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Who has more influence on Asian Stock Markets around the SubpWho has more influence on Asian Stock Markets around the Subp
rime Mortgage Crisisrime Mortgage Crisis -- the U.S. or China?the U.S. or China?
Chien-Chung NiehChien-Chung Nieh Chao-Hsiang Yang Chao-Hsiang Yang* Yu-Sheng Kao* Yu-Sheng Kao****
January 7, January 7, 20112011
Professor of Department of Banking and Finance, Tamkang University, Taipei, Taiwan.Professor of Department of Banking and Finance, Tamkang University, Taipei, Taiwan.** Ph.D. student of Department of Banking and Finance, Tamkang University, Taipei, Taiwan. Ph.D. student of Department of Banking and Finance, Tamkang University, Taipei, Taiwan.**** Ph.D. student of Department of Banking and Finance, Tamkang University, Taipei, Taiwan. Ph.D. student of Department of Banking and Finance, Tamkang University, Taipei, Taiwan.
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AbstractAbstract
• Investigate the changes in the long-run asymmetric equilibrium relationships between the U.S. and China’s stock markets and six major Asian stock markets of Taiwan, Hong Kong, Singapore, Japan, South Korea and India around the subprime mortgage crisis by the Enders and Siklos (2001) asymmetric threshold co-integration model.
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• The main findings demonstrated that with the application of traditional symmetric co-integration tests of Engle and Granger (1987), the subprime mortgage crisis did not reinforce the co-movement trends between the U.S. and China’s markets and Asian markets. However, with the application of the Enders-Siklos threshold co-integration test, there was significant increase in these asymmetric co-integration relationships between them during the period of the subprime mortgage crisis.
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Four different approaches utilized to measure international shock Four different approaches utilized to measure international shock transmission effect by Dornbusch transmission effect by Dornbusch et alet al. (2000) and Forbes and Rigo. (2000) and Forbes and Rigobon (2001).bon (2001).
• Cross-market correlation coefficients (the change of common trend)• ARCH or GARCH frameworks (volatility spillover effect)• Co-integration techniques (the change of common trend)• Direct estimation of specific transmission mechanisms by using the Probit model.
Literature ReviewLiterature Review
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Researchers approaches Findings
King and Wadhwani (1990)
Lee and Kim (1993)
The correlation approach The cross-market correlations increased significantly among the U.S., the U.K., and Japanafter the U.S. stock market collapse in October 1987.
Cha and Oh (2000) The correlation approach The links between the developed markets and the Asian emerging markets had significantly intensified after the U.S. stock market collapse in 1987 and during the Asian Financial Crisis in 1997.
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Forbes and Rigobon (2002)
The correlation coefficients are conditional on market volatility.(heteroskedasticity).
There was virtually noincrease in unconditional correlation coefficients during the 1997 Asian Financial Crisis, 1994 Mexican devaluation, and 1987 U.S. stock market collapse.
Caporale et al. (2005)
The conditional variance bythe application of both heteroskedasticity and endogeneity
The existence of contagion within the stock markets in Hong Kong, Japan, South Korea, Singapore, Taiwan, and Malaysia during the 1997 Asian Financial Crisis.
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Hamao et al. (1990) The GARCH method The volatility spillovers of the stock indices from New York to Tokyo, London to Tokyo, and New York to London after the U.S. stock market collapse in 1987.
Sheng and Tu (2000) The Co-integration method The co-integration did not exist in the eleven Asian stock markets and U.S. stock market before the 1997 Asian FinancialCrisis, but it did during the financial crisis.
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• Co-integration relationship → a common trend. ↗ an upward status (positive impact)• asymmetric adjustments ↘ a downward status (negative impact) Li and Lam (1995), Koutmos (1998), and Chiang (2001) • What is the impact of the Subprime Mortgage Crisis from the U.S. stock
markets on the Asian stock markets during the period of the financial crisis?
• Exploration of these problems by the asymmetric threshold co-integration model.
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Nonlinear ESTAR Unit root test by Kapetanios et al.(2003) • The KSS nonlinear stationary test is based on detecting the presence of
non-stationarity against nonlinear but a globally stationary exponential smooth transition autoregressive model (ESTAR) process:
MethodologiesMethodologies
(1) )]exp(1[ 211 tttt YYY
• Kapetanios et al. (2003) follow Luukkonen et al. (1988) to compute a first-order Taylor series approximation to the
(2) T,.......... 2, 1, t,1
1
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tdt
P
iitt YYY
)]exp(1[ 21 tY under the null of 0
by the following auxiliary regression: , and approximate Eqn. (1)
Then, the null hypothesis and alternative hypothesis are expressed0 (non stationarity) against 0 (nonlinear stationarity).
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Enders and Siklos (2001) Threshold Co-integration Model
• The Enders and Siklos (2001) technique extended the Engle and Granger (1987) framework to test non-linear co-integration (Enders and Granger, 1998).
• Enders and Siklos (2001) modifies ε to allow for two types of asymmetric error corrections based on a co-integrating relationship as depicted in OLS.
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• Comparisons of Yi,t and Xt-1 : Yi,t : The variables of the Asian stock markets on period t. Xt-1 : The variables of the U.S. stock market (S&P 500 index) on period t-1.
The study of the co-integration relationships between the current Yi,t data of the six major Asian stock markets with the following Xt-1 data of the U.S. stock market. (Eun and Shim, 1989; Liu et al., 1998)
Equation (1) : The long-run equilibrium relationship between the U.S. and China and the six major Asian stock markets (Taiwan , Hong Kong, Singapore, Japan, Korea, India).
(3) 7 , .......... 2 ,1 ,110, iXY titti
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)4( )1(1
11211 tit
p
iittttt II
tI
tT 0
1
1
1
cif
cif
t
t
{
tM 0
1
1
1
rif
rif
t
t
{
candr : threshold values
TAR Model
M-TAR Model
, such that: is the Heaviside indicator function, where ],[ ttt MTI
Next, the residuals ε, are used in:
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• The threshold value is endogenously determined by using the Chan’s (1993) grid search method to find the consistent estimate of the threshold. This method arranges the values, in an ascending order and excludes the smallest and largest 15 percent, and the consistent estimate of the threshold is the parameter that yields the smallest residual sum squares (RSS) over the remaining 70 percent.
• We test the null hypothesis of no co-integration relationship by
(5), and test the null hypothesis of symmetric adjustment by (6)
(5) 0: 210 H
(6) : 210 H
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DataData
• This study chose the S&P500 index to represent the U.S. stock markets and the SSE Composite index to represent the China stock markets.
• The other Asian stock markets include Taiwan, Hong Kong, Singapore, Japan, Korea and India, and all observations are taken logarithm, and we only kept the data of synchronized trading days in all stock markets. (Hamao et al., 1990)
• The entire sample period : 2004/1/2 to 2010/3/31. The cutting point : March 13, 2007 (the time when the Subprime Mort
gage Crisis of the New Century Financial Corp took place. Gorton, 2008)
The period of “pre Subprime Mortgage Crisis” : 2004/1/2 to 2007/3/13. The period of “during the Subprime Mortgage Crisis” : 2007/3/14 to 2010/3/31.
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Empirical ResultsEmpirical Results
CF AFrCF AF r
CF AF r
Entire period Pre-subprime mortgage crisis During subprime mortgage crisis
Correlation Coefficient of Return 0.0881 0.0724 0.0948
Correlation Coefficient of Volatility of Return 0.4613** 0.0954 0.3792**
Engle-Granger Co-integration -0.704 -2.034 -0.319
Ender-Siklos Threshold Co-integration
4.058 1.589 -0.0187 4.346 1.776 0.0132 3.636 1.121 -0.0246
Relationships between the U.S. and China
Notes: 1. ** denote significance at the 5% significance levels, respectively.
2. The critical values of the Engle-Granger Co-integration are taken from Engle and Yoo (1987).
3. The lag-length of difference Ks selected by minimizing AIC; r is the estimated threshold value.
denote the F-statistics for the null hypothesis of no co-integration and symmetric adjustment. Critical values are taken from4. and CFAF
Enders and Siklos (2001).
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Entire period Pre-subprime mortgage crisis During subprime mortgage crisis
Panel A (U.S.)
aiwan 0.2824** 0.2368** 0.3025**
Hong Kong 0.3707** 0.2221** 0.3946**
Singapore 0.3807** 0.1718* 0.4165**
Japan 0.2925** 0.1881* 0.3217**
Korea 0.3367** 0.2376** 0.3753**
India 0.3494** 0.1850* 0.4037**
Panel B (China)
Taiwan 0.2835** 0.0927 0.3657**
Hong Kong 0.4204** 0.1789* 0.4953**
Singapore 0.3203** 0.1574* 0.3715**
Japan 0.2794** 0.1226* 0.3393**
Korea 0.2793** 0.1048 0.3576**
India 0.2665** 0.0513 0.3601**
Results of Correlation Coefficient of Return
Notes: * and ** denote significance at the 10% and 5% significance levels, respectively.
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Entire period Pre-subprime mortgage crisis During subprime mortgage crisis
Panel A (U.S.)
Taiwan 0.6755*** 0.3752** 0.6279***
Hong Kong 0.8694*** 0.5163** 0.8261***
Singapore 0.7179*** 0.4206** 0.6535***
Japan 0.8885*** 0.3480** 0.8884***
Korea 0.8214*** 0.3564** 0.8711***
India 0.5513** 0.3260** 0.5688**
Panel B (China)
Taiwan 0.4280** 0.0464 0.4001**
Hong Kong 0.5572** 0.3075** 0.4682**
Singapore 0.4212** 0.2290** 0.5573**
Japan 0.4790** -0.0018 0.4616**
Korea 0.4032** 0.0232 0.4147**
India 0.5131** 0.2117** 0.5138**
Notes: 1. The volatility of return is measured by the conditional variance of return from the ARMA(p,q)-GARCH(p,q) model; the numbers
in the parentheses are the appropriate lag-lengths selected by minimizing AIC.
2. ** and *** denote significance at the 5% and 1% significance levels, respectively.
Results of Correlation Coefficient of Volatility of Return
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.0000
.0004
.0008
.0012
.0016
.0020
.0024
.0028
2004 2005 2006 2007 2008 2009
U.S.
.0000
.0004
.0008
.0012
.0016
.0020
.0024
.0028
2004 2005 2006 2007 2008 2009
TAIWAN
.000
.001
.002
.003
.004
.005
.006
2004 2005 2006 2007 2008 2009
HONGKONG
.000
.001
.002
.003
.004
.005
2004 2005 2006 2007 2008 2009
SINGAPORE
.000
.001
.002
.003
.004
2004 2005 2006 2007 2008 2009
JAPAN
.000
.001
.002
.003
.004
2004 2005 2006 2007 2008 2009
KOREA
.000
.001
.002
.003
.004
.005
.006
.007
2004 2005 2006 2007 2008 2009
INDIA
.0000
.0004
.0008
.0012
.0016
.0020
.0024
2004 2005 2006 2007 2008 2009
CHINA
The Volatility of Return in 8 Stock Markets
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t Statistics on ̂
Level First difference
U.S. -1.360(2) -18.272(1)***
Taiwan -1.475(1) -18.873(2)***
Hong Kong -1.483(0) -18.433(0)***
Singapore -1.463(2) -17.689(1)***
Japan -1.548(1) -17.653(1)***
Korea -1.294(0) -18.715(2)***
India -1.072(1) -17.531(2)***
China -0.843(3) -16.913(3)***
Results of the Nonlinear Unit Root Test – KSS Test
Notes: 1. The numbers in the parentheses are the appropriate lag-lengths selected by minimize AIC.
2. The simulated critical value for different Ks were tabulated in Kapetanios et al. (2003).
3. *** denote significance at the 1% significance level, respectively.
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Entire period Pre-subprime mortgage crisis During subprime mortgage crisis
Engle-Granger ADF Statistic Engle-Granger ADF Statistic Engle-Granger ADF Statistic
Panel A (U.S.)
Taiwan -1.458 -2.587 -1.443
Hong Kong -1.061 -3.728** -2.104
Singapore -1.292 -2.801 -1.727
Japan -2.032 -1.908 -2.376
Korea -1.232 -1.850 -2.527
India -0.689 -2.999 -1.429
Panel B (China)
Taiwan -2.105 -2.488 -2.379
Hong Kong -2.632 -1.393 -2.521
Singapore -1.953 -1.341 -2.575
Japan -1.235 -1.557 -2.705
Korea -2.352 -1.272 -3.144*
India -1.912 -1.187 -1.959
Results of the Engle-Granger Test for Co-integration
Notes: * and ** denote significance at the 10% and 5% significance levels, respectively.
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CFAF rCF
AF rCFAF r
Entire period Pre-subprime mortgage crisis During subprime mortgage crisis
Panel A (U.S.)
Taiwan 37.302*** 3.310* 0.01349 9.943*** 1.057 -0.00860 50.027*** 6.267*** 0.01537
Hong Kong 48.536*** 3.837** -0.01121 19.888*** 1.336 -0.00934 76.026*** 8.633*** -0.01410
Singapore 76.547*** 1.983 -0.01307 16.869*** 0.773 -0.01182 132.028*** 11.643*** -0.01577
Japan 74.756*** 3.053* 0.01475 16.519*** 2.756* -0.01238 106.267*** 7.262*** -0.01730
Korea 34.294*** 7.987*** -0.00581 22.702*** 1.479 0.01745 46.861*** 8.981*** -0.00564
India 23.808*** 2.792* -0.01604 13.264*** 1.598 0.02396 34.992*** 6.262*** -0.01863
Panel B (China)
Taiwan 4.305 2.042 -0.00784 0.906 1.728 0.01183 8.833** 4.818** 0.01184
Hong Kong 20.340*** 5.154** 0.00379 3.830 0.913 -0.00612 27.475*** 5.887** 0.01932
Singapore 10.648*** 0.561 -0.01112 4.300 0.389 0.00520 10.787*** 5.643** 0.01606
Japan 4.887 5.387** 0.01390 1.130 0.391 0.01402 10.807*** 7.792*** 0.00751
Korea 12.569*** 4.871** -0.00867 0.995 1.778 0.01406 15.028*** 6.746*** -0.00564
India 6.850** 0.617 -0.01734 1.153 2.312 0.01641 9.331*** 4.237** -0.02235
Results of the Ender-Siklos Test for Threshold Co-integration
Notes: *, ** and *** denote significance at the 10%, 5% and 1% significance levels, respectively.
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Conclusions Conclusions
There are four major findings in this research:
• First, there are significant increases in correlation coefficients of return
between the U.S. and Asian markets and between China and the Asian
markets during the financial crisis.
• Secondly, there are significant increases in correlation coefficients of
volatility of return between the U.S. and Asian markets and between
China and the Asian markets during the crisis. (volatility spillovers).
• Third, there are asymmetric co-integration relationships between the
U.S. and Asian markets (except the China market) around the crisis,
and the asymmetry in these co-integration relationships has
significantly increased during the crisis.
• China has no co-integration relationship with the Asian markets before
the crisis, but, during the crisis, the asymmetric co-integration
relationship between China and the Asian markets appeared.
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• The stock market co-movement between China and the Asian
stock markets increased during the financial crisis. Based on the
empirical results, this shows China has had more influence on the
Asian markets recently.
• Finally, the subprime mortgage crisis has weakened the effect of
international portfolio diversification. But investors can somewhat
diversify risks by investing in U.S. and China simultaneously.