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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.

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• The End

• Thank you for your attention