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An Empirical Analysis of the U.S. Dollar, Yen and Eurodollar Exchange
Shock Mean and Volatility Spillover to domestic and China Stock Markets
by
Ching-Chun Wei
Department of Finance, Providence University,
200 Chung-Chi Rd. Shalu, Taichung 43301, Taiwan
Phone: 886-4-26328001-Ext.13063
E-mail:ccw@pu.edu.tw
October 18-19th, 2008
Florence, Italy
1
8th Global Conference on Business & Economics
ISBN : 978-0-9742114-5-9
An Empirical Analysis of the U.S. Dollar, Yen and Eurodollar Exchange
Shock Mean and Volatility Spillover to domestic and China Stock Markets
by
Ching-Chun Wei*
Department of Finance, Providence University, 200 Chung-Chi Rd. Shalu, Taichung 43301, Taiwan
Abstract
This paper used the Constant Conditional Correlation (CCC) and Dynamic Conditional
Correlation (DCC) Multivariate EGARCH-M model to analyze the U.S. Dollar (USD),
Japanese Yen, and Eurodollar to Renminbi (RMB) unexpected exchange rate shock mean and
volatility spillover to the domestic and China stock markets. Under the CCC-MEGARCH
model, we found that exchange rate markets have significant mean spillover to the China
Shenzhen Composite index (SJC) stock market, and the cross-section volatility spillover
effects and leverage effects are significant in the three exchange-rate models. Next, based
on the DCC-MEGARCH-M model, the mean spillover effect has been found in the USD to
RMB exchange rate model, the cross-section volatility spillover effects are significant in the
USD and Yen to RMB exchange rate market, and the asymmetric effects and volatility
persistence are significant in the USD and Eurodollar to RMB exchange rate market. We
found that the USD to RMB exchange rate has more significant mean spillover effect,
volatility cross-section effect, and asymmetric and volatility persistence effects than other
markets.
Keywords: CCC and DCC MEGARCH-M; Volatility spillover; Unexpected exchange rate
shock
JEL Classification: C12; F31; G15.
1. INTRODUCTION
Over half of China’s exports go to the United States, the European Union, and Japan (with
over 30% going to the U.S. alone). If China’s rapidly rising current account surplus, huge
accumulation of reserves, and limited appreciation of the renminbi (RMB) persuade
*
Correspond author: Department of Finance, Providence University, 200 Chung-Chi Rd., Shalu, Taichung
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legislators and policymakers in the major industrial countries that China is blocking effective
balance-of-payments adjustment, China may well find its access to these markets constrained
by new protectionist barriers. In response to international pressure over its policy for pegging
RMB to the USD, the Chinese government on July 21, 2005 announced it would immediately
appreciate the RMB to the dollar by 2.1% and adopted a currency policy based on a basket of
currencies (including the USD, Yen, and Eurodollar). However, the 2.1% revaluation of the
RMB has symbolic value, notably in the United States. It is indicative of the recognition that
China now where responsibility for the stability of the global economy. A more flexible
exchange rate should enable the People’s Bank of China to more effectively tailor monetary
condition to local needs as it moves toward a more market-based financial system. There are
two approaches that suggest the interrelationship between the exchange rates and stock prices
of two countries. Based on the “asset approach” of Frankel (1983), the exchange rate adjust
equate supply and demand for financial assets, and the expectation of financial asset price
movements affect exchange rate dynamics. Suppose the stock market affects are large,
change the impact of expansionary monetary policy on the exchange rate lead to an
appreciation rather than depreciation. On the “goods market” model of Dornbusch and Fisher
(1980), currency movements affect the international competitiveness of firms and the balance
of trade, thereby influencing real income and output, and eventually affect current and future
cash flows of companies and their stock prices.
Bodar and Reding (1999) examined the impact of Germany exchange rate fluctuations on the
stock market volatility and the correlation between the Germany stock market and European
markets. They found that there is no strong evidence that a higher exchange rate variability
increase stock market volatility. Colavecchio and Funke (2006) used the multivariate
GARCH model to study volatility spillover between the Chinese non-deliverable forward
market and seven of its Asia-Pacific counterparts and examine co-movements between China
and other Asian forward exchange rates and the volatility of Asian currencies is affected by
RMB exchange rate developments. Mum (2007) examined the exchange rate fluctuations in
international stock markets and to what extent volatility and correlations in equity markets
are influenced by exchange rate fluctuations. Empirical evidence indicated that a higher
foreign exchange rate variability increase local stock market volatility but decrease volatility
for the U.S. stock market. Tai (2007) used an asymmetric MGARCH(1,1)-M model examined
is whether there are pure contagion effects between stock and foreign exchange markets for
Asian country during the 1997 Asian crisis. The empirical results show that strong positive
impact of return shocks originating from the domestic stock market to its foreign exchange
market during the crisis is found.
Some recent authors have used the GARCH and EGARCH model to investigate the dynamic
43301, Taiwan. Phone:+886-4-26328001 Ext.13603, E-mail:ccw@pu.edu.tw
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relationship between stock return volatility and trading volume for individual stocks listed on
the Chinese stock market (Wang, Wang, Liu (2005); Copeland and Zhang (2003); Lee and
Rui (2000)). Extensive studies on conditional volatility of financial markets in the economics
of Greater China have been conducted. These include the works of Friedmann and Sanddorf
(2000) for stock index, as well as those of Yeh and Lee (2000), Ho and Tsui (2004), and
Colavecchio and Funke (2006) for non-deliverable forward exchange rate. Friedmann and
Sanddorf-Kohle (2000) apply both the EGARCH model and the GJR model to daily Chinese
stock index returns. They found that the EGARCH can well be at least as robust in periods of
high volatility than the GJR GARCH model. Kin Yip Ho and Tsui (2004) searched for
evidence of conditional volatility in the quarterly real GDP of Greater China, which
comprises the economies of Mainland China, Hong Kong, and Taiwan. They used the
EGARCH model to capture the existence of asymmetric
conditional volatility in real GDP. Yeh and Lee (2000) investigated the response of investors
to unexpected returns and the information transmission in the stock markets China. They
analyzed the asymmetric reaction of return volatility to good and bad news by utilizing the
GARCH model. They found that the impact of bad news of volatility is greater than the
impact of good news of the same effect in Taiwan and Hong Kong, but not for China.
Colavecchio and Funke (2006) used the multivariate GARCH model to study volatility
spillover between the Chinese non-deliverable forward market and seven of its Asia-Pacific
counterparts over the period January 1998 to May 2005. The objective of this study is to
examine the nature of co-movements between China and other Asian forward exchange rates
and the volatility of Asian currencies is affected by renminbi exchange rate developments.
The objectives of this paper by using the CCC and DCC-MEGARCH-M models that include
the unexpected exchange rate shock from the exchange rate market into the mean equation
are to capture the mean spillover effect from one market to another, and to capture the
volatility spillover effects in the variance equation, while also estimating the asymmetric
effect of exchange rate volatility to the stock market. The remainder of this paper proceeds as
follows: Section 2 presents the Constant Conditional Correlation (CCC) and the Dynamic
Conditional Correlation (DCC)-MEGARCH-M models. Section 3 starts the description of the
data employed in this study and presents the empirical results. Section 4 presents the
conclusion of the paper findings.
2. METHODOLOGY
In the financial literature, it has been suggested that the ARCH models are well suited to
capturing exchange rate and stock return movements. However, there are different
approaches to proxy exchange rate uncertainty. One is the use of variance of the exchange
rate return (Goldberg and KolstadK, 1995). Another is the estimated standard GARCH model
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to obtain a conditional measure of volatility. GARCH methods have been used to derive
measures of uncertainty (Engle, 1982). Here, we use the GARCH method error term to be the
proxy for measuring exchange rate uncertainty or shocks. Kutmos (1995) defines “price
spillover” as the impact of an innovation from market i on the conditional mean of market j,
whereas “volatility spillover” is the impact of an innovation from market i on the conditional
variance of market j. Here, we put the exchange rate variable into the mean equation to
examine the impact of unexpected exchange rate shock to the stock markets. The advantage
of the MEGARCH specifications is that they permit time-varying conditional covariance as
well as variances; thus, they allow for possible interactions within the conditional mean and
variance of two or more financial series.
Our study use a Constant Conditional Correlation (CCC) and Dynamic Conditional
Correlation(DCC) form of the multivariate EGARCH model to investigate market
interdependence and volatility transmission between unexpected exchange rate market to
stock markets in US, Japan and European to China. We estimate the MEGARCH model
suggested by Bollerslev (1990) is used as a framework to take account of asymmetric
volatility spillovers and the standard multivariate EGARCH model that assumes that the
underlying correlations between shocks are constant over time. This constant correlation
specification has generally a well-behaved likelihood function as well as limiting the number
of estimation of coefficients to a workable level. However, the dynamic conditional
correlation model which allow these correlations to be a time-varying.
Following Koutmos and Booth(1995) and Antoniou et al. (2003), we specifically the
multivariate EGARCH-M model as follows:
n
n
j 1
j 1
Ri ,t   i ,0    i , j R j ,t 1   j G j   i ,t
(1)

n


j 1

 i ,t  exp  i ,0   ij f j ( Z j ,t 1 )   i ln(  i2,t 1 )
(2)
f i ( Z j ,t 1 )  ( Z j ,t 1  E ( Z j ,t 1 )  r j Z j ,t 1 )
(3)
 i , j ,t   i , j  i ,t  j , t
(4)
From the mean equation, the dynamic relationships in returns are captured by using a Vector
Autoregressive (VAR) model, where the conditional mean in each market (Ri,t) is a function
of own past returns and cross-market past returns(Ri,t). However, βi,j, captures the lead-lag
relationship between returns in different markets, for i  j .The coefficients of  j measures
the unexpected exchange rate mean spillover on form three unexpected exchange rate
markets to the local and china stock markets. Gj is the unexpected exchange rate shock from
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market j. The coefficients of  j measures the three unexpected exchange rate price spillover
to the local and china stock markets. The significantβi,j value, implying market j leads
market i. Here, the first-order VAR is adopted because we think the stock market will quickly
respond to information from other markets.
The conditional variance equation describes the conditional variance in each market as an
exponential function of past standardized innovation,( Z j ,t 1 
 j ,t 1
), that is from its own
 j ,t 1
market and other markets. The estimated value of  t that measure the persistence of
volatility. If  t =1, then the unconditional variance doesn’t exist and the conditional
variance follows an integrated process of order one.
Spillovers are captured by the
coefficients  ij (i  j ) , while asymmetry implies negative r j The asymmetric influence of
n
innovation on the conditional variance is captured by the term (  ij f i ( Z j ,t 1 ) ). Here, a
j 1
statistically significant positive rj together with a negative(positive) r j show that negative
innovations in market j have a greater impact on the volatility of market i than
positive(negative) innovations. The relative importance of the asymmetry(or leverage effect)
can be measure by the ratio
 1  rj
(1  r j )
.
The term Z jt  E ( Z jt ) measures the size effect. Assuming positive  ij the impact of
Z jt on  t will be positive(negative) if the magnitude of Z jt is greater(smaller) than its
expected value E ( Z jt ) . Assuming the conditional joint distribution of the returns of the
three markets is normal, the log likelihood for the multivariate EGARCH model can be
written as:
L( )  (1 / 2)( NT ) ln( 2 )  (1 / 2) ln st  t' S t1 t 
T
(5)
t 1
where N is the number of equation, T is the number of observations, θis the 33 1
parameter vector to be estimated t'  1,t 2,t 3,t  is the 1 3 vector of innovations at time
t, St is the 3 3 time varying conditional variance-covariance matrix with diagonal elements
given and cross diagonal elements are given. The log-likelihood function is highly non-linear
inθ,and therefore, numerical maximization techniques have to be used. The disturbance
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error term of mean equation are assumed to be conditional multivariate normal with mean
zero and conditional covariance matrix H t :
 t t 1 ~ N (0, H t )
(6)
and
H t  Dt  t Dt
or
 i , j ,t  q i , j ,t  i ,t  j , t
(7)
Under the conditional covariance matrix, Dt is a n time n diagonal matrix with the
time-varying standard deviations of (3) on the diagonal and Di is a time-varying symmetric
correlation matrix:
 1,t
 0

Dt   

 
 0
0
 2 ,t


0
  0 
  0 
 
 

 
 
   n.t 
,
 1,1,t

2 ,1,t
t  
 

  n ,1,t
1, 2,t  1,n,t 
 2, 2,t   2,n ,t 



 

  n , n ,t 
(8)
The dynamic correlations are captured through the asymmetric general diagonal DCC
equation:
 t  (  A A  B B  C  NC )  AZ t 1 Z t 1 A  B t 1 B  C t 1t 1C
(9)
The  and N are the conditional correlation matrices of Zt and  t .Above model is a
generalization of the DCC model of Engle(2002) to capture asymmetric correlations and first
used by Capiello et al. (2003). The matrix of A, B and C are restricted to being diagonal for
practicality in the estimation of model.  t is positive definite with probability one if
( (  A A  B  B  C  NC ) is positive definite. Through the final term of (7), the
time-varying correlations will respond asymmetrically to positive or negative shocks in each
market. Next, we scale  t to get the correlation matrix qt:
1
 t   t*  t t*
1
(10)
The multivariate EGARCH allow us to examine both volatility spillover between markets and
asymmetry. However, it is not useful to apply such an EGARCH specification to the
conditional correlations, both because it would unduly restrict the conditional correlations to
be always positive and because it has too many parameters. The DCC specification of (6)
does not have these problems, but nevertheless allows the possibility of asymmetric effects.
The model is estimated by maximum likelihood. As  t  t 1 is normally distributed, the
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log-likelihood can be express as:
L( )  
1 T
(n log( 2 )  log H t   t' H t1 t ) 

2 t 1
1 T
  (n log( 2 )  log Dt  t Dt   t' ( Dt  t Dt ) t )
2 t 1
(11)
where n is the number of equations, T is the number of observations,  is the parameter
vector to be estimated,  t' , is the vector of innovations at time t, Ht, is the time varying
conditional variance-covariance matrix with diagonal elements give by equation (2) and cross
diagonal elements are give by equation (4).
3. DATA SUMMARY AND EMPIRICAL RESULTS
3.1 Data Summary
In order to investigate the price and volatility spillover of unexpected exchange rate shock in
the China stock markets, the data consist of the daily data of five stock market indexes and
three countries’ exchange rates from the Taiwan Economic Journal (TEJ) data bank. These
are the New York Dow-Jones index (NYD), Japan Nikkei 225 index (J225), European
Amsterdam AEX index (AEX), China Shanghai Composite index (SHC), Shenzhen
Composite index (SJC), and the exchange rates for the USD(CAR), Japanese Yen (CJR) and
Eurodollar (CUR) to Chinese RMB from July 21, 2005 to January 4, 2007. Here, the
variables of returns in each market are calculated as the first difference in the natural
logarithms of the stock market indexes.
Table 1 presents the descriptive statistics for each variable series. The skewness statistics
suggest lack of normality in the distribution of the return series. The CAR, NYD, AEX, J225,
SHC and SJC have return distributions that are negatively skewed, while those of CJR and
CUR are positively skewed. The value of kurtosis indicate that each of the return series is
leptokurtosis. The Jarque-Bera (JB) normality test rejects the null hypothesis of normality.
The significant value of the Ljung-Box Q statistics for return series rejects the null hypothesis
of white noise and indicates the presence of autocorrelation. The ARCH-LM for all variables
is significant at 1% level, indicating the existence of ARCH phenomenon for all variable
series. Here, the stationarity of the series was investigated by employing the unit root tests
developed by augmented Dickey Fuller (ADF) (Dickey and Fuller, 1981), and Phillips and
Perron (1988) tests. The calculated ADF statistics are less than the critical value only for the
first difference variables; therefore, it could be concluded that all variables have difference
stationarity.
<Insert Table 1 about here>
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3.2.1 Constant Conditional Correlation (CCC)-MEGARCH (1, 1)-M Model
3.1.1 Mean and volatility spillover- from U.S. to China
The maximum likelihood estimates of the multivariate model with no parameter restrictions
are reported in Table 2. The full model considers both price and volatility spillovers from the
last two markets to close to the next market to trade. Table 2 shows that there are significant
unexpected exchange rate shock price spillovers to the China SJC stock market (-0.3956).
The value of feedback effects (0.1321 and -0.3771) have been found significant between two
China stock markets. The effect from the New York stock market to the China stock markets
is a significant spillover. However, we find no significant effects from the China stock
markets to the New York stock market (-0.3288 and -0.3689). Table 2 also presents second
moment interactions or volatility spillover between the domestic stock market and the two
China stock markets. The impacts of the past own and cross-market innovations on current
market volatility are investigated through parameter estimates. Table 2 reports these
parameter estimates, as well as the parameters measuring the asymmetric volatility spillover
effect,  i . The estimate parameters of the impact of past own innovation on current volatility
(  11 ,  22 ,  33 ) are all positive and significantly different from zero for all three markets.
These results indicate the presence of significant own volatility spillover in these markets.
The estimate parameters of the cross-market innovation spillover parameters,  i , j (i  j ) ,
indicate significant impact of past volatility shocks from the U.S. stock market to the two
China stock markets. Here, we also find that these are feedback impacts of volatility
innovation from those markets.
Within China, we find that feedback volatility spillover occurs in two China stock markets
with the SHC more significant than the SJC markets. Later, we compare these volatility
spillover effects; all markets have significant feedback spillover, and the impact spillover of
the U.S. to the China stock markets has more significance than that of China to the U.S. Here,
the leadership roles within the mean and volatility equation should be noted, with U.S. as the
key market for those markets. More importantly, Table 2 also shows the volatility
transmission mechanism to be asymmetric for all markets. That is, the coefficients measuring
asymmetric, namely, rj, are significant for the three markets. This result reinforces our
assertion that bad news increases volatility more than good news does. Except for the SHC
market, the estimates of the asymmetry parameter are negative and significantly different
from zero, suggesting that negative past innovations in these stock markets increase volatility
more than positive innovations do. The persistence of volatility is measured by ri, Table 2
reports that those values are less than one for all markets. However, these estimates are highly
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significant, indicating high volatility persistence in U.S. and two China stock markets.
The residual-based diagnostics are reported in Table 2. The LB and LB2 statistics show no
serious linear or nonlinear dependencies for the standardized residuals for the three markets.
Specifically, the standardized residual results show no evidence of autocorrelation, which
means that such effect was successfully captured by our model. From the ARCH-LM test, we
can conclude that both linear and nonlinear dependencies in the return series have been
effectively filtered.
However, skewness and kurtosis coefficients, as well as J-B statistics, indicate various
departures from normality. Thus the conditional tri-variate normality assumption may be
violated. The validity of the assumption of constant conditional correlation can be assessed
by testing for serial correlation in the cross-product of the standardized residuals. The
Ljung-Box statistics for eight lags are reported in Table 2, which show no evidence of serial
correlation, so that constant correlation specification appears to be a reasonable
parameterization of the variance–covariance structure of the three markets.
<Insert Table 2 about here>
3.1.2. Mean and Volatility Spillover- from Japan to China
At the Yen-RMB exchange rate in the Japan and China stock markets, there are significant
unexpected exchange rate price spillover effects to the China stock market (-0.1158 and
-0.1586) as shown in Table 3. At the Chinese stock markets, we have found negative and
significant feedback price spillover effect (-0.1477 and -0.1259). However, the unexpected
exchange rate shock is negative, and the significant spillover to the two China stock markets
indicates that negative exchange rate shock reduces the stock market returns. The variance
equation of this table indicates the volatility spillover between stock markets. The estimate
parameters of the 11 ,  22 and  33 (0.2658, 0.1206 and 0.2050) are all positive and
significantly different from zero for all three markets, indicating the presence of significant
own volatility spillover in those markets. The cross-market innovation spillover
parameter  i, j indicates significant impact of volatility from the Japan stock market to the
SJC stock market (-0.0291), from the SHC stock market to the Japan and SJC stock markets
(-0.0516 and 0.1607), and from the SJC market to the Japan and SHC stock markets (-0.0675
and 0.0404). Also, there are significant feedback volatility spillover effects in the two China
stock markets. Table 4 reports the volatility transmission mechanism to be asymmetric for all
markets, and the coefficients (-0.3652, 0.0941, and -0.0687) are all significant for all markets.
The Ljung-Box statistics show no serious evidence of linear and non-linear dependence in the
standardized residual; the ARCH-LM test shows that no ARCH effect exists; skewness,
kurtosis, and J-B statistics show that normality does not exist. The Ljung-Box Q statistics
tests for the cross-product of standardized residuals show no evidence of serial correlation.
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<Insert Table 3 about here>
3.1.3 Mean and Volatility Spillover-from Europe to China
The maximum likelihood estimates of the Eurodollar–RMB exchange rate to the China stock
market of the MEGARCH(1,1)-M model are reported in Table 4. Focusing on the parameters
in the conditional mean equation, we can see that current returns in the European markets are
not influenced by past returns in China’s stock markets. Returns in China are not correlated
with past returns in the European markets. However, we did not find a significant feedback
price spillover effect between the European and the China stock markets. However, the
Eurodollar–RMB unexpected exchange rate has a negative price spillover effects on the two
China stock markets. Table 4 also presents the estimation result of the measurement
coefficients of the volatility interactions. The estimate parameters of 11 ,  22 , and
 33 (0.1791,-8.436e-03 and0.0240) are all significantly different from zero at the European
and the China stock markets. It shows the presence of a significant own volatility spillover in
those markets. The cross-market innovation spillover effect shows the significant impact of
volatility from the SJC stock market to the European market (0.0577), and the feedback effect
between the two China stock markets. The reporting parameters of the volatility transmission
mechanism to be asymmetric are significant for all markets (-0.2273, 0.0469, and -0.0281).
Finally, the parameters of persistence volatility (0.8551, 0.9321, and 0.5916) show high
volatility persistence in the three markets. Table 4 shows that Ljung-Box Q statistics has no
serious evidence of serial autocorrelation, except for the SJC stock market. The estimated
coefficients of skewness, kurtosis, and J-B statistics imply a violation of the normality
assumption. The Ljung-Box Q statistics test for the validity of the assumption of constant
conditional correlation displays no evidence of serial correlation, so that constant correlation
specification will be suitable for those markets.
<Insert Table 4 about here>
3.2. The Dynamic Conditional Correlation (DCC)-MEGARCH (1, 1)-M Model
3.2.1 Mean and Volatility Spillover- from U.S. to China
Table 5 presents the estimation results for asymmetric DCC-MEGARCH(1,1)-M model. In
terms of the first moment interdependences, the coefficient of  j indicate that there are
negative significantly unexpected price spillover effects of unexpected exchange rate shock
to the China stock markets. Turning to the volatility spillover equation, it is observed that the
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estimated parameters  i, j are positive and significant at 1% level. The coefficients value of ri
measures the leverage effect to be less than one and negatively significant at SHC stock
markets. The residual-based diagnostic tests for the model of Ljung-Box Q statistics show no
serious evidence of linear and non-linear dependence in the standardized residuals. The
ARCH-LM test represents that no ARCH effect exist.
<Insert Table 5 about here>
3.2.2 Mean and Volatility Spillover-from Japan to China
The estimated results reported in Table 6 indicate that the price spillover effects between
those stock markets are all insignificant. This table also reports the estimation result of the
volatility spillover between stock markets. The estimated parameters of  i, j are all positive
and significant at least at the 10% level, indicating the presence of significant volatility
spillover in those markets. The estimated coefficients of persistence volatility ri are only
significant at the Japan stock markets.
<Insert Table 6 about here>
3.2.3 Mean and Volatility Spillover-from Europe to China
Finally, Table 7 displays the estimation result of the European to the China stock market. The
empirical results show a significant price spillover effect only at the European stock market
but not for the other two China stock markets. The estimated parameters of  i, j are also
significant only at the European stock market. The parameters of volatility persistence show a
significant volatility persistence at the European and SHC stock markets. In this model, we
did not find any significant price and volatility spillover effect among those markets.
<Insert Table 7 about here>
4. CONCLUSION
Through the CCC and DCC-MEGARCH(1,1)-M models, this paper includes the unexpected
exchange rate shock of the exchange market in the mean equation to capture the price
spillover effect from one market to another. In the CCC-MEGARCH-M model, we found that
the three exchange rate markets have significant price spillover to the China SJC stock
market. The effects of the cross-section volatility spillover effect have been found to be
significant at the USD, Yen, and Eurodollar to RMB exchange rates. The measurement of
October 18-19th, 2008
Florence, Italy
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8th Global Conference on Business & Economics
ISBN : 978-0-9742114-5-9
leverage effects is also significant in all three exchange-rate volatility spillover models.
Furthermore, the persistence of volatility measurement is significant which show the
volatility persistent.
Next, based on the DCC-MEGARCH(1,1)-M model, the price spillover effect has been found
in the USD–RMB exchange rate model, but not in the other two exchange rate models. The
cross-section volatility spillover effects are also significant at the USD and Yen to RMB
exchange rate markets except for the Eurodollar exchange rate market. The asymmetric
effects and volatility persistence are significant at the USD and Eurodollar to RMB exchange
rate market. In summary, it is obvious that the USD to RMB exchange rate has more
significant price spillover effect, volatility cross-section effect, asymmetric, and volatility
persistence effect than others. In this study, we found that USD and RMB exchange rates play
a significant role in the China stock market. In particular, U.S. and China are more integrated
with each other. Compared with the U.S stock market, however, the yen and Eurodollar to
RMB exchange rates have weakly significant effect on the China stock prices
October 18-19th, 2008
Florence, Italy
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8th Global Conference on Business & Economics
ISBN : 978-0-9742114-5-9
References
Antoniou, A., Pescetto, G., and Violaris, A. (2003). Modeling International Price
Relationships and Interdependencies Between the Stock Index Future Markets of Three EU
Countries: A Multivariate Analysis, Journal of Business & Accounting, 30, 645-667.
Bollerslev, T. (1990). Modeling the Coherence in the Short-Run Nominal Exchange Rates,
Review of Economics and Statistics, 72, 498-505.
Cappiello,L., Robert, E., and Kevin,S. (2003). Asymmetric Dynamics in the Correlation of
Global Equity and Bond Returns, European Central Bank, Working Paper Series, 245,
1-65.
Colavecchio,C., and Michael F. (2006). Volatility Transmission between Renminbi and
Asia-Pacific on shore and off-shore U.S. Dollar Futures, Bank of Finland, BoFIT
Discussion Paper.
Copeland, L., and Zhang B. (2003). Volatility and Volume in Chinese Stock Markets, Journal
of Chinese Economic and Business Studies, 1, 287-300.
Dornbusch., R. and Fisher., S.(1980).Exchange Rates and the Current Account, American
Economic Review, 70, 960-71.
Frankerl, J.A. (1983). Monetary and Portfolio-Balance Models of Exchange Rate
Determination, in J.S. Bhandari and B.H. Putman (eds.), Economic Interdependence and
Flexible Exchange
Rates. Cambridge, MA.: MIT press.
Friedmann,R., and Sanddorf-Kohle. (2002). Volatility Clustering and Nontrading Days in
Chinese Stock Markets, Journal of Economics and Business, 54,193-217.
Goldberg,L., and Kolstand, C.(1995).Foreign Direct Investment, Exchange Rate Volatility
and Demand Uncertainty, International Economic Review, 36, 855-873.
Ho, K-Y., and Tsui, K.C. (2004). Volatility Dynamic of the Tokyo Stock Exchange: A
Sectoral Analysis based on the Multivariate GARCH Approach, Money Macro and Finance
(MMF) Research Group Conference.
Koutmos, G., and Booth,G..(1995). Asymmetric Volatility Transmissions in International
Stock Markets, Journal of International Money and Finance, 14, 747-762.
Lee,C.F., and Rui, O.M. (2000). Does Trading Volume Contain Information to predict Stock
Returns? Evidence from China’s Stock Markets, Review of Quantitative Finance and
Accounting, 14, 341-60.
Yeh, Y-H and Lee, T-S. (2000). The Interaction and Volatility Asymmetry of Unexpected
Returns in the Greater China Stock Markets, Global Finance Journal, 11, 129-149.
Wang, P., Wang P., and Liu, A. (2005).Stock Return Volatility and Trading Volume: Evidence
from the Chinese Stock Market, Journal of Chinese Economic and Business Studies, 3,
39-54.
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Table 1. Descriptive Statistics
CAR
Mean
0.1091
Mediam
0.1087
Maximum
0.1123
Minmum
0.1068
Std.Dev.
0.0015
Skewness
0.5959
Kurtosis
2.2923
Jarque-Bera
32.7460**
ARCH-LM(8) 94.286***
LB (8)
113.13***
191.29***
LB 2 (8)
CJR
14.7528
14.7486
16.2095
13.5931
0.5788
0.3340
2.9739
9.2088**
21.544***
22.22***
51.57***
CUR
0.1002
0.0992
0.1069
0.0949
0.0030
0.3071
1.8562
28.723**
11.844***
35.252***
43.273***
NYD
11297.71
11124.37
12786.64
10215.22
686.7813
0.6051
2.2302
35.0626**
2.366***
25.74**
27.98**
J225
15614.20
15960.62
18215.35
11695.05
1548.216
-0.8980
3.0088
54.9815**
3.969***
116.12*
107.63*
SHC
SJC
AEX
5811.692 1664.964 407.9452
5850.800 1589.540 396.7700
6444.400 3197.540 841.4800
5142.100 1020.630 240.3700
325.1345 580.9623 144.1937
1.12492
1.2414
-0.1500
3.1669
3.8527
2.0123
18.1556** 86.7373** 117.4557**
8.1103*** 7.368*** 12.016***
28.16**
24.85** 40.766**
197.96*
96.396*
158.66*
Note：1.The significant value of the LB-Q statistics for the squared returns suggests the presence of
autocorrelation in the square of stock returns. ARCH-LM statistics proposed by Engle (1982) aimed to
detect ARCH. In fact the value of ARCH-LM(8) are all significant at 1% level, indicating the
existence of ARCH phenomena for all variable series.
2. *** indicated at least significant at 1% level. ** indicated at least significant at 5% level. *indicated at
least significant at 10% level.
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Table 2. The CCC-MEGARCH(1.1)-M Model for U.S.D-RMB unexpected exchange rate mean and
volatility spillovers to U.S. and china stock markets
Mean Equation
DLNYD
 1, 0
4.2284e-04***
DLSHC
 2,0
1.8319e-03***
(2.2088e-04)
1,1
0.0488
-0.0194
 2 ,1
-0.3288***
 2, 2
-0.1148***
8.7562e-03
0.1321
(0.02)
1
 3,1
 3, 2
2
-0.3463***
(0.09)
-0.3771***
(0.04)
 3, 3
(0.02)
0.0704
-0.3689***
(0.06)
(0.03)
 2,3
1.8087e-03*
(4.8847e-04)
(0.05)
(0.02)
 1,3
 3, 0
(4.3529e-04)
(0.05)
 1, 2
DLSJC
0.3916***
(0.03)
3
(0.17)
-0.3956***
(0.17)
Variance Equation
 1,0
9.5419e-07***
 2,0
1.3232e-04***
(1.6861e-07)
 1,1
0.0663***
(7.1824e-06)
 2,1
0.0896***
(0.01)
 1,2
-0.0232**
 2,2
0.0959***
-0.0703***
-0.0357***
(9.334e-03)
1
-0.1590***
2
0.1624***
0.9878***
 3,2
-0.9950***
(6.3258e-03)
(5.9603e-03)
0.0894***
(3.9394e-03)
 3,3
0.0789*
(4.8602e-03)
3
(0.01)
2
-0.0196***
(7.3792e-03)
(0.02)
(0.02)
1
 3,1
(3.1042e-03)
 2,3
2.3393e-04***
(2.1378e-06)
(0.01)
(0.01)
 1,3
 3,0
-6.5727e-03***
(3.5835e-03)
3
-0.8074***
(0.05)
Diagnostic test checking
DLNYD
DLSHC
DLSJC
L-BQ(8)
18.2433
25.8624
12.2540
L BQ 8
28.2762
50.2753*
33.4546*
ARCH-LM(8)
4.1007
8.6746
8.1212
Skewness
-0.4305*
-1.2746*
-1.2718*
Kurtosis
2.3295*
5.5509*
4.6204*
124.9087*
721.3709*
537.8280*
2
Jarque-Bera
Note: 1. DLNYD, DLSHC and DLSJC are the difference log of the New York Dow-Jones Stock index, China
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Shanghai Composite Stock index and Shenzhen Composite Stock index, respectively. 2. *** indicated
at least significant at 1% level, ** indicated at least significant at 5% level, * indicated at least
significant at 10% level.
Table 3. The CCC-MEGARCH(1.1)-M Model for Yen-RMB unexpected exchange rate
mean and volatility spillovers to Japan and China stock markets
Mean Equation
Coefficient
DLJ225
 1, 0
1.144e-03**
(4.6558e-04)
 1,1
0.0356
 2,0
 2,1
DLSHC
1.4519e-03***
(2.8262e-04)
0.038***
(0.0366)
 1, 2
0.0547**
1
-0.1154***
(0.03)
-0.0723
(0.07)
Variance equation
 1,0
2.1707e-05
(3.3601e-06)
 1,1
0.2658***
 2, 2
-1.4658e-03
 1,3

1
L BQ 8
L BQ 2 8
ARCH-LM
Skewness
Kurtosis
Jarque-Bera
-0.0516***
(0.01)
-0.0675***
(0.01)
-0.3652***
(0.07)
0.7449***
(0.04)
5.6995
14.1878
0.4168
-0.29742***
0.70088**
16.37364*
(0.02)
 3, 2
(0.027)
 2,3
 3, 3
1.1939e-04***
(1.7323e-06)
-1.7070e-03
 3,0
2
 2,0
 2,1
3
 3,1
(6.4842e-03)
 2,2
0.1206***
(4.86e-04)
0.0404***
(2.2939e-03)
0.0941***
(0.02)
0.4649***
(5.8158e-03)
13.0672
15.5516*
2.31609
-1.27467*
5.55096*
721.37099*
 2,3
2
2
-0.1259***
(0.02)
-0.1477***
(0.01)
-0.1158***
(0.03)
(0.07)
 1,2
 3,1
(0.02)
(0.03)
 1,3
 3, 0
DLSJC
1.5948e-03***
(3.4168e-04)
-4.6720e-03
0.0428**
(0.02)
-0.1586***
(0.04).
2.3817e-04***
(1.8234e-07)
-0.0291***
(1.7292e-03)
 3,2
 3,3
3
3
0.1607***
(2.5516e-05)
0.2050***
(5.6536e-04)
-0.0687***
(0.02)
-0.8023***
(0.03)
11.7211
13.5835
3.3284
-1.2718*
4.62048*
537.828*
Note: 1. DLJ225, DLSHC and DLSJC are the difference log of the Japan Nikkei Stock index, China Shanghai
Composite Stock index and Shenzhen Composite Stock index, respectively.
2. *** indicated at least significant at 1% level, ** indicated at least significant at 5% level, * indicated at
October 18-19th, 2008
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ISBN : 978-0-9742114-5-9
least significant at 10% level.
3. L BQ
8
L BQ 2 8 is the Ljung-Box Q statistic value for return and squared return
and
autocorrelation at lag period eight.
Table 4. The CCC-MEGARCH(1.1)-M Model for Eurodollar-RMB unexpected exchange rate mean and
volatility spillovers to Europe and China stock markets
Mean Equation：
DLAEX
 1, 0
 1,1
4.0483e-04
(3.0687e-04)
-0.0833**
SHC
 2,0
3.2085e-03***
(6.2143e-04)
 2,1
-0.0412
(0.05)
 1, 2
-0.0296
0.0233
 2, 2
0.0558
0.0274
3.1469e-03***
 3,1
-6.3177e-03
(0.10)
 3, 2
(0.09)
 2,3
-0.0665
(0.04)
1
 3, 0
(0.09)
(0.04)
 1,3
SJC
(0.10)
 3, 3
(0.09)
2
-0.2177*
(0.05)
-0.0715
0.0964
(0.10)
3
(0.08)
-0.2557***
(0.09)
Variance Equation
 1,0
 1,1
3.7517e-06***
(1.2474e-06)
0.1791***
 2,0
1.4813e-05***
(7.5191e-07)
 2,1
0.0343
(0.05)
 1,2
0.0341
0.0577**
 2,2
-8.436e-03***
 2,3
6.383e-03***
-0.2273***
0.0469***
(0.05)
1
0.8551***
L-BQ(8)
L BQ 2 8
ARCH-LM(8)
Skewness
Kurtosis
Jarque-Bera
(0.05)
16.2452
37.8126
24.975*
-0.4090*
1.6942
71.5266*
 3,2
 3,3
0.9321***
(6.0954e-03)
19.1684
19.2844
3.1455
-1.2747*
5.5510*
721.3710*
0.0583**
7.1375e-03**
0.0240***
(7.7002e-03)
3
(6.0305e-03)
2
(2.4851e-05)
(3.9240e-03)
(3.7196e-03)
2
1.2004e-04**
(0.03)
(1.566e-02)
(0.03)
1
 3,1
(0.03)
(0.03)
 1,3
 3,0
-0.0281***
(0.01)
3
0.5916***
(0.07)
10.7551
15.1049***
4.1420
-1.2718*
4.6205*
537.8289*
Note: 1. DLAEX, DLSHC and DLSJC are the difference log of the Europe Amsterdam Aex Stock index, China
Shanghai Composite Stock index and Shenzhen Composite Stock index, respectively. 2. *** indicated at least
significant at 1% level, ** indicated at least significant at 5% level, * indicated at least significant at 10% level.
October 18-19th, 2008
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3.
ISBN : 978-0-9742114-5-9
L BQ8 and L BQ 2 8 is the Ljung-Box Q statistic value for return and squared return
autocorrelation at lag period eight.
Table 5. The DDC-MEGARCH(1.1)-M Model for U.S.D-RMB unexpected exchange rate mean and
volatility spillovers to U.S. and China stock markets
Mean Equation：
Coefficient
 i,o
DLNYD
DLSHC
DLSJC
4.4154e-04*
4.2247e-03***
4.8600e-03***
(2.5308e-04)
(4.2462e-04)
(4.3388e-04)
-0.0360
0.0549***
0.1082***
(0.04)
(0.01)
(0.02)
-0.0333***
-0.2370***
-0.2174***
(0.04)
(0.06)
(0.06)
3.1890e-05***
1.7000e-04***
1.7648e-04***
(1.4861e-06)
(1.2561e-05)
(1.7555e-05)
0.0483***
0.1788***
0.1009***
(1.84e-03)
(0.02)
(0.01)
0.1602***
0.1662***
0.2831***
(0.04)
(0.05)
(0.06)
-0.1759***
-0.0664*
9.9148e-03
(3.3917e-03)
(0.04)
(0.04)
 i, j
j
Variance equation
 i,o
 i, j
i

j
Diagnostic test checking
L-BQ(8)
7.7199
L BQ 8
11.9951
ARCH-LM(8)
0.7783
Log-likelihood
4683.25
2
Note: 1. DLNYD, DLSHC and DLSJC are the difference log of the New York Dow-Jones Stock index, China
Shanghai Composite Stock index and Shenzhen Composite Stock index, respectively.
2. *** indicated at least significant at 1% level, ** indicated at least significant at 5% level, * indicated at
least significant at 10% level.
3. L BQ
8
and
L BQ 2 8 is the Ljung-Box Q statistic value for return and squared return
autocorrelation at lag period eight.
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ISBN : 978-0-9742114-5-9
Table 6. The DDC-MEGARCH(1.1)-M Model for Yen-RMB unexpected exchange rate mean and
volatility spillovers to Japan and China stock markets
DLJ225
DLSHC
DLSJC
1.0627e-03
2.1424e-03
2.9674e-03***
(5.8503e-04)
(1.5454e-03)
(6.6629e-04)
-0.0103
5.9904e-03
0.0620
(0.10)
(0.02)
(0.04)
-0.0143
-0.0111
-0.0433
(0.11)
(0.03)
(0.04)
1.0617e-04***
2.1187e-04***
2.2556e-04***
(8.5108e-06)
(2.5924e-05)
(1.1984e-05)
0.1885**
0.1246***
0.0966***
(0.10)
(0.04)
(0.02)
0.1242
-0.0120
-0.2443***
(0.14)
(0.04)
(0.13)
-0.2443**
0.550
0.0343
(0.13)
(0.07)
(0.03)
Mean Equation：
Coefficient
 i,o
 i, j
j
Variance equation
 i,o
 i, j
i

j
Diagnostic test checking
L-BQ(8)
L BQ 2 8
ARCH-LM(8)
Log-likelihood
2.006
4.520
0.073639
4410.0327
Note: 1. DLJ225, DLSHC and DLSJC are the difference log of the Japan Nikkei Stock index, China Shanghai
Composite Stock index and Shenzhen Composite Stock index, respectively.
2. *** indicated at least significant at 1% level, ** indicated at least significant at 5% level, * indicated at
least significant at 10% level.
3. L BQ
8
and
L BQ 2 8 is the Ljung-Box Q statistic value for return and squared return
autocorrelation at lag period eight.
October 18-19th, 2008
Florence, Italy
20
8th Global Conference on Business & Economics
ISBN : 978-0-9742114-5-9
Table 7. The DDC-MEGARCH(1.1)-M Model for Eurodollar-RMB unexpected exchange rate mean and
volatility spillovers to Europe and China stock markets
Mean Equation：
Coefficient
 i,o
 i, j
j
DLAEX
DLSHC
DLSJC
3.0794e-04***
2.6471e-03***
2.6228e-03***
(1.1558e-04)
(3.8402e-04)
(4.7437e-04)
0.4632***
0.0623***
0.1567***
(0.06)
(0.02)
(0.03)
-0.5550***
8.7791e-03
0.0803
(0.08)
(0.06)
(0.05)
6.1826e-05
1.9205e-05
4.8109e-05
(2.549e-06)
(1.4964e-05)
(3.0801e-05)
0.2327***
0.0796
0.1159
(0.04)
(0.05)
(0.08)
0.7558***
0.7862***
0.6423***
(0.07)
(0.09)
(0.17)
-0.2724***
0.1262*
0.1348
(0.05)
(0.07)
(0.08)
Variance equation
 i,o
 i, j
i

j
Diagnostic test checking
L-BQ(8)
L BQ 2 8
ARCH-LM
Log-like
3.137
1.237
3.089
4692.1838
Note: 1. DLAEX, DLSHC and DLSJC are the difference log of the Europe Amsterdam Aex Stock index, China
Shanghai Composite Stock index and Shenzhen Composite Stock index, respectively.
2. *** indicated at least significant at 1% level, ** indicated at least significant at 5% level, * indicated at
least
significant at 10% level.
3. L BQ
8
and
L BQ 2 8 is the Ljung-Box Q statistic value for return and squared return
autocorrelation at lag period eight.
October 18-19th, 2008
Florence, Italy
21