods. The
power analysis uses three estimation methods: (1) ADF tests over the entire sample,
(2) forward recursive ADF tests for de-averaged subsamples and (3) forward recursive
ADF tests for de-averaged and de-trended subsamples. Table 1 illustrates the percentage of identified time series with periodically collapsing bubbles for different periodical
probabilities to continue π.
15
π
99 %
95 %
Estimation method (1)
66.2 %
19.9 %
9.4 %
5.8 %
1.9 %
0.3 %
0%
99.3 %
89.8 %
81.0 %
71.3 %
59.3 %
18.1 %
2.9 %
88.7 %
86.2 %
78.5 %
70.6 %
55.6 %
17.2 %
2.8 %
90 %
85 %
75 %
50 %
25 %
Table 1: Power analysis
The power analysis shows for all three estimation methods that the percentage of detected periodically collapsing bubbles increases with the probabilities to continue. Moreover, the analysis indicates that the estimation method (2) with forward recursive ADF
tests for de-averaged subsamples has more power to detect periodically collapsing bubbles than the estimation methods (1) with ADF tests over the entire sample and (3) with
forward recursive ADF tests for de-averaged and de-trended subsamples. In line with
the power analysis of Phillips et al. (2009), the results suggest that subsample based
ADF tests are superior to ADF tests over the entire sample. This finding underlines the
criticism of Evans (1991) that ADF tests for the entire sample are widely incapable of
unveiling periodically collapsing bubbles. Overall, the power analysis makes the case for
the following empirical analysis of rational asset bubbles to use forward recursive ADF
tests for de-averaged subsamples.
6 Empirical analysis
6.1 Dataset
The empirical analysis covers 4 asset classes, namely corporate equities, real estate, government bonds and commodities of 62 countries for the period from 1986:01 to 2010:12.9
The prices of corporate equities, real estate and government bonds are analyzed on a
country basis, whereas commodity prices are analyzed for a major market place.10 The
analysis focuses on these asset classes since they are frequently suspected to have been
9
10
The estimation sample is chosen to map the period of “Great Moderation” (Stock and Watson, 2002).
In case of government bonds all yields are subtracted from 100 to generate artificial bond prices.
16
subjected to asset bubbles in recent decades. Figures 4–6 illustrate all countries that are
covered by the empirical analysis. Black shaded countries are subject to the analysis,
whereas gray shaded countries are not. Figure 7 lists all commodities that are part of
the analysis.
Figure 4: Corporate equity data
Figure 5: Real estate data
Commodities
Aluminum
Figure 6: Government bond data
Crude oil
Cocoa
Gold
Coffee
Silver
Copper
Soybeans
Corn
Sugar
Cotton
Wheat
Figure 7: Commodity data
The periods of analysis for each country and asset class may vary due to data availability.
For a detailed list of countries, asset classes, periods of analysis and data sources (see
Appendix B).
6.2 Roadmap of global vagabonding bubbles
The detection of rational asset bubbles is conducted using right-sided forward recursive
ADF tests at the 5 % significance level. The results of analysis are illustrated for each
asset class separately by colored world maps of 5-year windows. Red and black colored
countries are subject to the analysis, whereas gray colored countries are not. Red colored
countries indicate the presence of a rational asset bubble that continues for at least five
succeeding months. Black colored countries indicate that no rational asset bubble is
17
found that satisfies this criterion. This restriction is implemented for the following maps
to show only sizeable rational asset bubbles by shielding temporary explosive behavior.
Nevertheless, all figures are still based upon all found indications of explosiveness.
The analysis for the period 1986 to 1990 identifies the corporate equity crashes of 1987
and gives indications for Japan’s “bubble economy” (see Figures 8–10 and Table 11).
Figure 8: Corporate equity markets
from 1986 to 1990
Figure 10: Government bond markets
from 1986 to 1990
Figure 9: Real estate markets
from 1986 to 1990
Commodity
Bubble
Aluminum
—
Cocoa
Coffee
Commodity
Bubble
Crude oil
Yes
Yes
Gold
No
—
Silver
No
Copper
No
Soybeans
No
Corn
No
Sugar
No
Cotton
—
Wheat
No
Figure 11: Commodity markets
from 1986 to 1990
The analysis for the late 1980s detects many indications of rational asset bubbles in
corporate equity markets. In 1986/1987 more than half of corporate equity markets
were characterized by explosive behavior. Most of these indications suddenly disappear
along with the crash of October 1987 (see, e.g., Shiller, 1987; Carlson, 2006). At the end
of the decade indications of rational asset bubbles decrease in frequency (see Table 2).
The frequency of rational asset bubbles in real estate markets shows a similar development. The number of indications of explosive behavior increases in 1986/1987, reaches
a peak in 1988 and abates while approaching the end of the decade. In contrast, during
this time, government bond and commodity markets are only slightly exposed to rational
18
1986
1987
1988
1989
1990
Bubble
Obs.
Bubble
Obs.
Bubble
Obs.
Bubble
Obs.
Corp. equities
60.6%
132
59.3%
140
31.9%
144
17.6%
193
6.2%
226
Real estate
20.4%
93
42.7%
96
62.5%
96
53.1%
113
28.8%
132
Gov. bonds
11.0%
245
4.6%
261
1.1%
264
4.0%
272
3.6%
304
8.9%
101
2.8%
108
0.0 %
108
6.5%
108
2.8%
109
Commodities
Bubble
Obs.
Table 2: Summary statistic for the period 1986-1990
asset bubbles. Meanwhile, Japan is the only country that is subject to rational asset
bubbles in corporate equity, real estate and government bond markets. These findings
support literature on Japan’s “bubble economy” claiming the existence of various asset
bubbles (see, e.g., Ito and Iwaisako, 1995; Okina et al., n.d.; Shigenori, 2005). After
these markets successively marked their peaks at the turn of the decade, all indications
of explosive behavior disappeared.
The analysis for the period 1991 to 1995 marks the beginning of the following bubble
era in corporate equities and confirms anecdotal evidence on rational asset bubbles in
advance of the East Asian Crisis (see Figures 12–14 and Table 15).
from 1991 to 1995
from 1991 to 1995
from 1991 to 1995
Commodity
Bubble
Commodity
Aluminum
No
Crude oil
No
Cocoa
No
Gold
No
Coffee
—
Silver
No
Copper
Yes
Soybeans
No
Corn
No
Sugar
No
Cotton
No
Wheat
No
from 1991 to 1995
19
Bubble
In the early 1990s, rational asset bubbles are rare across all asset classes. During this
time, the frequency of explosive behavior in corporate equity markets began to prosper
again. The analysis indicates the formation of a rational asset bubble in the US corporate
equity market. At the same time, the frequency of explosive behavior reaches the troughs
in real estate markets. Rational asset bubbles in government bond and commodity
markets are still scarce (see Table 3).
1991
1992
1993
1994
1995
Bubble
Obs.
Bubble
Obs.
Bubble
Obs.
Bubble
Obs.
Bubble
Obs.
2.24%
268
4.91%
285
13.10%
313
21.30%
324
7.12%
365
Real estate
28.37%
141
8.33%
144
4.05%
148
12.82%
156
10.59%
170
Gov. bonds
2.88%
312
3.85%
312
7.37%
312
7.84%
319
1.20%
334
Commodities
0.00%
120
0%
128
0%
132
2.27%
132
3.79%
132
Corp. equities
Nevertheless, anecdotal evidence from Hoffmann and Schnabl (2008) claims the presence of asset bubbles in various East Asian countries prior to the Asian crisis in 1997.
The empirical analysis indicates rational asset bubbles in corporate equity markets of
Malaysia and the Philippines. Other countries exposed to the East Asian crisis, such as
Indonesia, South Korea and Thailand, are not found to be subjected to explosive behavior. These findings are in line with most empirical literature on rational asset bubbles
for these countries (see, e.g., Chan et al., 1998; Doffou, 2007).
The analysis for the period 1996 to 2000 indicates that rational asset bubbles in
corporate equities spread over most advanced countries (see Figures 16–18 and Table 19).
from 1996 to 2000
from 1996 to 2000
20
from 1996 to 2000
Commodity
Bubble
Commodity
Bubble
Aluminum
No
Crude oil
No
Cocoa
No
Gold
No
Coffee
—
Silver
No
Copper
No
Soybeans
No
Corn
No
Sugar
No
Cotton
No
Wheat
No
from 1996 to 2000
In the late 1990s, more and more corporate equity markets in Europe and North America
faced the formation of rational asset bubbles. During this time, the then-president of the
Federal Reserve — Alan Greenspan — already warned that corporate equity markets
might be over-valued by coining the phrase of “irrational exuberance” (Greenspan, 1996).
Remarkably, at the turn of millennium almost every country under analysis on those two
continents was subjected to a rational asset bubble (see Table 4).
1996
1997
1998
1999
2000
Bubble
Obs.
Bubble
Obs.
Bubble
Obs.
Bubble
Obs.
Bubble
Obs.
Corp. equities
15.30%
438
38.32%
475
33.66%
511
28.71%
519
38.95%
570
Real estate
20.41%
196
27.96%
211
38.91%
239
38.29%
269
35.14%
276
Gov. bonds
0.00%
348
3.45%
348
3.36%
357
1.94%
360
0%
359
Commodities
0.00%
132
0.00%
132
0.00%
132
0.00%
132
0%
132
The empirical analysis confirms an increasing frequency of explosive behavior in real
estate markets. In retrospect, this acceleration was the harbinger of the financial crisis
of the next decade. At the same time, rational asset bubbles in government bond and
commodity markets were still scarce.
The analysis for the period 2001 to 2005 gives reason to suppose that rational asset
bubbles began to split into two camps (see Figures 20–22 and Table 23).
21
from 2001 to 2005
from 2001 to 2005
from 2001 to 2005
Commodity
Bubble
Commodity
Bubble
Aluminum
No
Crude oil
No
Cocoa
No
Gold
No
Coffee
Yes
Silver
No
Copper
No
Soybeans
No
Corn
No
Sugar
No
Cotton
No
Wheat
No
from 2001 to 2005
Most corporate equity bubbles reached their peaks in 2000/2001. As this occurs, most
indications of explosive behavior immediately disappear. The following short period of
time is characterized by only a few rational asset bubbles across all asset classes and
markets (see Table 5).
2001
2002
Bubble
Obs.
Bubble
2003
Obs.
Bubble
2004
2005
Obs.
Bubble
Obs.
Bubble
Obs.
Corp. equities
10.91%
605
3.30%
636
3.57%
645
10.49%
648
26.42%
689
Real estate
33.57%
283
37.05%
305
42.68%
328
43.47%
352
47.21%
377
Gov. bonds
0.81%
370
0.26%
380
4.35%
391
2.46%
447
0.41%
484
Commodities
0.00%
132
0.00%
132
0.00%
140
2.08%
144
8.33%
144
Almost all asset bubbles that were detected hereupon started to emerge after the abating
of the mild recession 2001/2002. In the following period, rational asset bubbles began to
split into two camps, focusing on real estate bubbles in advanced countries and corporate
22
equity bubbles in emerging countries. The frequency of rational asset bubbles in real
estate markets still increased, whereas rational asset bubbles in corporate equity markets
just began to emerge in the middle of the decade.
The analysis for the period 2006 to 2010 captures peaks of rational asset bubbles for
both camps. The subsequent global financial crisis marks the formation of rational asset
bubbles in commodity markets (see Figures 24–26 and Table 27).
from 2006 to 2010
from 2006 to 2010
from 2006 to 2010
Commodity
Bubble
Aluminum
No
Commodity
Crude oil
Bubble
Yes
Cocoa
No
Gold
Yes
Coffee
Yes
Silver
Yes
Copper
Yes
Soybeans
No
Corn
No
Sugar
No
Cotton
No
Wheat
Yes
from 2006 to 2010
In the middle of the decade the frequency of rational asset bubbles in corporate equity
and real estate markets increased. In 2007/2008 the onset of the global financial crisis
triggered asset prices to fall on a worldwide scale. As a result, most indications of
explosive behavior in both markets immediately disappear. The set in of the financial
crisis also marked the starting point for many commodity markets to show explosive
behavior. This behavior can be particularly observed in commodity markets, such as
gold, that are historically related to the safe haven motive (see Baur and McDermott,
2010).
23
2006
2007
2008
2009
Bubble
Obs.
Bubble
Obs.
Bubble
Obs.
Corp. equities
41.26%
732
53.28%
732
16.42%
743
Real estate
47.70%
392
49.40%
415
21.46%
438
0.21%
478
0.00%
461
1.13%
444
2.03%
13.19%
144
9.72%
144
20.83%
144
9.72%
Gov. bonds
Commodities
Bubble
2010
Obs.
Bubble
Obs.
3.36%
744
10.66%
741
12.10%
463
19.59%
444
443
0.00%
432
144
21.53%
144
Table 6 indicates that along with the abating of the global financial crisis explosive behavior in corporate equity and real estate markets increased again. In contrast, rational
asset bubbles in government bond markets were still rare over the entire period.
6.3 Frequency, length and clustering of rational asset bubbles
Rational asset bubbles show different characteristics across asset classes and countries,
respectively. The following analysis reveals these differences for all detected rational
asset bubbles regarding the average frequency, average length and tendency to cluster
across countries.
Frequency. Rational asset bubbles appear across all asset markets, but some asset
classes seem to be more often subject to rational asset bubbles than others. The analysis
detects explosive behavior in asset prices for real estate markets in 32.08 %, for corporate
equity markets in 21.01 %, for commodity markets in 4.73 % and for government bond
markets in 2.40 % of months. The average frequency across all asset classes and markets
amounts to 16.02 % of months.
Figure 28 illustrates the frequency of rational asset bubbles for all asset classes together
along the timeline. To identify frequency cycles of rational asset bubbles, the average
frequency is taken as benchmark. Accordingly, local peaks of these cycles are identified
for the end of the 1980s, the 1990s and the middle of 2000s, whereas local troughs are
identified for the beginning of the 1990s, the 2000s and the end of 2000s. Interestingly,
every peak is accompanied by a boom and every trough is accompanied by a recession
in the real economy.
24
Figure 28: Frequency analysis for each year
Moreover, the analysis detects explosive behavior in asset prices for 91.93 % of countries. This finding suggests that rational asset bubbles are a worldwide phenomenon
across all asset classes. Figure 29 lists all countries sorted by the percentage of months
that are subject to explosive behavior in corporate equity, real estate and bond markets.
Figure 29: Frequency analysis for each country
The analysis shows that rational asset bubbles are not equally distributed over countries.
The average percentage of months over all countries amounts to 8.1 %. In some countries,
25
such as the Ivory Coast, Slovenia and Venezuela, the analysis detects indications of
explosive behavior in more than 40% of months. In other countries, such as Thailand,
Taiwan and Singapore, no explosive behavior is detected at all.
Length. The length of rational asset bubbles differs for each asset class and country.
Table 7 displays the average length of explosive behavior for corporate equities, real
estate, government bonds and commodities.
Absolute length
Number of bubbles
Average length
Corporate equities
2524
294
8.59
Real estate
2014
106
19.00
Government bonds
226
42
5.38
Commodities
151
27
5.59
Table 7: Length analysis for each asset class
The average length of rational asset bubbles differs for each asset class. Rational asset
bubbles in real estate have the highest average length, followed by corporate equities,
government bonds and commodities.
The length of rational asset bubbles differs also across countries. Figure 30 illustrates
the average length of explosive behavior in corporate equity, real estate and government
bond markets for each country.
26
Figure 30: Length analysis
for each country
The analysis reveals that the average length of explosive behavior across all asset classes
and markets is 10.47 months. The majority of countries is below this number.
Clustering. The created roadmap of rational asset bubbles suggests that the explosive behavior in each asset class is clustered across countries. This intuition is crosschecked with empirical evidence by comparing the conditional and unconditional probabilities of rational asset bubbles.
Figure 31: Clustering of corp. eq. bubbles Figure 32: Clustering of real estate bubbles
Figure 33: Clustering of gov. bond bubbles Figure 34: Clustering of commodity bubbles
27
Figures 31-34 plot the conditional and unconditional probabilities for corporate equities,
real estate, government bonds and commodities. The conditional probability denotes the
probability of a rational asset bubble in the presence of another rational asset bubble
in the same asset class at the same time. The unconditional probability denotes the
probability of a rational asset bubble independent of the presence or absence of another
rational asset bubble in the same asset class at the same time. The 45-degree line
(solid line) illustrates all points at which the conditional and unconditional probabilities
of rational asset bubbles are identical. If rational asset bubbles tend to cluster, then
these points should be above this line. The tendency to cluster is checked for each
asset class by running regressions with no constant (dashed line). The regression lines
of corporate equities, real estate and commodities run above, whereas the regression
line of government bonds runs below the 45-degree line. In general, the tendency to
cluster in an asset class is statistically significant at the 1 % significance level, when
the 99%-confidence bands (dotted lines) do not encompass the 45-degree line. The
empirical results show that the tendency to cluster for corporate equities, real estate
and commodities is statistically significant since the 99 % confidence bands exceed the
45-degree line. In contrast, the 99%-confident bands of government bonds encompass
the 45-degree line so that there is no evidence of a tendency to cluster. These findings
imply that rational asset bubbles in corporate equities, real estate and commodities are
more likely to exist when other markets within the same asset class experience a rational
asset bubble at the same time. In contrast, rational asset bubbles in government bonds
do not depend upon the presence of other rational asset bubbles of the same asset class
at the same time.
7 Conclusions
The paper aims to support the notion of global vagabonding bubbles with empirical
evidence. For this purpose, the paper detects rational asset bubbles in corporate equity,
28
real estate, government bond and commodity markets. The employed quasi real-time
procedure dates the beginning and end of rational asset bubbles using right-sided forward recursive ADF tests. Based upon these results, the paper draws a roadmap of
global vagabonding bubbles from 1986 to 2010.
Starting in the late 1980s the analysis detects the corporate equity crashes of 1987
and gives indications of Japan’s “Bubble Economy” for the corporate equity, real estate and government bond market. The empirical analysis for the early 1990s marks
the beginning of the following bubble era in corporate equities and confirms anecdotal
evidence on rational asset bubbles in advance of the East Asian Crisis. The empirical
analysis indicates rational asset bubbles in corporate equity markets of Malaysia and
the Philippines. Other countries exposed to the East Asian crisis, such as Indonesia,
South Korea and Thailand, are not found to show explosive behavior. In the late 1990s
the analysis indicates rational asset bubbles in corporate equity markets spreading over
most advanced countries. At the end of the decade, almost every country under analysis
in Europe and North America was subjected to rational asset bubbles. Most corporate
equity bubbles reached their peaks in 2000/2001. Along with the peaks, most indications of explosive behavior immediately disappear. In the early 2000s, the world began
to split into two camps after the abating of the mild recession of 2001/2002. On the
one side, real estate bubbles continued to form mainly in Europe and North America.
On the other side, especially emerging countries in Asia and South America faced the
formation of corporate equity bubbles in the middle of the decade. In the late 2000s,
asset bubbles of both camps immediately disappeared along with the set in of the global
financial crisis in 2007/2008. At the same time commodity markets started to indicate
the presence of rational asset bubbles.
Based upon these empirical results, the paper yields the following main conclusions.
Firstly, rational asset bubbles appear frequently worldwide in all asset classes. The average frequency across all asset classes and markets is 16.02 % of months. Some asset
29
markets are more often subject to rational asset bubbles than others. The analysis detects the highest average frequency of rational asset bubbles in real estate with 32.08 %
of months, followed by corporate equities with 21.01 %, commodities with 4,73 % and
government bonds with 2.40 % of months. The analysis detects explosive behavior in asset prices for 91.93% of countries. Secondly, the average length of rational asset bubbles
varies across all asset classes and countries. The average length of explosive behavior
across all asset classes and markets is 10.47 months. The highest average length of
rational asset bubbles is detected for real estate with 19.00 months, followed by corporate equities with 8.59, commodities with 5.59 and government bonds with 5.38 months.
Thirdly, rational asset bubbles tend to be clustered in corporate equity, real estate and
commodity markets. For these asset classes it holds that rational asset bubbles are more
likely to exist in the presence of another asset bubble within the same asset class at the
same time. In contrast, government bond markets do not tend to be clustered across
countries.
30
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34
A Appendix
B Appendix:
Data information
All time series that do not originally come with monthly frequency are linearily interpolated to conduct analyses on monthly basis.
Africa:
Africa
Market indices
From – To
Source
Egypt
CASE 30 Index
1998:01 – 2010:12
World Market Monitor
Ivory Coast
BRVM Index
1998:10 – 2010:12
Marocco
All Share Index
2002:02 – 2010:12
South Africa
All Share Index
2002:02 – 2010:12
Table 8: African corporate equity market
Africa
Market indices
From – To
Source
Egypt
—
—
—
Ivory Coast
—
—
—
Marocco
—
—
—
South Africa
M:ZA:0:2:0:2:0:1
1980:01 – 2010:08
BIS Property Price Statistics
Table 9: African real estate market
Africa
Market yields
From – To
Source
Egypt
—
—
—
Ivory Coast
—
—
—
Marocco
15 year Treasury bond yield
1997:12 – 2007M3
IMF IFS
South Africa
Government bond yield
1980M1 – 2010M6
IMF IFS
Table 10: African government bond market
35
Asia:
Asia
Market indices
From – To
Source
China
Shanghai Composite Index
1996:01 – 2010:12
Indonesia
Composite Index
1986:02 – 2010:12
Japan
Nikkei 225 Index
1981:02 – 2010:12
Korea
KOSPI Index
1980:02 – 2010:12
Malaysia
Bursa Malaysia Securities
1986:01 – 2010:12
Berhad Composite Index
Pakistan
Karachi 100 Share Index
1997:08 – 2010:12
Philippines
Manila Stock Index
1986:02 – 2010:12
Russia
RTS Stock Index
1997:02 – 2010:12
Singapore
Straits Times Index
1980:01 – 2010:12
Sri Lanka
Composite Index
1992:07 – 2010:12
Taiwan
Taipai Trade Weighted Index
1986:02 – 2010:12
Thailand
General Index
1988:01 – 2010:12
Table 11: Asian corporate equity market
Asia
Market indices
From – To
Source
China
Q:CN:0:0:0:1:1:0
1998:03 – 2010:03
Indonesia
Q:ID:4:1:2:0:0:0
2002:03 – 2010:09
Japan
Urban land prices
1980:01 – 2010:06
Ministary of Internal Affairs
and Communications, Statistics Bureau
Korea
M:KR:0:1:0:2:0:0
1986:01 – 2010:07
Malaysia
Q:MY:0:1:0:0:1:0
1999:03 – 2009:12
Pakistan
—
—
—
Philippines
—
—
—
Russia
Q:RU:9:1:1:1:1:0
2001:03 – 2010:06
Singapore
—
—
—
Sri Lanka
—
—
—
Taiwan
—
—
—
Thailand
Q:TH:3:3:0:0:1:0
1995:03 – 2010:06
Table 12: Asian real estate market
36
Asia
Market yields
From – To
Source
China
—
—
—
Indonesia
—
—
—
Japan
1980M1 – 2010M3
IMF IFS
Korea
Yld. on national housing
1980M1 – 2010M6
IMF IFS
bonds, 1&2
Malaysia
Government bonds 5 years
1992M2 – 2009:11
IMF IFS
Pakistan
1980M1 – 2010M5
IMF IFS
Philippines
1994:10 – 2007M5
IMF IFS
Russia
2005M4 – 2010M4
IMF IFS
Singapore
—
—
—
Sri Lanka
1983M7 – 1994M8
IMF IFS
Taiwan
—
—
—
Thailand
1980M1 – 2010M6
IMF IFS
Table 13: Asian government bond market
37
Australia:
Australia
Market indices
From – To
Source
Australia
(S&P/ASX) All Ordinary
1980:01 – 2010:12
Australia
Index
New Zealand
50 Free Stock Index
2002:04 – 2010:12
Table 14: Australian corporate equity market
Australia
Market indices
Australia
New Zealand
From – To
Source
Q:AU:4:1:1:1:0:0
1986:09 – 2010:06
Q:NZ:0:1:0:3:0:0
1980:01 – 2010:12
Table 15: Australian real estate market
Australia
Market yields
From – To
Source
Australia
Treasury bonds: 15 years
1980:01 – 2010M6
IMF IFS
New Zealand
1980:01 – 2010M6
IMF IFS
Table 16: Australian government bond market
38
Europe:
Europe
Market indices
From – To
Austria
ATX Index
1986:02 – 2010:12
Belgium
General Index
1980:02 – 2010:12
Bulgaria
SOFIX
2002:01 – 2010:12
Croatia
Crobex Index
1998:02 – 2010:12
Denmark
OMX C 20 Index
1990:01 – 2010:12
Finland
HEX General Index
1987:02 – 2010:12
France
CAC 40
1987:08 – 2010:12
Germany
DAX 30
1980:01 – 2010:12
Greece
Athens Stock Index
1990:02 – 2010:12
Hungary
BUX Index
1992:07 – 2010:12
Iceland
ICEX Main Index
1995:03 – 2010:12
Ireland
ISEQ Overall Index
1986:02 – 2010:12
Italy
BCI General Index
1980:01 – 2010:12
Latvia
General Index
2002:09 – 2010:12
Lithuania
General Index
2002:09 – 2010:12
Luxembourg
General Cours Index
2000:01 – 2010:12
Netherlands
AEX 24 Index
1989:01 – 2010:12
Norway
OBX Index
1992:07 – 2010:12
Poland
WIG 20 Index
1994:05 – 2010:12
Portugal
BVL General Index
1988:02 – 2010:12
Romania
BET Index
2002:02 – 2010:12
Slovak Republic
SAX Index
1996:09 – 2010:12
Slovenia
SBI Index
1997:02 – 2010:09
Spain
Madrid General Index
1980:01 – 2010:12
Sweden
OMS S 30 Index
1994:05 – 2010:12
Switzerland
SPI Index
1987:11 – 2010:12
Turkey
National 100 Composite Index
1994:10 – 2010:12
UK
FTSE 100 Index
1984:02 – 2010:12
Source
Table 17: European corporate equity market
39
Europe
Market indices
From – To
Austria
Q:AT:1:1:0:0:1:0
2000:03 – 2010:03
Belgium
Q:BE:0:1:1:0:0:0
1973:03 – 2010:06
Bulgaria
Q:BG:4:8:1:1:1:0
1993:03 – 2010:06
Croatia
—
—
—
Denmark
Q:DK:0:2:0:1:0:0
1992:03 – 2010:03
Finland
Q:FI:0:1:1:1:1:0
2006:03 – 2010:06
France
Q:FR:0:1:1:1:0:0
1996:03 – 2010:12
Germany
A:DE:0:1:1:2:1:0
1995:12 – 2010:12
Greece
Q:GR:4:8:0:0:1:0
1993:12 – 2010:06
Hungary
Q:HU:2:1:1:1:0:0
2001:12 – 2010:06
Iceland
M:IS:3:1:0:3:1:0
2000:01 – 2010:07
Ireland
Q:IE:0:1:1:3:0:0
1999:03 – 2009:12
Italy
H:IT:0:1:0:0:1:0
1990:06 – 2009:12
Latvia
Q:LV:0:8:1:1:1:0
2006:03 – 2010:06
Lithuania
Q:LT:0:1:0:0:1:0
1998:12 – 2010:03
Luxembourg
—
—
—
Netherlands
M:NL:0:1:1:1:0:0
1995:01 – 2010:08
Norway
Q:NO:0:1:0:1:0:0
1992:03 – 2010:06
Poland
Q:PL:6:8:1:2:1:0
2005:06 – 2010:06
Portugal
M:PT:0:1:0:2:1:0
1988:01 – 2010:08
Romania
—
—
—
Slovak Republic
Q:SK:0:1:1:2:1:0
2005:03 – 2010:06
Slovenia
—
—
—
Spain
Q:ES:0:1:0:1:1:0
1995:03 – 2010:06
Sweden
Q:SE:0:1:0:1:0:0
1986:03 – 2010:06
Switzerland
Q:CH:0:2:0:2:0:0
1970:03 – 2010:06
Turkey
—
—
—
UK
M:GB:0:1:0:2:0:0
1983:01 – 2010:08
Table 18: European real estate market
40
Source
Europe
Market yields
From – To
Source
Austria
1980:01 – 2010:06
IMF IFS
Belgium
1980:01 – 2010:06
IMF IFS
Bulgaria
1993:07 – 2010:06
IMF IFS
Croatia
—
—
—
Denmark
1980:01 – 2010:06
IMF IFS
Finland
1987:11 – 2010:06
IMF IFS
France
1980:01 – 2010:06
IMF IFS
Germany
1980:01 – 2010:06
IMF IFS
Greece
1986:05 – 2010:06
IMF IFS
Hungary
2001:01 – 2010:06
IMF IFS
Iceland
Government bond yield:
1992:01 – 2010:06
IMF IFS
10 year ind.
Ireland
1980:01 – 2010:06
IMF IFS
Italy
1980:01 – 2010:06
IMF IFS
Latvia
2001:01 – 2010:06
IMF IFS
Lithuania
2001:01 – 2010:06
IMF IFS
Luxembourg
1980:01 – 2010:06
IMF IFS
Netherlands
1980:01 – 2010:06
IMF IFS
Norway
1980:01 – 2009:09
IMF IFS
Poland
2001:01 – 2010:06
IMF IFS
Portugal
1980:01 – 2010:06
IMF IFS
Romania
2005:04 – 2010:04
IMF IFS
Slovak Republic
2000:09 – 2009:06
IMF IFS
Slovenia
1991:12 – 2010:06
IMF IFS
Spain
1980:01 – 2010:06
IMF IFS
Sweden
1980:01 – 2010:06
IMF IFS
Switzerland
1980:01 – 2010:04
IMF IFS
Turkey
—
—
—
UK
Govt bond yield: Long-term
1980:01 – 2010:06
IMF IFS
Table 19: European government bond market
41
Middle East:
Middle East
Market indices
From – To
Source
Israel
MAOF 25 Index
1993:04 – 2010:12
Jordan
—
—
—
Table 20: Middle Eastern corporate equity market
Middle East
Market indices
From – To
Source
Israel
M:IL:0:1:0:1:0:0
2001:01 – 2010:05
Jordan
—
—
—
Table 21: Middle Eastern real estate market
Middle East
Market yields
From – To
Source
Israel
—
—
—
Jordan
—
—
—
Table 22: Middle Eastern government bond market
42
North America:
North
ica
Amer-
Market indices
From – To
Source
Bermuda
BSX Index
2004:11 – 2010:12
Canada
TSE S&P 300 Composite
1980:01 – 2010:12
Mexico
IPC General Index
1992:04 – 2010:12
US
NASDAQ Composite
1980:01 – 2010:12
Table 23: North American corporate equity market
North
ica
Amer-
Market indices
From – To
Source
Bermuda
—
—
—
Canada
—
—
—
Mexico
—
—
—
US
Q:US:0:2:1:3:0:0
1975:03 – 2010:06
Table 24: North American real estate market
North
ica
Amer-
Market yields
From – To
Source
Bermuda
—
—
—
Canada
1980M1 – 2010:06
IMF IFS
> 10 years
Mexico
1995:01 – 2010:05
IMF IFS
US
Govt bond yield: 10 year
1980:01 – 2010:06
IMF IFS
Table 25: North American government bond market
43
South America:
South
ica
Amer-
Market indices
From – To
Source
Argentina
Merval Index
1992:06 – 2010:12
Brazil
Bovespa Index
1992:04 – 2010:12
Chile
IGPA Index
1992:04 – 2010:12
Columbia
IGBC General Index
1998:10 – 2010:12
Peru
Lima Stock Index
1990:02 – 2010:12
Venezuela
Bursatil Index
1994:01 – 2010:12
Table 26: South American corporate equity market
South
ica
Amer-
Market indices
From – To
Source
Argentina
—
—
—
Brazil
—
—
—
Chile
—
—
—
Columbia
—
—
—
Peru
—
—
—
Venezuela
—
—
—
Table 27: South American real estate market
South
ica
Amer-
Market yields
From – To
Source
Argentina
—
—
—
Brazil
—
—
—
Chile
—
—
—
Columbia
—
—
—
Peru
—
—
—
Venezuela
1984:01 – 2009:12
IMF IFS
Table 28: South American government bond market
44
World:
World
Commodity prices
From – To
Source
World
Aluminium
1987:09 – 2010:12
World
Cacao
1981:02 – 2010:12
World
Coffee
2000:02 – 2010:12
World
Copper
1983:02 – 2010:12
World
Corn
1981:04 – 2010:12
World
Cotton
1989:02 – 2010:12
World
Crude oil
1983:01 – 2010:12
World
Gold
1980:01 – 2010:12
World
Silver
1981:02 – 2010:12
World
Sojabeans
1980:01 – 2010:12
World
Sugar
1980:01 – 2010:12
World
Wheat
1980:01 – 2010:12
Table 29: Commodity market
45
Universität Bayreuth
Rechts- und Wirtschaftswissenschaftliche Fakultät
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Chasing the Ghost of Global Vagabonding Bubbles

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