Who has more influence on Asian Stock Markets around
the Subprime Mortgage Crisis－the U.S. or China?
Chien-Chung Nieh*
Chao-Hsiang Yang**
Yu-Sheng Kao***
January 7, 2011
* 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.
1
Abstract
• 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.
2
• 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 comovement trends between the U.S. and China’s markets and
Asian markets. However, with the application of the EndersSiklos threshold co-integration test, there was significant
increase in these asymmetric co-integration relationships
between them during the period of the subprime mortgage
crisis.
3
Literature Review
Four different approaches utilized to measure
international shock transmission effect by Dornbusch et
al. (2000) and Forbes and Rigobon (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.
4
Researchers
King and Wadhwani
(1990)
Lee and Kim (1993)
Cha and Oh (2000)
approaches
Findings
The correlation approach The cross-market
correlations increased
significantly among the
U.S., the U.K., and Japan
after the U.S. stock market
collapse in October 1987.
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.
5
Forbes and Rigobon The correlation coefficients
(2002)
are conditional on market
volatility.
(heteroskedasticity).
There was virtually no
increase 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 by
the 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.
6
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 Financial
Crisis, but it did during
the financial crisis.
7
• 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 cointegration model.
8
Methodologies
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:
Yt  Yt 1[1  exp( Yt 21 )]  t
(1)
• Kapetanios et al. (2003) follow Luukkonen et al. (1988) to
compute a first-order Taylor series approximation to the
[1  exp( Yt 21 )] under the null of   0, and approximate Eqn. (1)
by the following auxiliary regression:
P 1
Yt    Y    i Yt  d  t ,
3
t 1
t  1, 2, .........., T
(2)
i 1
Then, the null hypothesis and alternative hypothesis are expressed
  0 (non stationarity) against   0 (nonlinear stationarity).
9
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.
10
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).
Yi ,t   0  1 X t 1   i ,t
i  1, 2 .......... , 7
(3)
• 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)
11
Next, the residuals ε, are used in:
p 1
 t  I t 1 t 1  (1  I t )  2 t 1   i  t i   t
(4)
i 1
I t is the Heaviside indicator function, where I t  [Tt , M t ] , such that:
Tt  ｛
1
if  t 1  c
0
if  t 1  c
M t ｛
1
if  t 1  r
0
if  t 1  r
TAR Model
M-TAR Model
r and c ：threshold values
12
• 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)
H 0 : 1   2  0
H 0 : 1   2
(5)
(6)
13
Data
• 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 Mortgage 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.
14
Empirical Results
Relationships between the U.S. and China
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
FC
FA
r
FC
FA
r
4.058
1.589
-0.0187
4.346
1.776
0.0132
FC
3.636
r
FA
1.121
-0.0246
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.
4. FC and FA denote the F-statistics for the null hypothesis of no co-integration and symmetric adjustment. Critical values are taken from
Enders and Siklos (2001).
15
Results of Correlation Coefficient of Return
Entire period
Pre-subprime mortgage crisis
During subprime mortgage crisis
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**
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**
Panel A (U.S.)
Panel B (China)
Notes: * and ** denote significance at the 10% and 5% significance levels, respectively.
16
Results of Correlation Coefficient of Volatility of Return
Entire period
Pre-subprime mortgage crisis
During subprime mortgage crisis
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**
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**
Panel A (U.S.)
Panel B (China)
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.
17
The Volatility of Return in 8 Stock Markets
.0028
.0028
.006
.0024
.0024
.005
.0020
.0020
.0016
.0016
.0012
.0012
.0008
.0008
.0004
.0004
.004
.003
.002
.001
.0000
.0000
2004 2005
2006
2007
2008
.000
2004 2005
2009
2006
2007
2008
2009
2004 2005
.005
2007
2008
2009
2008
2009
HONGKONG
TAIWAN
U.S.
2006
.004
.004
.003
.003
.002
.002
.001
.001
.004
.003
.002
.001
.000
.000
2004 2005
2006
2007
2008
2004 2005
2009
2006
2007
2008
2009
JAPAN
SINGAPORE
.007
.0024
.006
.0020
.005
.000
2004 2005
2006
2007
KOREA
.0016
.004
.0012
.003
.0008
.002
.0004
.001
.000
.0000
2004 2005
2006
2007
INDIA
2008
2009
2004 2005
2006
2007
CHINA
2008
2009
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Results of the Nonlinear Unit Root Test – KSS Test
t Statistics on ˆ
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)***
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.
19
Results of the Engle-Granger Test for Co-integration
Entire period
Pre-subprime mortgage crisis
During subprime mortgage crisis
Engle-Granger ADF Statistic
Engle-Granger ADF Statistic
Engle-Granger ADF Statistic
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
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
Panel A (U.S.)
Panel B (China)
Notes: * and ** denote significance at the 10% and 5% significance levels, respectively.
20
Results of the Ender-Siklos Test for Threshold Co-integration
Entire period
Pre-subprime mortgage crisis
During subprime mortgage crisis
FC
FA
r
FC
FA
r
FC
FA
r
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
2.042
-0.00784
0.906
1.728
0.01183
8.833**
4.818**
0.01184
Panel A (U.S.)
Panel B (China)
Taiwan
4.305
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
5.387**
0.01390
1.130
0.391
0.01402
10.807***
7.792***
0.00751
4.871**
-0.00867
0.995
1.778
0.01406
15.028***
6.746***
-0.00564
0.617
-0.01734
1.153
2.312
0.01641
9.331***
4.237**
-0.02235
Japan
4.887
Korea
12.569***
India
6.850**
Notes: *, ** and *** denote significance at the 10%, 5% and 1% significance levels, respectively.
21
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.
22
• 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.
23
• The End
• Thank you for your attention
24