International asset pricing models: The case of ASEAN stock markets
The International Journal of Banking and Finance, 2008/09 Vol. 6. Number 1: 2008: 117-140 117
INTERNATIONAL ASSET PRICING
MODELS: THE CASE OF ASEAN
STOCK MARKETS
Chee-wooi Hooy and Kim-leng Goh
University Science Malaysia and
The University of Malaya
Abstract
This paper is about the role of economic grouping as it affects international
capital asset pricing models, ICAPM. The conventional ICAPM is extended
to include the economic grouping, regional and world factors. Inclusion of the
economic grouping factor increases the explanatory power of the asset pricing
models. Data on ASEAN (Indonesia, Malaysia, Philippines, Singapore and
Thailand) stock markets are used in tests of the proposed models. The economic
grouping factor turned out to be most important while the regional factor is least
important for asset pricing in these stock markets. While four of the markets have
higher systematic risk exposure to the economic group, the Singapore market,
the largest market, exhibits higher exposure to world risk. The segmentation of
emerging markets offers a possible explanation for these results.
Keywords: CAPM, GARCH, integration, market risk, trading bloc
JEL classification: F360, G120
1. Introduction
The decade of the 1990s witnessed a surge in regional trade agreements.1 Some
proponents of trade regionalism argue that, since trading blocs involve lesser
number of participants in the process of liberalization, blocs offer a more efficient
way of moving towards globalization. Frankel et al. (1995) and Frankel and
Wei (1998) suggest that the recent trade regionalisms are likely to be welfare-
improving. The formation of regional trading blocs not only induces intra-trade
among the member countries leading to higher economic integration, but also
stimulates active capital mobilization through FDI and portfolio investment flows
across capital markets. This is usually supported by the regulation and policy
cooperation in the financial system and the increased likelihood of coordination
in monetary and exchange rate policies. The experience of European countries
1 http://www.cid.harvard.edu/cidtrade/issues/regionalism.html.
IJBF
118 The International Journal of Banking and Finance, 2008/09 Vol. 6. Number 1: 2008: 117-140
in the formation of the European Union (EU) provides a supporting case
(Fratzscher, 2002).
When the formation of a trading bloc opens up trade opportunities, newly
created economic activities boost prosperity of the trading region while also
attracting capital inflows from member countries. What started as a trading bloc
often leads to wider cooperation among eonomic grouping. Improved liquidity
and market depth encourage monetary integration and a closer economic linkage
among the member countries, in particular in the financial sector.
Many studies in the literature focused on the issues of portfolio diversification,
market liberalization and contagion effect of an economic or financial crisis in
their analysis of stock market linkages among member countries of groups. One
of the early studies is by Lessard (1973) who investigated the diversification
opportunity in an investment union formed by four Latino countries. That study
documented evidence that the stock markets of the union members have closer
relation compared to that of non-member countries. The formation of the EU
has generated research interests on its capital markets some two decades later.
Akdogan (1992), for example, found evidence of integration among the stock
markets of EU members, suggesitng increasing relaxation of capital controls
among the union members. That result is further supported by studies of Johnson
and Soenen (1993) and Johnson et al. (1994) using covariance and correlation
analysis.
A number of studies investigated linkages of markets in other regional blocs.
Soydemir (2000) documented that the Mexican stock market is weakly linked to
the markets of Argentina and Brazil, but has strong ties with the US market. The
result reveals the simple fact of different trading bloc memberships – Mexico is
a member of NAFTA (North American Free Trade Agreement) that includes the
US, while the other two Latino markets are members of MERCOSUR (Mercado
Comun del Cono Sur). The paper concludes that stock market interdependence
is a result of economic convergence. Similarly, Chen et al. (2002) established
evidence of a long-run equilibrium relationship among six Latino markets,
attributing their findings to the formation of MERCOSUR. Although Mexico is
included in their analysis, the degree of exogeneity of its market is higher, and
has a weaker linkage with the other markets. More recently, Johnson and Soenen
(2003) reported significant simultaneous responses that exist between Canada
and Mexico (both NAFTA members) and among Argentina, Brazil, Chile (which
are members of MERCOSUR) and Peru.
The literature provides clear indication that stock markets are systematically
linked to their counterparts belonging to the same economic grouping. Such
linkages would in turn affect the way assets are priced in these markets. On the
latter aspect, however, there is little evidence in the literature documenting how
economic grouping affects the asset pricing mechanism.
This study investigated the economic grouping effects on asset pricing in
the case of ASEAN (Association of Southeast Asian Nations). The ASEAN Free
Trade Area, or AFTA, was established with the aim of promoting intra-regional
trade. Subsequent developments led to wider economic cooperation among
The International Journal of Banking and Finance, 2008/09 Vol. 6. Number 1: 2008: 117-140 119
ASEAN members. The concept of an ASEAN Investment Area was endorsed
in order to lower barriers to intra-regional investment, to liberalize regulations,
and to streamline incentives to be offered to boost regional investments. Efforts
are also underway for moving towards a higher degree of monetary and financial
integration by fostering closer linkages through cross-border movements within
the ASEAN securities markets.2 In the capital market, for example, the FTSE/
ASEAN Index series was recently launched as an indicator to the performance
of the five oldest stock markets in ASEAN, i.e., the stock markets of Indonesia,
Malaysia, Philippines, Singapore and Thailand. This study includes these five
countries, which are also the founding members of ASEAN. The different size
and the degree of openness of the five markets offer an interesting case study.
The markets of Malaysia and Singapore are relatively larger in size, matching
the ratio of market capitalization to GDP of the markets in some industrialized
countries, and their degree of financial openness is also much higher (Rillo,
2004) compared to the other three markets. By these measurements, Singapore
has the most developed market.
This study also proposes a direct modeling of the economic grouping impact
using the international asset pricing model (ICAPM), while accounting for the
time-varying nature of asset prices. The contribution of this paper is threefold.
First, we show that the international asset pricing model, to which an economic
grouping factor is added, has a higher explanatory power than the conventional
ICAPM models. Second, a dynamic analysis adopted in this study indicates that
the pricing mechanism in a more developed market within the economic group
is far more stable. Lastly, this study offers new evidence that the stock markets
of ASEAN have a higher tendency to converge within the economic group than
to the world market.
The rest of this paper is organized as follows. Section 2 introduces the
modified ICAPM model to study economic grouping framework and the
methodology for analysis. Section 3 reports the results and Section 4 concludes
the paper.
2. Models and Methodology
The ICAPM offers a theoretical framework for the pricing of risky assets in a
fully integrated world of financial markets. The conditional expected return of
a national equity market is exposed to the movement in returns of the world
portfolio given by the following process:
(1)
where R
it
and R
wt
represent the returns of market-i and global portfolio,
respectively, is the world risk-free rate, and all expectations are taken with
respect to the information set available at time t-1, ; Ω
t-1
. This version of one-
2 http://www.ASEAN.org.
i );|R,Rcov(R)|R(E tWtitWtFttit ∀�=−� −− 11 δ
120 The International Journal of Banking and Finance, 2008/09 Vol. 6. Number 1: 2008: 117-140
factor ICAPM assumes that the expected excess return of an individual market
above an international risk-free rate is proportional to the country specific and
non-diversifiable risk in the world market. The global portfolio return is the only
source of systematic risk that affects the return of each individual market in
model (1).
Some researchers including Errunza and Losq (1985), Campbell and
Hamao (1992), Davidson et al. (2003) and Bekaert et al. (2005) have added the
returns of dominant markets in the region into the ICAPM to reflect the regional
effects. In this paper, the region refers to Asia. We consider a two-factor model
given as:
(2)
where R
Rt
represents the return to a regional portfolio. Apart from exposure to
the world market risk, model (2) includes movements in the markets within the
region as an additional source of systematic risk that affects the return of each
individual market. Although the regional influence is included, the effect due to
formation of economic groupings is not considered in the two-factor model. We
propose a three-factor model specified as follows:
(3)
where R
Gt
, the returns to the portfolio of the economic group, provides a new
factor to capture the risk exposure to the grouping where country-i is a member.
Following the above discussion, a number of different versions of ICAPM
are obtained. For ease of exposition, the conditional expectation operation is
dropped. Three versions of one-factor ICAPM model are considered. First is the
version that relates to the world market systematic risk that is given by:
(4)
where ER
i
denotes the excess return of market-i and ER
W
is the global
portfolio excess return. The coefficient β
i
W captures the systematic risk of market-
i due to exposure to the world market, as suggested in model (1). The other two
single-factor models account for the exposure to market risk of the region and
market risk of the economic grouping. These models are stated as
(5)
and
(6)
where ER
R
and ER
G
represent the excess returns of the regional portfolio
and economic grouping portfolio, respectively. Henceforth, we refer to models
(4), (5) and (6) as the W-CAPM, R-CAPM and G-CAPM, respectively.
i );|R,R(Cov)|R,R(Cov R)|R(E tWtitWttRtitRtFttit ∀�+�=−� −−− 111 δδ
)|R,R(Cov)|R,R(Cov R)|R(E tRtitRttGtitGtFttit 111 −−− �+�=−� δδ
i );|R,R(Cov tWtitWt ∀�+ −1δ
itWt
W
iiit ERER εβα ++=
itRt
R
iiit ERER εβα ++=
itGt
G
iiit ERER εβα ++=
The International Journal of Banking and Finance, 2008/09 Vol. 6. Number 1: 2008: 117-140 121
Three possible specifications of two-factor ICAPM can be formed by
including the exposure to market risks of the region and world, economic
grouping and world, and, economic grouping and region. The specifications of
the two-factor models are as follows:
(7)
(8)
(9)
The models are referred to as RW-CAPM, GW-CAPM and GR-CAPM,
respectively in this paper.
The three-factor ICAPM model incorporates the economic grouping,
regional and global effects. The model, denoted as GRW-CAPM, is as follows:
(10)
We conduct a preliminary analysis of models (4) to (10) using OLS estimation in
a static framework. The best model is selected based on the Akaike information
criterion (AIC) and Schwarz criterion (SC). The parameter stability of the
selected model is examined using the CUSUM of squares test (Brown et al.,
1975) and the N-step forecast test. Evidence of recursive residuals outside the
error bounds determined at the 5 per cent level is taken to indicate parameter or
variance instability.
The selected model is then re-estimated using the generalized autoregressive
conditional heteroscedasticity (GARCH) model of Bollerslev (1986) to account
for temporal dependence in unconditional residuals which can be induced by
time-varying volatility. The conditional expectation underlying models (1) to (3)
is stated as follows:
where, the conditional variance σ2
it
follows the specification which can be
written as:
(11)
This simple specification of GARCH(1,1) is found to be generally sufficient for
empirical modeling (Engle and Ng, 1993). To account for non-normal conditional
residual distribution, we apply the robust consistent variance-covariance estimator
suggested by Bollerslev and Wooldridge (1992). A dynamic framework is used
whereby the model is estimated using rolling regression method. The window
is set at 3 years.
This study is based on monthly data collected for the period January 1988
and November 2004. The Morgan Stanley Capital International (MSCI) Country
itWt
W
iRt
R
iiit ERERER εββα +++=
itWt
W
iGt
G
iiit ERERER εββα +++=
itRt
R
iGt
G
iiit ERERER εββα +++=
itWt
W
iRt
R
iGt
G
iiit ERERERER εβββα ++++=
),(N~| it1-tit
20 σε �
2
1
2
1
2
−− ++= t,it,iit βσαεωσ
122 The International Journal of Banking and Finance, 2008/09 Vol. 6. Number 1: 2008: 117-140
Index is used in the computation of returns of the individual country. The world
portfolio is proxied by the MSCI All Country World Index, a free float-adjusted
market capitalization index computed from the stock exchanges of 49 leading
markets. The MSCI All Country Asia Index based on indices of 12 leading Asian
markets is used to compute the returns on the regional portfolio. The return on
the portfolio of the ASEAN group for a member country is computed from an
equal weighted market index of the remaining four members. The three-month
Treasury bill rates of US, downloaded from the website of The Federal Reserve,
represent the risk-free rate used in this study.
3. Results
Table 1: presents some summary statistics and the correlation matrix of the
market returns.
Table 1: Summary Statistics and Correlation Matrix of Returns
Summary
Statistics
Indonesia Malaysia Philippines Singapore Thailand Region World
Mean 0.004 0.004 0.002 0.005 0.002 -0.001 0.005
Std. Dev. 0.149 0.093 0.098 0.073 0.121 0.063 0.042
Skewness 0.422 -0.207 -0.009 -0.478 -0.381 0.002 -0.566
Kurtosis 7.042 6.415 4.609 5.152 4.628 3.468 3.785
Jarque-Bera 144.206 100.091 21.910 46.912 27.319 1.855 16.045
(p-value) (0.000) (0.000) (0.000) (0.000) (0.000) (0.396) (0.000)
No. of
Observations
203 203 203 203 203 203 203
Correlation Indonesia Malaysia Philippines Singapore Thailand Region World
Indonesia 1
Malaysia 0.479 1
Philippines 0.500 0.549 1
Singapore 0.505 0.658 0.617 1
Thailand 0.466 0.566 0.634 0.655 1
Region 0.231 0.391 0.348 0.528 0.438 1
World 0.261 0.424 0.409 0.622 0.467 0.785 1
Note: Std. Dev. - standard deviation.
With the exception of the regional returns, the Jarque-Bera test rejects the
normality assumption of the unconditional distribution of all the series. The
returns of the Indonesian market have the highest standard deviation while the
return variability of the Singapore market is the lowest. The ASEAN markets
The International Journal of Banking and Finance, 2008/09 Vol. 6. Number 1: 2008: 117-140 123
experienced more volatile movements than the global market. All correlation
coefficients are positive, indicating co-movements of the same direction.
Malaysia and Singapore are the most closely related markets compared to
other markets. The Indonesian market has the lowest correlation not only with
its ASEAN counterparts, but also with the regional and world markets. Being
the most developed and liberalized stock market, the Singapore market has the
highest correlation with the regional and world markets.
3.1. Static Estimation
The results of the OLS estimation of the W-, R- G-, RW-, GW-, GR- and GRW-
CAPM models stated in equations (4) to (10) are reported in Table 2.
Note that the economic grouping factor is significant at 1% in all the
models across all five countries. The inclusion of the economic grouping factor
also increases the adjusted-R2, i.e., models with this factor has better explanatory
power than models without. The world and regional returns are not always
significant in explaining returns of the individual markets. While regional returns
are significant in the one-factor R-CAPM model, they are not significant with
the incorporation of the global returns in the RW-CAPM model. Similarly, the
regional factor is also not significant in the GRW-CAPM model. This suggests
that the world market movements have higher influence on the ASEAN stock
markets compared to regional dynamics. The economic grouping dynamics
have higher impact than the world market movements. Of the GRW-CAPM
models, the economic grouping is always significant, but the world factor is only
significant in two out of the five models.
The magnitude of the betas suggests that the economic grouping factor
has a dominant role in equity pricing of the ASEAN markets. The presence of
economic grouping factor reduces the magnitude of the world and regional betas
when the one-factor model is extended to the two- and three-factor models.
The exposure to systematic risks due to economic grouping is also consistently
higher than those due to the regional and world factors. The only exception
is Singapore, where exposure to the world factor is higher than the economic
grouping factor.
The above findings are further supported by the model selection result.
The GW-CAPM model outperforms the other models in four of the markets,
while the GR-CAPM setting is the preferred model for Thailand. The results
are indicative that inclusion of the economic grouping returns has enhanced the
goodness-of-fit and explanatory power of the conventional ICAPM model. The
diagnostic results, however, suggest that the OLS estimation suffers from ARCH
effects. This problem is taken into account in the next section.
Two tests are conducted to show that the selected models experienced
parameter instability. The results of the CUSUM of squares and N-step
forecast tests are illustrated in Figure 1. Both the tests consistently show that
the parameters in the selected models are not stable over time in all but the
Philippine market. Some instability occurred in the early 1990s, but instability is
most predominantly around the period of the 1997 financial crisis. To deal with
this problem, the rolling window estimation is conducted.
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Table 2: ICAPM Models-Static Estimations
Model Constant Group Region World Q(12) Q2(12) Normality ARCH LM F Adj R2 AIC SC
Indonesia
W-CAPM 0.001 0.778 24.627 32.905 180.521 4.026 31.378 0.131 -1.040 -1.007
(0.889) (0.000)*** (0.017)** (0.001)*** (0.000)*** (0.000)*** (0.000)***
R-CAPM 0.006 0.621 20.557 28.920 156.448 3.627 27.277 0.115 -1.022 -0.989
(0.531) (0.000)*** (0.057)* (0.004)*** (0.000)*** (0.000)*** (0.000)***
G-CAPM 0.001 0.937 15.509 40.940 418.920 7.695 119.114 0.369 -1.360 -1.327
(0.867) (0.000)*** (0.215) (0.000)*** (0.000)*** (0.000)*** (0.000)***
RW-CAPM 0.002 0.170 0.601 23.293 31.813 181.006 3.950 15.871 0.128 -1.032 -0.983
(0.807) (0.557) (0.043)** (0.025)** (0.001)*** (0.000)*** (0.000)*** (0.000)***
GW-CAPM 0.004 1.147 -0.391 13.693 41.485 374.164 8.270 63.281 0.381 -1.375 -1.326
(0.679) (0.000)*** (0.027)** (0.321) (0.000)*** (0.000)*** (0.000)*** (0.000)***
GR-CAPM 0.001 1.095 -0.266 13.072 41.079 375.583 8.198 62.218 0.377 -1.369 -1.320
(0.881) (0.000)*** (0.050)** (0.364) (0.000)*** (0.000)*** (0.000)*** (0.000)***
GRW-CAPM 0.003 1.152 -0.070 -0.323 13.359 41.442 370.343 8.314 42.034 0.379 -1.366 -1.300
(0.708) (0.000)*** (0.749) (0.248) (0.343) (0.000)*** (0.000)*** (0.000)*** (0.000)***
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Malaysia
W-CAPM 0.000 0.876 31.640 167.200 59.203 7.642 115.337 0.361 -2.105 -2.072
(0.030)** (11.795) (0.002)*** (0.000)*** (0.000)*** (0.000)*** (0.000)***
R-CAPM 0.006 0.705 21.594 113.650 32.372 6.126 98.538 0.326 -2.051 -2.018
(0.344) (0.000)*** (0.042)** (0.000)*** (0.000)*** (0.000)*** (0.000)***
G-CAPM 0.003 0.747 12.129 82.995 16.221 4.714 263.557 0.565 -2.489 -2.457
(0.596) (0.000)*** (0.435) (0.000)*** (0.000)*** (0.000)*** (0.000)***
RW-CAPM 0.002 0.222 0.644 26.060 153.060 53.899 7.384 59.162 0.365 -2.106 -2.058
(0.798) (0.120) (0.000)*** (0.011)** (0.000)*** (0.000)*** (0.000)*** (0.000)***
GW-CAPM 0.001 0.632 0.247 14.478 123.060 20.584 6.333 139.751 0.579 -2.516 -2.467
(0.813) (0.000)*** (0.017)** (0.271) (0.000)*** (0.000)*** (0.000)*** (0.000)***
GR-CAPM 0.003 0.646 0.195 16.734 115.290 21.925 5.977 139.419 0.578 -2.515 -2.466
(0.590) (0.000)*** (0.008)*** (0.160) (0.000)*** (0.000)*** (0.000)*** (0.000)***
GRW-CAPM 0.002 0.627 0.100 0.148 15.779 123.570 21.892 6.318 93.247 0.578 -2.510 -2.444
(0.722) (0.000)*** (0.402) (0.382) (0.202) (0.000)*** (0.000)*** (0.000)*** (0.000)***
Table 2: ICAPM Models-Static Estimations (continued)
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Table 2: ICAPM Models-Static Estimations (continued)
Model Constant Group Region World Q(12) Q2(12) Normality ARCH LM F Adj R2 AIC SC
Philippines
W-CAPM -0.004 1.040 17.229 13.430 2.440 1.062 144.366 0.415 -1.986 -1.953
(0.550) (0.000)*** (0.141) (0.339) (0.295) (0.395) (0.000)***
R-CAPM 0.003 0.799 13.036 6.769 0.256 0.493 105.099 0.340 -1.865 -1.832
(0.634) (0.000)*** (0.366) (0.873) (0.880) (0.917) (0.000)***
G-CAPM -0.001 0.894 10.113 25.129 0.121 2.270 329.290 0.619 -2.415 -2.382
(0.791) (0.000)*** (0.606) (0.014)** (0.941) (0.011)** (0.000)***
RW-CAPM -0.003 0.087 0.949 16.619 12.645 1.704 0.986 72.086 0.413 -1.977 -1.928
(0.611) (0.600) (0.000)*** (0.164) (0.395) (0.427) (0.464) (0.000)***
GW-CAPM -0.003 0.732 0.342 10.408 27.965 1.653 2.378 182.228 0.642 -2.472 -2.423
(0.497) (0.000)*** (0.000)*** (0.580) (0.006)*** (0.437) (0.007)*** (0.000)***
GR-CAPM -0.001 0.792 0.193 9.841 22.971 0.210 1.910 172.397 0.629 -2.437 -2.388
(0.803) (0.000)*** (0.006)*** (0.630) (0.028)** (0.900) (0.036)** (0.000)***
GRW-CAPM -0.004 0.737 -0.081 0.422 10.547 28.879 2.156 2.497 121.282 0.641 -2.464 -2.399
(0.432) (0.000)*** (0.531) (0.018)** (0.568) (0.004)*** (0.340) (0.005)*** (0.000)***
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Singapore
W-CAPM 0.001 0.948 12.226 101.370 36.305 6.307 293.565 0.592 -2.882 -2.849
(0.849) (0.000)*** (0.428) (0.000)*** (0.000)*** (0.000)*** (0.000)***
R-CAPM 0.007 0.741 12.061 59.878 6.159 5.096 204.807 0.502 -2.684 -2.652
(0.119) (0.000)*** (0.441) (0.000)*** (0.046)** (0.000)*** (0.000)***
G-CAPM 0.005 0.675 15.281 23.029 41.912 1.881 411.017 0.670 -3.095 -3.063
(0.205) (0.000)*** (0.226) (0.027)** (0.000)*** (0.039)** (0.000)***
RW-CAPM 0.002 0.138 0.804 10.676 93.288 32.790 6.304 148.372 0.593 -2.882 -2.833
(0.688) (0.164) (0.000)*** (0.557) (0.000)*** (0.000)*** (0.000)*** (0.000)***
GW-CAPM 0.002 0.454 0.503 16.363 47.638 45.667 2.775 329.416 0.765 -3.429 -3.380
(0.613) (0.000)*** (0.000)*** (0.175) (0.000)*** (0.000)*** (0.002)*** (0.000)***
GR-CAPM 0.005 0.507 0.345 25.006 40.655 76.921 2.978 283.661 0.737 -3.316 -3.268
(0.167) (0.000)*** (0.000)*** (0.015)** (0.000)*** (0.000)*** (0.001)*** (0.000)***
GRW-CAPM 0.002 0.452 0.040 0.464 17.547 47.577 49.807 2.828 218.899 0.764 -3.421 -3.355
(0.557) (0.000)*** (0.615) (0.000)*** (0.130) (0.000)*** (0.000)*** (0.001)*** (0.000)***
Table 2: ICAPM Models-Static Estimations (continued)
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Table 2: ICAPM Models-Static Estimations (continued)
Model Constant Group Region World Q(12) Q2(12) Normality ARCH LM F Adj R2 AIC SC
Thailand
W-CAPM -0.005 1.216 30.032 103.230 10.534 4.376 135.878 0.400 -1.612 -1.580
(0.538) (0.000)*** (0.003)*** (0.000)*** (0.005)*** (0.000)*** (0.000)***
R-CAPM 0.003 0.996 18.874 61.432 1.676 3.271 121.346 0.373 -1.568 -1.536
(0.694) (0.000)*** (0.092)* (0.000)*** (0.432) (0.000)*** (0.000)***
G-CAPM -0.002 1.101 16.263 36.122 297.860 3.102 309.758 0.605 -2.029 -1.996
(0.721) (0.000)*** (0.180) (0.000)*** (0.000)*** (0.001)*** (0.000)***
RW-CAPM -0.002 0.386 0.814 24.646 86.510 5.621 4.064 71.119 0.410 -1.623 -1.574
(0.751) (0.063)* (0.000)*** (0.017)** (0.000)*** (0.060)** (0.000)*** (0.000)***
GW-CAPM -0.004 0.908 0.390 15.154 41.754 148.087 3.378 169.663 0.625 -2.078 -2.029
(0.450) (0.000)*** (0.009)*** (0.233) (0.000)*** (0.000)*** (0.000)*** (0.000)***
GR-CAPM -0.002 0.917 0.339 14.570 44.631 144.553 3.879 172.567 0.629 -2.089 -2.040
(0.734) (0.000)*** (0.005)*** (0.266) (0.000)*** (0.000)*** (0.000)*** (0.000)***
GRW-CAPM -0.003 0.896 0.248 0.142 14.816 44.698 134.620 3.799 114.994 0.629 -2.082 -2.017
(0.617) (0.000)*** (0.116) (0.471) (0.252) (0.000)*** (0.000)*** (0.000)*** (0.000)***
Note: Figures in the parentheses are the p-values. * denotes significance at the 0.10 level; ** denotes significance at the 0.05 level; and *** denotes
significance at the 0.01 level. The coefficient significance test is based on the White (1980) heteroskedasticity consistent covariance estimates. AIC and
SC denotes the Akaike and Schwarz information criteria. The underlined values show the model chosen using adjusted (Adj) R2, AIC and SC. Q(12)
and Q2(12) are the LM tests for serial dependence in the residuals and squared residuals at 12 lags, respectively. Normality is the Jacque-Bera test for
normality. ARCH LM is the test for presence of ARCH effects at 12 lags. F indicates the overall test for model significance.
The International Journal of Banking and Finance, 2008/09 Vol. 6. Number 1: 2008: 117-140 129
Figure 1: Tests for Parameter Instability
CUSUM of Squares Test N-step Forecast Test
Indonesia
Malaysia
Philippines
Singapore
Thailand
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Table 3: Rolling Regression Estimates of the GW-CAPM Model for Indonesia
Constant Mean Group World Constant Variance ARCH GARCH Q(12) Q2(12) Normality ARCH LM Adj R2
88/1-90/12 0.012 0.422 -0.806 0.001 -0.112 1.183 11.397 12.177 8.458 0.335 0.156
(0.420) (0.100)*** (0.011)** (0.701) (0.572) (0.000)*** (0.495) (0.432) (0.015)** (0.963)
89/1-91/12 -0.013 1.416 -1.351 0.007 0.910 -0.047 10.782 3.082 6.805 0.479 0.128
(0.271) (0.000)*** (0.033)** (0.018)** (0.381) (0.007)*** (0.548) (0.995) (0.033)** (0.889)
90/1-92/12 -0.023 1.273 -0.724 0.001 -0.277 1.134 14.560 20.293 2.015 0.699 0.406
(0.007)*** (0.000)*** (0.000)*** (0.001)*** (0.208) (0.000)*** (0.266) (0.062)* (0.365) (0.727)
91/1-93/12 -0.019 0.666 0.203 0.000 -0.201 1.167 5.712 13.836 1.345 0.926 0.358
(0.066)* (0.000)*** (0.155) (0.000)*** (0.348) (0.000)*** (0.930) (0.311) (0.510) (0.554)
92/1-94/12 -0.005 0.535 0.567 0.002 -0.027 0.531 13.250 13.024 1.520 0.901 0.567
(0.637) (0.000)*** (0.005)*** (0.699) (0.800) (0.671) (0.351) (0.367) (0.468) (0.572)
93/1-95/12 0.003 0.907 0.169 0.000 0.333 0.595 14.430 17.321 0.303 0.912 0.516
(0.770) (0.000)*** (0.402) (0.639) (0.038)** (0.087)* (0.274) (0.138) (0.859) (0.564)
94/1-96/12 0.008 1.100 0.134 0.000 -0.166 1.070 6.654 7.642 1.987 0.883 0.657
(0.142) (0.000)*** (0.345) (0.445) (0.481) (0.006)*** (0.880) (0.812) (0.370) (0.585)
95/1-97/12 0.021 1.262 -0.147 0.000 1.286 0.056 11.733 7.999 1.965 0.965 0.626
(0.000)*** (0.000)*** (0.390) (0.120) (0.002)*** (0.558) (0.467) (0.785) (0.374) (0.527)
96/1-98/12 0.014 1.052 0.673 0.000 0.514 0.752 12.593 7.764 1.195 0.367 0.244
(0.070)* (0.000)*** (0.001)*** (0.835) (0.112) (0.001)*** (0.399) (0.803) (0.550) (0.951)
97/1-99/12 0.019 1.358 -0.137 0.002 0.473 0.594 18.482 17.371 1.015 0.772 0.350
(0.442) (0.000)*** (0.756) (0.408) (0.032)** (0.000)*** (0.102) (0.136) (0.602) (0.669)
98/1-00/12 -0.039 1.255 -0.633 -0.001 -0.082 1.048 13.353 14.038 1.795 1.084 0.318
(0.001)*** (0.000)*** (0.008)*** (0.000)*** (0.634) (0.000)*** (0.344) (0.298) (0.408) (0.450)
99/1-01/12 -0.007 1.459 -0.553 0.002 -0.083 0.853 6.882 6.210 1.929 0.532 0.532
(0.655) (0.000)*** (0.006)*** (0.416) (0.392) (0.005)*** (0.865) (0.905) (0.381) (0.853)
00/1-02/12 0.009 1.077 -0.318 0.002 -0.239 1.057 12.274 5.939 2.129 0.395 0.395
(0.512) (0.000)*** (0.012)** (0.318) (0.052)* (0.000)*** (0.424) (0.919) (0.345) (0.937)
01/1-03/12 0.020 0.802 -0.177 0.001 -0.259 1.131 4.433 7.066 0.890 0.411 0.154
(0.042)** (0.000)*** (0.033)** (0.000)*** (0.027)** (0.000)*** (0.974) (0.853) (0.641) (0.929)
02/1-04/11 0.022 0.982 -0.008 0.002 0.036 0.702 7.179 22.320 1.574 2.747 0.394
(0.156) (0.011)** (0.987) (0.801) (0.744) (0.500) (0.846) (0.034)** (0.455) (0.060)*
Full Sample 0.004 1.232 -0.258 0.000 0.306 0.763 8.348 18.773 356.015 7.839 0.359
(0.524) (0.000)*** (0.094)* (0.500) (0.027)** (0.000)*** (0.757) (0.094)* (0.000)*** (0.000)***
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Constant Mean Group World Constant Variance ARCH GARCH Q(12) Q2(12) Normality ARCH LM
Adj R2
88/1-90/12 0.001 0.364 0.827 0.000 -0.219 1.124 11.312 6.797 0.603 0.631 0.619
(0.894) (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.502) (0.871) (0.740) (0.780)
89/1-91/12 0.002 0.440 0.573 0.000 0.216 0.544 13.432 7.770 1.067 0.210 0.709
(0.706) (0.000)*** (0.001)*** (0.528) (0.292) (0.295) (0.338) (0.803) (0.586) (0.994)
90/1-92/12 0.001 0.595 0.361 0.000 -0.223 1.149 6.748 10.178 1.335 0.981 0.771
(0.765) (0.000)*** (0.000)*** (0.000)*** (0.001)*** (0.000)*** (0.874) (0.600) (0.513) (0.516)
91/1-93/12 0.008 0.646 0.242 0.001 -0.141 0.592 13.635 5.892 0.468 0.371 0.708
(0.000)*** (0.000)*** (0.036)** (0.000)*** (0.336) (0.010)** (0.325) (0.921) (0.792) (0.949)
92/1-94/12 0.000 0.809 0.106 0.001 0.071 0.485 19.398 10.390 0.512 0.463 0.640
(0.971) (0.000)*** (0.682) (0.634) (0.605) (0.614)*** (0.079)* (0.582) (0.774) (0.899)
93/1-95/12 -0.004 1.037 0.174 0.000 0.931 0.216 26.146 14.322 0.951 0.651 0.606
(0.259) (0.000)*** (0.136) (0.128) (0.002)*** (0.049)** (0.010)** (0.281) (0.622) (0.764)
94/1-96/12 0.005 0.615 0.353 0.000 -0.175 1.008 5.369 10.832 1.454 0.570 0.530
(0.312) (0.000)*** (0.000)*** (0.000)*** (0.039)** (0.000)*** (0.945) (0.543) (0.483) (0.826)
95/1-97/12 0.010 0.888 0.056 0.000 0.050 1.187 10.460 9.527 1.040 0.564 0.643
(0.225) (0.000)*** (0.808) (0.000)*** (0.779) (0.000)*** (0.576) (0.657) (0.594) (0.831)
96/1-98/12 0.012 0.842 -0.185 0.000 0.253 0.854 8.034 7.032 1.006 0.325 0.535
(0.257) (0.000)*** (0.475) (0.946) (0.258) (0.000)*** (0.782) (0.856) (0.605) (0.967)
97/1-99/12 0.000 1.041 -0.713 0.001 -0.205 1.106 8.320 13.270 1.413 0.717 0.563
(0.969) (0.000)*** (0.009)*** (0.000)*** (0.089)* (0.000)*** (0.760) (0.350) (0.493) (0.712)
98/1-00/12 0.003 0.794 -0.637 0.007 -0.272 0.684 8.041 12.549 0.002 0.659 0.297
(0.768) (0.000)*** (0.011)** (0.000)*** (0.015)** (0.000)*** (0.782) (0.403) (0.999) (0.758)
99/1-01/12 0.033 0.400 0.380 0.000 -0.116 1.143 10.719 15.445 1.147 1.465 0.221
(0.001)*** (0.000)*** (0.001)*** (0.000)*** (0.642) (0.000)*** (0.553) (0.218) (0.564) (0.267)
00/1-02/12 0.023 0.325 0.411 0.000 -0.143 1.071 7.696 14.116 1.846 1.583 0.317
(0.001)*** (0.000)*** (0.000)*** (0.166) (0.642) (0.006)*** (0.808) (0.293) (0.397) (0.227)
01/1-03/12 0.019 0.418 0.317 0.002 -0.156 0.354 3.663 6.761 0.865 0.377 0.552
(0.013)** (0.000)*** (0.013)** (0.309) (0.059)* (0.624) (0.989) (0.873) (0.649) (0.946)
02/1-04/11 -0.002 0.310 0.551 0.001 0.232 0.166 8.658 7.758 1.412 0.492 0.683
(0.747) (0.008)*** (0.000)*** (0.368) (0.181) (0.813) (0.732) (0.804) (0.494) (0.878)
Full Sample 0.002 0.522 0.398 0.000 0.159 0.797 7.872 9.846 0.700 0.722 0.565
(0.654) (0.000)*** (0.000)*** (0.270) (0.042)** (0.000)*** (0.795) (0.629) (0.705) (0.729)
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Table 5: Rolling Regression Estimates of the GW-CAPM Model for Philippines
Constant Mean Group World Constant Variance ARCH GARCH Q(12) Q2(12) Normality ARCH LM
Adj R2
88/1-90/12 -0.010 0.541 0.274 0.003 -0.141 0.567 12.357 19.316 2.794 1.548 0.229
(0.198) (0.000)*** (0.010)** (0.177) (0.018)** (0.116) (0.417) (0.081)* (0.247) (0.239)
89/1-91/12 0.008 0.616 0.385 0.003 -0.137 0.618 15.643 15.265 6.775 0.882 0.399
(0.363) (0.000)*** (0.004)*** (0.015)** (0.056)* (0.002)*** (0.208) (0.227) (0.034)** (0.585)
90/1-92/12 0.009 0.738 0.424 0.003 -0.096 0.543 7.235 3.996 5.634 1.360 0.570
(0.320) (0.000)*** (0.030)** (0.327) (0.085)** (0.438) (0.842) (0.984) (0.060)* (0.309)
91/1-93/12 0.014 1.091 0.122 0.001 -0.397 1.077 9.193 13.672 2.164 0.935 0.626
(0.011)** (0.000)*** (0.303) (0.147) (0.014)** (0.000)*** (0.686) (0.322) (0.339) (0.548)
92/1-94/12 0.012 1.078 0.001 0.001 -0.054 0.766 15.482 16.378 1.347 1.533 0.698
(0.222) (0.000)*** (0.997) (0.797) (0.738) (0.460) (0.216) (0.175) (0.510) (0.244)
93/1-95/12 -0.004 1.358 -0.490 0.001 -0.310 1.004 14.182 17.805 0.450 1.361 0.750
(0.367) (0.000)*** (0.000)*** (0.000)*** (0.001)*** (0.000)*** (0.289) (0.122) (0.799) (0.308)
94/1-96/12 0.007 1.115 -0.292 0.000 -0.189 1.117 18.227 12.471 0.101 1.342 0.677
(0.213) (0.000)*** (0.015)** (0.000)*** (0.352) (0.000)*** (0.109) (0.409) (0.951) (0.317)
95/1-97/12 -0.012 0.916 0.302 0.000 -0.032 1.139 10.994 5.671 0.066 0.239 0.542
(0.227) (0.000)*** (0.271) (0.320) (0.931) (0.000)*** (0.529) (0.932) (0.967) (0.990)
96/1-98/12 0.009 0.796 0.210 0.000 -0.119 1.142 11.632 6.463 0.296 0.283 0.640
(0.332) (0.000)*** (0.372) (0.551) (0.563) (0.000)*** (0.476) (0.891) (0.862) (0.980)
97/1-99/12 -0.023 0.610 0.671 0.006 -0.104 0.044 16.154 7.253 0.151 0.501 0.667
(0.055)* (0.000)*** (0.007)*** (0.334) (0.319) (0.969) (0.184) (0.840) (0.927) (0.874)
98/1-00/12 -0.028 0.588 0.908 0.000 -0.194 1.166 8.545 9.897 1.139 2.177 0.696
(0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.090)** (0.000)*** (0.741) (0.625) (0.566) (0.104)
99/1-01/12 -0.034 0.663 0.521 0.002 0.662 -0.300 6.669 14.126 2.244 0.541 0.713
(0.000)*** (0.000)*** (0.000)*** (0.017)** (0.002)*** (0.125) (0.879) (0.293) (0.326) (0.847)
00/1-02/12 -0.016 0.790 0.317 0.005 0.659 -0.659 10.109 10.568 0.951 0.638 0.595
(0.000)*** (0.000)*** (0.000)*** (0.001)*** (0.000)*** (0.000)*** (0.606) (0.566) (0.622) (0.774)
01/1-03/12 -0.026 0.934 0.342 0.000 -0.203 1.177 12.926 5.538 1.830 0.348 0.610
(0.003)*** (0.000)*** (0.009)*** (0.000)*** (0.189) (0.000)*** (0.374) (0.938) (0.401) (0.959)
02/1-04/11 -0.023 0.912 -0.034 0.001 -0.305 1.190 12.214 10.476 1.857 0.826 0.551
(0.001)*** (0.000)*** (0.851) (0.000)*** (0.209) (0.000)*** (0.429) (0.574) (0.395) (0.628)
Full Sample -0.003 0.732 0.341 0.003 -0.004 0.334 10.383 27.781 1.656 2.380 0.637
(0.493) (0.000)*** (0.000)*** (0.938) (0.934) (0.969) (0.582) (0.006)*** (0.437) (0.007)***
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Table 6: Rolling Regression Estimates of the GW-CAPM Model for Singapore
Constant Mean Group World Constant Variance ARCH GARCH Q(12) Q2(12) Normality ARCH LM
Adj R2
88/1-90/12 0.002 0.378 0.615 0.001 -0.207 0.770 16.504 11.278 1.042 0.594 0.720
(0.691) (0.000)*** (0.000)*** (0.004)*** (0.284) (0.002)*** (0.169) (0.505) (0.594) (0.808)
89/1-91/12 0.006 0.400 0.631 0.000 0.337 0.507 12.093 8.062 3.799 0.501 0.816
(0.286) (0.000)*** (0.000)*** (0.484) (0.467) (0.300) (0.438) (0.780) (0.150) (0.874)
90/1-92/12 0.003 0.427 0.567 0.000 -0.167 1.070 9.140 5.987 0.636 0.585 0.911
(0.330) (0.000)*** (0.000)*** (0.000)*** (0.003)*** (0.000)*** (0.691) (0.917) (0.727) (0.814)
91/1-93/12 0.004 0.427 0.570 0.000 -0.114 1.207 9.054 5.107 1.691 0.730 0.877
(0.245) (0.000)*** (0.000)*** (0.000)*** (0.696) (0.000)*** (0.698) (0.954) (0.429) (0.702)
92/1-94/12 0.002 0.399 0.569 0.000 -0.164 1.160 4.780 13.290 2.028 1.308 0.839
(0.571) (0.000)*** (0.000)*** (0.000)*** (0.399) (0.000)*** (0.965) (0.348) (0.363) (0.332)
93/1-95/12 0.004 0.512 0.470 0.000 -0.171 1.138 4.627 18.196 1.273 0.844 0.824
(0.262) (0.000)*** (0.000)*** (0.000)*** (0.233) (0.000)*** (0.969) (0.110) (0.529) (0.614)
94/1-96/12 -0.001 0.501 0.402 0.000 -0.185 1.144 14.895 14.963 0.421 1.363 0.778
(0.576) (0.000)*** (0.000)*** (0.000)*** (0.471) (0.000)*** (0.247) (0.243) (0.810) (0.308)
95/1-97/12 -0.012 0.261 0.825 0.000 0.034 1.169 12.082 15.700 1.042 1.129 0.696
(0.049)** (0.000)*** (0.000)*** (0.000)*** (0.888) (0.000)*** (0.439) (0.205) (0.594) (0.424)
96/1-98/12 -0.011 0.309 0.771 0.000 0.295 0.749 13.477 14.261 2.244 0.430 0.657
(0.103) (0.038)** (0.000)*** (0.293) (0.253) (0.000)*** (0.335) (0.284) (0.326) (0.919)
97/1-99/12 0.012 0.426 0.729 0.000 0.280 0.630 9.280 12.295 0.786 0.522 0.684
(0.143) (0.000)*** (0.000)*** (0.473) (0.286) (0.015)** (0.679) (0.422) (0.675) (0.860)
98/1-00/12 0.018 0.504 0.808 0.001 0.561 0.192 6.761 13.868 1.424 0.938 0.637
(0.002)*** (0.000)*** (0.000)*** (0.063)* (0.010)** (0.188) (0.873) (0.309) (0.491) (0.545)
99/1-01/12 0.006 0.372 0.409 0.004 0.280 -0.452 19.134 9.508 1.998 0.338 0.578
(0.467) (0.000)*** (0.004)*** (0.078)* (0.261) (0.254) (0.085)* (0.659) (0.368) (0.962)
00/1-02/12 0.002 0.239 0.432 0.001 1.144 -0.139 17.495 6.482 0.627 0.603 0.537
(0.693) (0.000)*** (0.000)*** (0.001)*** (0.001)*** (0.005)*** (0.132) (0.890) (0.731) (0.801)
01/1-03/12 -0.003 0.309 0.446 0.002 0.547 -0.384 9.632 12.749 0.610 0.520 0.644
(0.459) (0.000)*** (0.000)*** (0.004)*** (0.024)** (0.021)** (0.648) (0.388) (0.737) (0.862)
02/1-04/11 -0.001 0.402 0.567 0.000 -0.145 1.089 9.320 10.980 0.439 0.777 0.811
(0.709) (0.000)*** (0.000)*** (0.000)*** (0.493) (0.000)*** (0.675) (0.531) (0.803) (0.665)
Full Sample 0.002 0.401 0.572 0.000 0.174 0.799 11.058 10.061 3.139 0.864 0.759
(0.520) (0.000)*** (0.000)*** (0.217) (0.034)** (0.000)*** (0.524) (0.611) (0.208) (0.585)
13
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Table 7: Rolling Regression Estimates of the GW-CAPM Model for Thailand
Constant Mean Group World Constant Variance ARCH GARCH Q(12) Q2(12) Normality ARCH LM
Adj R2
88/1-90/12 0.005 0.676 0.844 0.002 -0.262 0.914 9.417 8.857 1.549 0.484 0.498
(0.504) (0.000)*** (0.000)*** (0.000)*** (0.054)* (0.000)*** (0.667) (0.715) (0.461) (0.886)
89/1-91/12 0.008 0.800 0.544 0.002 -0.379 0.829 14.638 17.344 2.027 0.564 0.620
(0.450) (0.000)*** (0.000)*** (0.000)*** (0.090)* (0.000)*** (0.262) (0.137) (0.363) (0.831)
90/1-92/12 0.003 0.884 0.316 0.001 -0.298 1.079 16.278 10.486 1.761 0.992 0.617
(0.668) (0.000)*** (0.094)* (0.000)*** (0.001)*** (0.000)*** (0.179) (0.573) (0.414) (0.509)
91/1-93/12 0.014 1.068 -0.118 0.002 -0.348 0.991 14.697 6.019 1.151 0.579 0.504
(0.061)* (0.000)*** (0.300) (0.000)*** (0.013)** (0.000)*** (0.258) (0.915) (0.562) (0.819)
92/1-94/12 0.000 1.075 -0.215 0.003 -0.265 0.564 22.854 17.516 0.550 0.805 0.638
(0.949) (0.000)*** (0.023)** (0.001)*** (0.001)*** (0.049)** (0.029)** (0.131) (0.759) (0.643)
93/1-95/12 -0.004 1.198 -0.161 0.000 -0.162 1.096 13.749 19.978 1.111 0.637 0.755
(0.291) (0.000)*** (0.057)* (0.000)*** (0.409) (0.000)*** (0.317) (0.068)* (0.574) (0.775)
94/1-96/12 -0.007 1.206 -0.116 0.000 -0.036 1.144 10.173 5.117 2.798 0.443 0.676
(0.226) (0.000)*** (0.386) (0.670) (0.763) (0.000)*** (0.601) (0.954) (0.247) (0.911)
95/1-97/12 -0.010 1.191 -0.182 0.000 -0.140 1.307 9.568 6.138 1.007 0.245 0.554
(0.017)** (0.000)*** (0.320) (0.000)*** (0.489) (0.000)*** (0.654) (0.909) (0.604) (0.989)
96/1-98/12 -0.049 0.641 1.532 0.009 -0.107 0.498 10.731 15.678 6.493 0.665 0.563
(0.000)*** (0.000)*** (0.005)*** (0.317) (0.138) (0.474) (0.552) (0.206) (0.039)** (0.753)
97/1-99/12 -0.042 0.680 1.348 0.008 -0.100 0.506 10.550 14.459 2.705 0.381 0.583
(0.003)*** (0.000)*** (0.009)*** (0.475) (0.157) (0.582) (0.568) (0.272) (0.259) (0.944)
98/1-00/12 -0.017 1.009 0.671 0.001 -0.174 0.945 20.034 8.504 1.490 0.674 0.539
(0.208) (0.000)*** (0.005)*** (0.000)*** (0.000)*** (0.000)*** (0.066)* (0.745) (0.475) (0.746)
99/1-01/12 -0.020 1.231 0.266 0.006 0.188 -0.185 60.674 6.622 0.938 0.760 0.704
(0.163) (0.000)*** (0.173) (0.238) (0.405) (0.829) (0.000)*** (0.882) (0.626) (0.679)
00/1-02/12 -0.002 1.162 0.172 0.000 -0.175 1.096 15.731 12.514 0.919 0.665 0.630
(0.832) (0.000)*** (0.208) (0.107) (0.278) (0.000)*** (0.204) (0.405) (0.632) (0.753)
01/1-03/12 0.022 0.677 0.407 0.000 -0.152 1.086 7.768 6.339 1.771 0.418 0.511
(0.016)** (0.000)*** (0.008)*** (0.305) (0.476) (0.000)*** (0.803) (0.898) (0.413) (0.925)
02/1-04/11 0.020 0.519 0.621 0.002 0.095 0.348 8.272 5.803 10.098 0.282 0.653
(0.044)** (0.004)*** (0.001)*** (0.632) (0.731) (0.763) (0.764) (0.926) (0.006)*** (0.979)
Full Sample 0.000 0.880 0.335 0.000 0.103 0.848 10.265 12.251 29.167 1.001 0.617
(0.972) (0.000)*** (0.007)*** (0.355) (0.115) (0.000)*** (0.593) (0.426) (0.000)*** (0.450)
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1
35 Table 8: Rolling Regression Estimates of the GR-CAPM Model for Thailand
Constant Mean Group Region Constant Variance ARCH GARCH Q(12) Q2(12) Normality ARCH LM
Adj R2
88/1-90/12 0.000 0.866 0.353 0.005 0.291 -0.458 20.305 9.956 1.463 1.637 0.430
(0.933) (0.000)*** (0.003)*** (0.005)*** (0.056)* (0.042)** (0.062)* (0.620) (0.481) (0.212)
89/1-91/12 0.018 0.858 0.359 0.001 -0.341 1.054 10.837 11.527 2.675 0.572 0.601
(0.023)** (0.000)*** (0.000)*** (0.000)*** (0.054)* (0.000)*** (0.543) (0.484) (0.262) (0.824)
90/1-92/12 0.011 0.943 0.159 0.001 -0.292 1.127 21.705 11.456 2.349 0.804 0.619
(0.145) (0.000)*** (0.090)* (0.000)*** (0.052)* (0.000)*** (0.041)** (0.490) (0.309) (0.644)
91/1-93/12 0.010 0.999 -0.074 0.002 -0.320 0.808 18.156 7.207 1.436 0.371 0.513
(0.144) (0.000)*** (0.204) (0.000)*** (0.000)*** (0.000)*** (0.111) (0.844) (0.488) (0.949)
92/1-94/12 0.008 1.234 -0.133 0.000 -0.020 1.050 12.102 13.001 0.494 0.673 0.642
(0.304) (0.000)*** (0.214) (0.000)*** (0.916) (0.000)*** (0.438) (0.369) (0.781) (0.747)
93/1-95/12 -0.006 1.224 -0.182 0.000 -0.162 1.099 17.121 26.717 1.592 0.840 0.769
(0.141) (0.000)*** (0.002)*** (0.000)*** (0.448) (0.000)*** (0.145) (0.008)*** (0.451) (0.617)
94/1-96/12 -0.011 1.148 -0.143 0.000 -0.081 1.178 7.821 6.170 2.210 0.529 0.671
(0.022)** (0.000)*** (0.108) (0.002)*** (0.316) (0.000)*** (0.799) (0.907) (0.331) (0.855)
95/1-97/12 -0.013 1.154 -0.194 0.000 -0.088 1.228 8.336 5.517 0.403 0.272 0.553
(0.001)*** (0.000)*** (0.045)** (0.714) (0.525) (0.000)*** (0.758) (0.938) (0.818) (0.983)
96/1-98/12 -0.032 0.725 0.948 0.001 -0.145 1.051 14.262 16.407 7.825 3.039 0.570
(0.005)*** (0.000)*** (0.000)*** (0.000)*** (0.270) (0.000)*** (0.284) (0.173) (0.020)** (0.038)**
97/1-99/12 -0.014 0.675 1.011 0.008 -0.129 0.552 12.756 17.292 3.743 0.879 0.590
(0.416) (0.000)*** (0.002)*** (0.031)** (0.134) (0.129) (0.387) (0.139) (0.154) (0.588)
98/1-00/12 -0.022 0.717 0.920 0.005 -0.145 0.607 11.455 4.333 0.949 0.766 0.602
(0.089)* (0.000)*** (0.000)*** (0.000)*** (0.001)*** (0.000)*** (0.490) (0.977) (0.622) (0.674)
99/1-01/12 -0.021 1.169 0.405 0.004 0.206 0.054 50.657 8.980 1.140 0.791 0.724
(0.134) (0.000)*** (0.039)** (0.231) (0.343) (0.939) (0.000)*** (0.705) (0.565) (0.654)
00/1-02/12 -0.015 1.274 0.273 0.010 0.419 -0.823 14.436 10.586 0.896 0.483 0.640
(0.082)* (0.000)*** (0.033)** (0.000)*** (0.000)*** (0.000)*** (0.274) (0.565) (0.639) (0.887)
01/1-03/12 0.018 0.575 0.524 0.000 -0.197 1.125 8.849 2.688 3.089 0.521 0.525
(0.049)** (0.000)*** (0.000)*** (0.416) (0.559) (0.003)*** (0.716) (0.997) (0.213) (0.861)
02/1-04/11 0.018 0.701 0.439 0.002 0.230 0.251 8.806 4.628 10.061 0.258 0.622
(0.054)* (0.000)*** (0.028)** (0.529) (0.285) (0.741) (0.719) (0.969) (0.007)*** (0.985)
Total 0.001 0.933 0.247 0.000 0.100 0.852 9.383 14.302 32.996 1.226 0.621
(0.857) (0.000)*** (0.010)** (0.357) (0.142) (0.000)*** (0.670) (0.282) (0.000)*** (0.268)
136 The International Journal of Banking and Finance, 2008/09 Vol. 6. Number 1: 2008: 117-140
-2
-1 . 5
-1
-0 . 5
0
0 . 5
1
1 . 5
2
8 8 / 1 -
9 0 /1 2
8 9 / 1 -
9 1 / 1 2
9 0 /1 -
9 2 / 1 2
9 1 /1 -
9 3 /1 2
9 2 / 1 -
9 4 /1 2
9 3 / 1 -
9 5 / 1 2
9 4 /1 -
9 6 / 1 2
9 5 /1 -
9 7 /1 2
9 6 / 1 -
9 8 /1 2
9 7 / 1 -
9 9 / 1 2
9 8 /1 -
0 0 / 1 2
9 9 /1 -
0 1 /1 2
0 0 / 1 -
0 2 / 1 2
0 1 / 1 -
0 3 / 1 2
0 2 /1 -
0 4 / 1 1
Eco n o m ic G ro up W orld
-2
-1 .5
-1
-0 .5
0
0 . 5
1
1 . 5
2
8 8 /1 -
9 0 /1 2
8 9 / 1 -
9 1 / 1 2
9 0 / 1 -
9 2 / 1 2
9 1 /1 -
9 3 / 1 2
9 2 /1 -
9 4 /1 2
9 3 / 1 -
9 5 / 1 2
9 4 / 1 -
9 6 / 1 2
9 5 /1 -
9 7 / 1 2
9 6 /1 -
9 8 /1 2
9 7 / 1 -
9 9 / 1 2
9 8 / 1 -
0 0 / 1 2
9 9 /1 -
0 1 / 1 2
0 0 /1 -
0 2 /1 2
0 1 / 1 -
0 3 /1 2
0 2 / 1 -
0 4 / 1 1
Econ o m ic G ro u p W o rld
3.2. Rolling Regression Estimation
The GW-CAPM model is reestimated using the GARCH(1,1) specification and
a rolling window of three years. A total of 15 windows are considered. The GR-
CAPM model is also estimated for Thailand following the model selection results
from the last section. The estimates are given in Tables 3 to 8. The diagnostic
results suggest that the models are generally well specified. The ARCH-LM
test and Q-statistic for the squared residuals indicate that the problem of ARCH
effects has been adequately dealt with. The Jarque-Bera normality test shows no
evidence against the normality assumption in most of the cases.
The economic grouping variable is significant in all 15 windows as well as
the full sample period for all the five markets. In contrary, the world factor, (and
regional factor for Thailand) is not always significant. For Singapore, however,
the exposure to world risk is significant for all the windows. For the other four
markets, it is interesting to note that the world market returns are not significant
in many instances prior to and during the 1997 crisis.
The betas obtained from the rolling estimates of the GW-CAPM model
are plotted in Figure 2. These estimates are generally not stable, thus supporting
the use of rolling windows. Singapore is the only market with stable betas, and
instability is only observed around the 1997-8 financial crisis years. Bigger
fluctuations are seen in the other markets, in particular, Indonesia. It is also
clear that the financial crisis caused higher instability in beta estimates of four
markets.
Figure 2: World and Economic Group Betas of the GW-CAPM Model
Indonesia
Malaysia
The International Journal of Banking and Finance, 2008/09 Vol. 6. Number 1: 2008: 117-140 137
-2
-1 .5
-1
-0 .5
0
0 . 5
1
1 . 5
2
8 8 /1 -
9 0 /1 2
8 9 / 1 -
9 1 / 1 2
9 0 / 1 -
9 2 / 1 2
9 1 /1 -
9 3 / 1 2
9 2 /1 -
9 4 /1 2
9 3 / 1 -
9 5 / 1 2
9 4 / 1 -
9 6 / 1 2
9 5 /1 -
9 7 / 1 2
9 6 /1 -
9 8 /1 2
9 7 / 1 -
9 9 / 1 2
9 8 / 1 -
0 0 / 1 2
9 9 /1 -
0 1 / 1 2
0 0 /1 -
0 2 /1 2
0 1 / 1 -
0 3 /1 2
0 2 / 1 -
0 4 / 1 1
Econ o m ic G ro u p W o rld
Figure 2: World and Economic Group Betas of the GW-CAPM Model
(continued)
Philippines
Singapore
Thailand
Risk exposures to the economic grouping and world generally move in opposite
direction. Markets with low world betas are likely to have a relatively high
exposure to economic grouping risks, and vice versa. Specifically, the exposure
to the economic grouping factor is relatively higher in Indonesia, Malaysia,
Philippines and Thailand except for a small number of windows. The Singapore
market has a different behaviour, where the exposure to the world factor is more
prominent. Nevertheless, the economic grouping beta for Singapore is only
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
88/1-
90/12
89/1-
91/12
90/1-
92/12
91/1-
93/12
92/1-
94/12
93/1-
95/12
94/1-
96/12
95/1-
97/12
96/1-
98/12
97/1-
99/12
98/1-
00/12
99/1-
01/12
00/1-
02/12
01/1-
03/12
02/1-
04/11
Eco no m ic G ro u p W o rld
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
88/1-
90/12
89/1-
91/12
90/1-
92/12
91/1-
93/12
92/1-
94/12
93/1-
95/12
94/1-
96/12
95/1-
97/12
96/1-
98/12
97/1-
99/12
98/1-
00/12
99/1-
01/12
00/1-
02/12
01/1-
03/12
02/1-
04/11
Eco n om ic G rou p W o rld
138 The International Journal of Banking and Finance, 2008/09 Vol. 6. Number 1: 2008: 117-140
slightly lower than the world beta, suggesting that the influence of the former
cannot be overlooked.
The five markets have different reactions to the financial crisis. While
Malaysia and Indonesia markets saw a drop in exposure to the world risk, the
magnitude of the world beta for the other three markets has increased during
around the financial crisis period.
These three markets re-anchored themselves to the global market but
returned to the pre-crisis position after 1999. The case of Malaysia, however,
is different. The exposure to the economic grouping risk has dropped since the
crisis. This supports the evidence provided by Goh et al. (2005) that degree of
exogeneity of the Malaysia market has increased within the ASEAN group since
the implementation of the capital control policy by the Malaysian government
in the last quarter of 1998. They show a reduction in the contemporaneous
movements between the Malaysia and the other four ASEAN stock markets
since the crisis.
4. Conclusion
This article tested different specifications of the ICAPM model and proposed the
economic grouping factor as an additional variable in the asset pricing. Using
data on five stock markets of ASEAN, the importance of exposure to systematic
risks in this economic group is shown. We found that the economic grouping
factor, if included, increases the explanatory power of the ICAPM model. Some
evidence is found that the exposure to world and regional risks has reduced in
importance when the economic grouping risks are taken into account.
This article also shows that the pricing mechanism is not stable over time.
The Singapore market, which is perhaps most developed market, exhibited
relatively stable pricing behaviour compared to any of the other four other
markets (Indonesia, Malaysia, Philippines and Thailand). The exposure to world
risk is generally higher in the Singapore market, but the other four markets
have higher exposure to the economic grouping risk. However, the effect of the
economic grouping factor on the Singapore market is rather sizeable and not
negligible.
Given that exposure to economic grouping behaviour is not be neglected
in international asset pricing model, the findings offer some explanation for
segmentation in emerging markets. The higher exposure to movement in returns
of the economic group comes together with the reduction in the impact of global
market movements on the individual markets, and hence there is lower degree of
integration into the world market.
Author statement: The submitting author is Kim-leng Goh: E-mail: klgoh@
um.edu.my. Author statement: The submitting author is Kim-leng Goh: E-
mail: klgoh@um.edu.my at the University of Malaya. The authors express their
gratitude for the comments of the reviewer and the assistance of the editors to
expedite the copy editing of the paper. The authors are jointly responsible for
any remaining errors.
The International Journal of Banking and Finance, 2008/09 Vol. 6. Number 1: 2008: 117-140 139
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International Journal of Banking and Finance
9-1-2008
International asset pricing models: The case of ASEAN stock markets
Chee-Wooi Hooy
Kim-Leng Goh
Recommended Citation