Discussion Papers In Economics
And Business

Intraday Return and Volatility Spillover Mechanism from
Chinese to Japanese Stock Market

Yusaku Nishimura
Yoshiro Tsutsui
Kenjiro Hirayama

Discussion Paper 14-01

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANIntraday Return and Volatility Spillover Mechanism from
Chinese to Japanese Stock Market

Yusaku Nishimura
Yoshiro Tsutsui
Kenjiro Hirayama

Discussion Paper 14-01

January 2014

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANIntraday Return and Volatility Spillover Mechanism from
Chinese to Japanese Stock Market
Yusaku Nishimura†
Yoshiro Tsutsui‡
Kenjiro Hirayama§

Abstract
We analyze the mechanism of return and volatility spillover effects from the Chinese to the Japanesestock market. We construct a stock price index comprised of those companies that have substantial
operations in China. This China-related index responds to changes in the Shanghai Composite Index
more strongly than does the TOPIX (the market index of the Tokyo Stock Exchange). This result
suggests that China has a large impact on Japanese stocks via China-related firms in Japan.
Furthermore, we find evidence that this response has become stronger as the Chinese economy has
gained importance in recent years.

Keywords: return and volatility spillover; China related stock index; high-frequency data; intraday
periodicity; long memory
JEL Classification Number: G10, G14, G15†

Institute of International Economy, University of International Business and Economics, 10 East Huixin Street,
Chaoyang District, Beijing, 100029, China, e-mail: yusaku.nishimura@gmail.com‡
Graduate School of Economics, Osaka University, 1-7 Machikane-yama, Toyonaka, 560-0043 Japan, Phone:+81-6-6850-5223, Fax: +81-6-6850-5274, e-mail: tsutsui@econ.osaka-u.ac.jp§
School of Economics, Kwansei Gakuin University, Uegahara, Nishinomiya-shi 662-8501 Japan, Phone:+81-798-54-6438, Fax: +81-798-54-6438, e-mail: hiraken@kwansei.ac.jp

1. Introduction

In this paper we empirically investigate the intraday stock price spillover effects between
Japanese and Chinese1 stock markets, with a special focus on overlapping trading hours. We focus on
both the return and volatility of stock prices in this paper. There exist studies that analyze interactions
between the two markets using daily (Liu and Chen 2008; Nishimura and Men 2010) or
high-frequency (Nishimura et. al. 2012; Tsutsui and Hirayama 2013) data. Almost all of those studies
conclude that there is a one-way influence running from China to Japan. The main purpose of thispaper is to investigate the mechanism producing this one-way causality.
Most past studies of return and/or volatility spillover effects utilize intraday (opening andclosing price data; Hamao et. al. 1990, Bae and Karolyi 1994), daily (closing price data; Karolyi 1995,
Christofi and Pericli 1999, Caporale et. al. 2006) or longer-frequency data (Theodossiou and Lee 1993,
Longin and Solink 1995, Ng 2000). However, dramatic advances in communications and computer
technology now make it possible to obtain information from other markets extremely rapidly. If thestock market is informationally efficient, this should result in speedy responses in overseas markets.
Since Chinese and Japanese markets are simultaneously open for certain hours each day, it isextremely interesting to use these hours to analyze high-frequency spillover effects that cannot be
accurately captured using daily or longer-frequency data. In addition, if daily closing prices are used
to represent daily observations, the fact that the Chinese market closes just one hour after Tokyo
implies that causality from Japan to China will be over-emphasized relative to the opposite causality.
Tokyo’s closing price on the following day will be influenced by all other intervening major stock
markets, weakening or blurring the effects of Shanghai on Tokyo. Any positive finding of spillover
effects from Shanghai to Tokyo thus gains credibility. For this reason, recent analyses of spillover
1“China” in this paper is defined to be mainland China, excluding Hong Kong, Macau, and Taiwan.

1effects between the two neighboring markets with overlapping trading hours have begun to utilize
high-frequency data (Jeong 1999; Baur and Jung 2006; Égert and Kočenda 2007; Harju and Hussain
2008; Nishimura et. al. 2012; and Tsutsui and Hirayama 2013).
These recent studies make use of market indices to estimate the extent of stock price spillover
effects between different stock markets, but they have not focused on the factors that lead tointernational transmission of stock price changes. This paper, however, endeavors to analyze why andhow such transmission arises across national borders. Such an analysis has heretofore not been
undertaken by researchers in our field.
Tsutsui and Hirayama (2005) propose three possible causes of international stock pricespillover effects. In the first place, common global shocks may be buffeting firms across borders.
When this is the case, use of daily observations tends to reveal causation from Japan to Asia, toEurope, and to the Americas, because that is the order in which these markets open and close on agiven day. If one utilizes high-frequency intra-day data during overlapping trading hours, the effects ofcommon global shocks are likely to be observed almost simultaneously. In this case, the observed
one-way influence from the Chinese to the Japanese stock market (Nishimura et. al. 2012; Tsutsui andHirayama 2013) cannot be explained by the existence of common global shocks.
The second cause of inter-market correlation is that a large change in the stock price index of
one country usually receives a great deal of attention from investors of other countries like a‘‘sun-spot’’. This phenomenon seems to occur particularly at the time of market opening when thenews about other markets is processed, but is probably not likely during normal trading hours.
The third cause is portfolio adjustment by international investors. Portfolios of institutional
investors are significantly diversified across national boundaries, and international capital movements
caused by portfolio adjustments of these investors thus affect stock prices worldwide. In the case of
China, however, international capital flows are strictly regulated, and individual and institutional2

investors in China are not allowed to invest in overseas stock markets in principle (except for someinstitutional investors).2 Likewise, acquisition of Chinese stocks by foreign investors is also subject tostrict regulations.3 Therefore, portfolio adjustments cannot explain the stock price spillover effects
between China and Japan.
These three hypotheses of international stock price spillovers as proposed by Tsutsui andHirayama (2005) presume stock markets that are highly open to international investment. Thus, noneof the hypothesis is applicable to a regulated stock market such as China’s. To explain the one-way
stock price spillover effects from China to Japan, we propose an alternative route through transactions
by international investors who focus on Japanese companies that have substantial operations in China.
Since individuals and financial institutions in China cannot buy or sell Japanese shares, it is theseinternational investors who affect prices of those Japanese companies. We hypothesize in this paperthat their behavior is the primary mechanism of the one-way spillover from China to Japan.
Let us explain this point in greater detail. Suppose Chinese stock prices decline for some reason.
Since stock prices reflect information/expectations about the future developments, such a decline isinterpreted as a signal foretelling subsequent worsening economic conditions. Japanese companies
that operate in China will be affected adversely if the Chinese economy worsens situation. Therefore,
international investors will sell their shares in those Japanese companies, resulting in drops in theshare prices. Negative news about Chinese economy is, of course, bad news to the Japanese economy
2

Since 2006, China has partially opened the door to international investment to those institutions that have acquired
the status of QDII (Qualified Domestic Institutional Investor). As of March 31, 2013, the permitted amount of overseas
investment balance at these QDIIs is 84.127 billion US dollars (data source: The State Administration of Foreign
Exchange of the People's Republic of China); this amounts to only about 2.2% of the total market capitalization of theTokyo Stock Exchange, which stands at 3825.268 billion US dollars (data source: The Tokyo Stock Exchange formarket capitalization and the Bank of Japan for the US$/JPY exchange rate). Thus the degree of cross-borderinvestments by the Chinese is quite limited. Furthermore, not all permitted amount can be invested in risky assets.
Hence, the share of foreign stocks in QDII’s portfolios is only a tiny fraction of the total balance.
3
Foreign financial institutions are required to obtain the status of QFII (Qualified Foreign Institutional Investor) inorder to purchase Chinese stocks. The permitted amount of investment by QFII is 268.545 billion RMB (using theexchange rate of 6.1787 RMB per US dollar) which is equal to just 1.26% of the market capitalization of the Chinese
stock markets (Shanghai and Shenzhen combined). Consequently, the presence of foreign investors in China is quite
limited. The data on market capitalization was obtained from the websites of Shanghai and Shenzhen Stock Exchanges.
The amount of QFII permitted and the exchange rate data were obtained from the State Administration of Foreign
Exchange of the People's Republic of China.

3

overall. Thus, the broad market index will also tend to register a fall, but its extent should be smaller
than that of the shares of China-related companies. The difference in the extent of the return andvolatility spillover effects between the market index and the China-related index will be investigatedin this paper.
In recent years the importance of China is rising for many Japanese companies, which areconstrained by the shrinking domestic economy and ageing population. This may lead to a higher
sensitivity of Japanese stock prices to Chinese movements. We will analyze whether evidence of suchan evolution can be found in the data.
The structure of this paper is as follows. In Section 2, we delineate our hypotheses and theempirical procedures we use to test them. Section 3 describes our dataset. Section 4 presents theresults of statistical tests of our hypotheses on the return and volatility spillover effects between
Chinese and Japanese stock markets. Section 5 concludes the paper.

2. Methodology

We present our hypotheses regarding return and volatility spillovers from China to Japan in
Section 2.1, and explain our testing procedures in Section 2.2.

2.1 Hypotheses about Return/Volatility Spillover Effects from China to Japan
We first hypothesize that Tokyo’s overall market index (henceforth MI) and the China-related
Index (CRX) respond differently to changes in Chinese stock prices (represented by the Shanghai
Composite Index.) If international investors’ transactions in companies included in the CRX are themain cause of the one-way transmission from China to Japan, the CRX must respond to China morestrongly than does the MI. Let  l and l (l  MI , CRX ) denote the response of Japanese stock price4

returns and volatility, respectively, to Chinese stock price returns and volatility. Spillover effects may
in principle be present in both of these measures or in either or in neither; the empirical answer to thisquestion will be reported later in this paper.
We propose two separate hypotheses about the spillover effects.

Hypotheses 1: The response of return/volatility of the CRX ( CRX / CRX ) to Chinese stock
return/volatility is greater than that of the MI (  MI / MI ).

If this hypothesis is accepted, then it implies that the main factor behind the one-way spilloverfrom China to Japan is the behavior of international investors who buy/sell companies included in the
CRX.
As the Chinese economy grows rapidly, the business ties between Chinese and Japanesecompanies also become closer, which is expected to raise the sensitivity of the CRX against Chinese
stock price movements. Hence our next hypothesis:

Hypothesis 2: The responses of return and volatility of Japanese stock prices to those of Chinese stock
prices have become larger more recently.

2.2 Empirical Procedure
Tests of volatility spillover effects require some estimate of volatility, since this is not observed
directly in the market. As has been done in many studies in the field, we use as a measure of volatility
the conditional variance estimated in an ARCH-type model.
The volatility of high-frequency returns of stocks is also known to follow a long-memory process,as documented by many authors (Andersen and Bollerslev; 1997, 1998, among others). In this paper,5

we adopt the FIGARCH (Fractionally Integrated GARCH) model, which is an extension of the
GARCH (Generalized ARCH) model (Bollerslev, 1986) to capture the long-memory property of stock
price returns.4
The final model to estimate return and volatility spillover effects from the Chinese stock price(denoted by CHN) to either of the Japanese indices (denoted by l  MI , CRX ) is an
AR(k)-FIGARCH(p,d,q) model as follows:

rl ,t (i )  cl  l ( L)rl ,t (i )  l ( L)rCHN ,t (i )   l ,t (i ) ,  l ,t (i )   l ,t (i ) zl ,t (i ) , zl ,t (i ) ~ WN (0,1)



(1)



2
 l2,t (i )  l  1  1   l ( L)   l ( L)(1  L) d  l2,t ( i )  l ( L)ˆ CHN,t ( i )1(2)

Equation (1) is the mean equation. Since the 5-minute returns exhibit a high serial correlation, they aremodeledasanautoregressive

process.

In

theabove,

L

denotesthelagoperator:

Lp rt ( i )  rt ( i  p ) ( p  0,1,) where the subscripts t(i) refer to day t and intra-day sequential number i .
l ( L) and l ( L)are polynomials in L: l ( L)  1,l L  2,l L2    k ,l Lk

and l ( L)  1,l L  2,l L2     j ,l L j .  t (i ) is the error term and zt (i ) is a white noise with a zero mean and a varianceequal to 1.
The parameters 1,l , 2,l ,, j ,l in the polynomial l (L) represent the coefficients on theseasonally adjusted Chinese stock returns rCHN ,t (i 1) , rCHN ,t (i  2) ,, rCHN ,t (i  j ) . Thus, the parameters  j ,lmeasure the degree of responsiveness of the Japanese stock return (either rCRX or rMI ) to lagged
Chinese returns up to previous j periods. If buying and selling of shares in the CRX are the cause of
the spillover effects from China to Japan, Hypothesis 1 implies that 1,CRX     j ,CRX 

1, MI     j , MIandHypothesis

2

implies

that

now
past
past
1,now
CRX     j , CRX  1, CRX     j , CRX

now
past
past
( 1,nowMI     j , MI  1, MI     j , MI ) where the superscript now refers to a more recent sub-period and4

Baillie et al. (1996) first proposed the FIGARCH model. Chung (1999) pointed out a structural problem with thismodel and proposed an improved version, which we adopt in this paper. See for more details Laurent and Perets (2002)
and Xekalaki and Degiannakis (2010).

6

past to an older sub-period.
Equation (2) is the variance equation of a FIGARCH model. l ( L), l ( L), and l ( L) arepolynomials in L :  l ( L )  1,l L   2,l L2     p ,l Lp ,  l ( L )  1,l L   2,l L2     q ,l Lq , andl ( L )  1,l L  2,l L2     j ,l L j . The parameter, d, is the key to the FIGARCH model. It captures the
dynamic properties of volatility. Namely, if d = 0, the volatility follows a short-memory process, and if
0  d  1 , it follows a long-memory process. The parameters 1,l , 2,l ,,  j ,l in the polynomial2
l (L) measure the effect of lags of estimated Chinese volatilities, ˆ CHN
ˆ2
ˆ2,, t ( i 1) ,  CHN , t ( i  2) , ,  CHN , t ( i  j )on the Japanese volatility. Under Hypothesis 1 we will observe that 1,CRX     j ,CRX 

1, MI     j , MI

and

under

Hypothesis

2

thefollowing

inequality

should

hold:

now
past
past
now
now
past
past
1,now
CRX     j , CRX  1, CRX     j , CRX ( 1, MI     j , MI  1, MI     j , MI ).

In order to examine the spillover effects between the two countries, it is natural to augment
equations (1) and (2) with the following companion equations:rCHN ,t (i )  cCHN  CHN ( L)rCHN ,t (i )  CHN ( L)rl ,t (i )   CHN ,t (i )





d
2
2
ˆ2
 CHN
 CHN
,t ( i )  CHN  1  1   CHN ( L )   CHN ( L )(1  L ), t ( i )  CHN ( L ) l , t ( i )
1

(3)

(4)

Since this model does not address the issue of the long-memory property, the resultant residuals andtheir squares exhibited serial correlation.5 Thus we decided to use a FIGARCH model to tackle theproblem of the long-memory property of stock returns.
However, a bivariate AR model can be reduced to a univariate AR model (Yamamoto 1988;
Zellner and Palm, 1974; Enders, 2003). This is accomplished by substituting out one variable byutilizing the information on the other variable from the second equation. The resultant equation
contains one AR term and two MA processes. The sum of two independent moving average processes
5Equations (1) to (4) constitute a VAR-Multivariate FIGARCH, but unfortunately a feasible estimation method of thissystem is not yet known. Thus, we instead estimated a multivariate BEKK-GARCH model proposed by Engle andKroner (1995). Since this model does not address the issue of the long-memory property, the resultant residuals andtheir squares exhibited serial correlation.

7

can be expressed as one moving average process (Box and Jenkins, 1976, p. 121) and if this MA
process is invertible, the equation can be written as an AR(∞). Assuming this is the case with oursystem of (1) and (3), we derive a univariate AR model for RCHN ,t (i ) as in the following (3’). Since it
is not possible as well as not practical to estimate an infinite number of AR coefficients, we cut off at
lag k. Thus, we utilize a univariate AR(k)-FIGARCH(p, d, q) model to estimate the volatility of theChinese stock market.

rCHN ,t (i )  cCHN  CHN ( L)rCHN ,t (i )   CHN ,t (i )



(3’)



d
2
2
 CHN
 CHN
,t ( i )  l  1  1   CHN ( L )   CHN ( L )(1  L ),t (i )
1

(4’)

where  CHN ,t (i )   CHN ,t (i ) zCHN ,t (i ) and zCHN ,t (i ) is a mean zero white noise with a unit variance.
Estimations (3’) and (4’) give us a conditional variance which can be substituted into the right-handside of equation (2) to test for volatility spillover from China to Japan.6 Compared with simultaneous
estimations of (1) through (4), this is a two-stage estimation procedure. This has been adopted bymany researchers in the field such as Hamao et. al. (1991), Bae and Karolyi (1994), Lin et al. (1994),
Ng (2000), Kim (2005), Baur and Jung (2006), etc.
There is an alternative justification for estimating (3’) and (4’). As was stated in Section 1, manystudies find there is a one-way return/volatility spillover from China to Japan. Therefore, even if (3)and (4) are estimated, the coefficients on rl ,t (i ) and on ˆ l2,t (i ) , CHN and CHN , will turn out to be
insignificant. Excluding these variables from the beginning, namely estimating (3’) and (4’), would
produce little bias in estimation. Consequently, instead of using a multivariate model, we apply a
two-stage estimation to univariate models.
To determine the lag orders of k, p, q we follow a conventional method applied by many

6

Considering the possibility that the standardized residuals do not follow normal distribution, we usedquasi-maximum likelihood estimation. For details of this estimation method, see Sec. 2.2 of Xekalaki and Degiannakis(2010).

8researchers. To determine the lag order k of the AR part, we increase it sequentially from 1 to 5 andchoose the value k* which minimizes the AIC (Akaike Information Criterion). We then compute fourcombinations of p and q, each of which varies between 1 and 2. Out of these four estimation results,
we choose p* and q* that minimize the AIC. With k*, p*, and q* thus determined, we estimate theAR(k*)-FIGARCH(p*, d, q*) model to compute standardized residuals and their squares. These twoseries are subjected to Ljung-Box tests (Ljung and Box, 1978) and LM ARCH tests (Engle, 1982) tocheck for serial correlation and ARCH effects. If these tests are satisfactory, the model is regarded asvalid, but if not, the lag order of k* is increased and the next AR(k*+1)- FIGARCH(p*, d, q*) model isestimated, and so forth.

3. Data
For the empirical analysis we use a representative Chinese stock price index and two Japanesestock price indices (Market Index and China-related Index) at 5-minute intervals.7 Log differences are
multiplied by 100 to compute returns. We utilize Shanghai Composite Index (SHCOMP) as therepresentative Chinese stock price index. The 5-minute data were retrieved from FoxTrader. The
Tokyo Stock Price Index (TOPIX) is used as the Market Index (MI) for Japan. It is acapitalization-weighted index of all the companies listed in Section 1 of the Tokyo Stock Exchange.
We also compiled our own China-related Stock Index (CRX) based on 50 companies whose
7
The reason for adopting a 5-minute frequency is two-fold. First, we need to strike a balance between the greater
degree of freedom provided by higher frequency (e.g., minute-by-minute or even shorter observation intervals) andincreasing market microstructure noise (e.g., bid-ask bounces) arising at such a frequency. The 5-minute frequency isrecommended as optimal by Andersen et al. (2001), Koopman et al. (2005) and many others. These and other authors
have used 5-minute returns in analyzing international return/volatility spillover effects (Jeong, 1999; Égert andKočenda, 2007; Harju and Hussain 2008; Nishimura et. al. 2012). The second reason for using a 5-minute frequency is
to deal with the high serial correlation at 5-minute intervals apparently caused by a peculiar system called ‘Special
Quotes’ in the Tokyo Stock Exchange (Tsutsui et al. 2007). A special quote is announced whenever the next transaction
price is likely to exceed a certain preset range. At the same time, buy/sell matching is halted in order to lure potential
buyers/sellers into this stock. If this signaling fails to induce an actual transaction, the special quote is revised upward
or downward by one notch after 5 minutes. Tsutsui et al. (2007) report that a sizable number stocks are subject to these
special quotes, resulting in spikes in autocorrelations in the minute-by-minute Nikkei 225 Index returns at 5-minuteintervals. We have to be wary, however, of the spurious nature of this phenomenon, because transactions are halted
when a special quote is announced. One cannot execute buy/sell orders at these special quotes. Thus, use of the
5-minute frequency is desirable to circumvent this microstructure noise.

9businesses are closely linked to the Chinese economy. We retrieved stock prices at 5-minute intervals
for these 50 companies and computed a capitalization-weighted index of these shares (see Appendix A
for details of this procedure). The source of these data is tick data released by NIKKEI Digital Media,
Inc.
Our sample runs from November 4, 2003 to November 18, 2011.8 The Tokyo Stock Exchange(TSE) is open for trading for four and a half hours and the Shanghai Stock Exchange (SSE) for fourhours every day. See Figure 1 for the specific trading hours of the TSE and SSE. Note that Japan
Standard Time (JST) is one hour ahead of China Standard Time (CST). Excluding overnight andlunch-time returns, there are 54 5-minute returns for MI and CRX and 48 returns for SHCOMP.
During our sample period, there were 1973 trading days for Tokyo and 1955 days for Shanghai,
resulting in 106542 observations in total for Tokyo and 93840 observations for Shanghai.
The volatility of intraday high-frequency stock returns is known to have intraday periodicity.9 As
is pointed out by many researchers such as Andersen and Bollerslev (1997, 1998) and Andersen et al.
(2000), this intraday periodicity may produce a serious bias in estimation. We adjust 5-minute returns
data for this periodicity by Flexible Fourier Form (FFF) as proposed by Gallant (1981) and used
extensively by Andersen and Bollerslev (1997, 1998). Appendix B provides a detailed description ofthis method. The adjusted 5-minute returns, however, still exhibited a long-memory property and thisis the reason we adopt a FIGARCH model in this paper.
To analyze mutual return/volatility spillover effects, we extract data points during theoverlapping trading hours each day between the Tokyo and Shanghai Stock Exchanges. The TSE isopen for trading between 9:00-11:00 (Japan Standard Time, JST) for the morning session and between
8

Beginning on November 21, 2011, the morning session of the TSE was extended to 11:30 instead of 11:00. Since themorning session starts at 9 am, it thus became two and a half hours long. This constitutes a rather grave change in the
trading environment, thus we decided to end the sample period at this time, namely November 18, 2011.
9
Andersen and Bollerslev (1997) find a U-shaped intraday periodicity in the S&P 500 futures return volatility andAndersen et. al. (2000) report a doubly U-shaped intraday periodicity in the return volatility of the Nikkei 225 Index.
Nishimura et. al. (2012) also point out a strong intraday periodicity in the return volatility of SHCOMP.

10

12:30-15:00 (JST) for the afternoon session. The SSE is open between 10:30-12:30 (JST) for themorning session and between 14:00-16:00 (JST) for the afternoon session. Therefore, there is a30-minute interval in the morning (10:30-11:00 JST) and a one-hour interval in the afternoon(14:00-15:00 JST) when the two stock exchanges are open at the same time (Figure 1).

[Insert Figure 1 about here]

Table 1 displays basic descriptive statistics for returns data during the overlapping trading hours.
The data are adjusted for intraday periodicity. There are 33,444 5-minute returns data during theoverlapping trading hours. The means of the intraday returns are not significantly different from zero.
The excess kurtosis and skewness are both significantly different from the values of a Normal
distribution at the 1% significance level (0 and 3 respectively), indicating that these returns have fatter
tails than a normal distribution.

LB10 and LB102 are the Ljung-Box statistics to test the null hypothesis that autocorrelation
coefficients in the returns and their squares up to 10 lags are all zero. The results indicate that both
returns and their squares exhibit a high degree of serial correlation.

[Insert Table 1 about here]

4. Are Transactions of China-related Shares the Cause of Return/Volatility Spillover Effects?
Here we report the results of hypothesis tests on the transmission mechanism from the Chinese
stock market to Japanese market. Section 4.1 first presents the estimated volatility in SHCOMP.
Section 4.2 displays the test results for Hypothesis 1 discussed in Section 2.1. Section 4.3 applies apath analysis to further investigate return spillover effects. Section 4.4 gives the results for Hypothesis
2 presented in Section 2.1.

11

4.1 Estimated Volatility of SHCOMP
We first determined the lag order k of AR(k)-FIGARCH(p, d, q) model as delineated in Section
2.2. We chose lag 4 as the optimal k*. We then tested for p and q to minimize the AIC and obtained
p=1 and q=1 as the optimal values. We thus estimate an AR(4)-FIGARCH(1, d, 1) model for our
hypothesis test. Table 2 displays estimation results for SHCOMP. All of our inferences are based on
robust standard errors from the quasi-maximum likelihood estimation, because the distribution ofstandardized residuals may not follow a normal distribution.

[Insert Table 2 about here]

The parameter d, which determines the long-memory property, is estimated to be 0.3950 at the
1% significance level. This result indicates that the intraday volatility of SHCOMP follows along-memory process.
We test for autocorrelation and ARCH effects in the standardized residuals. LB10 and LB102 are
the Ljung-Box statistic, which tests the null hypothesis that the autocorrelation coefficients from lag 1
to lag 10 of the standardized residuals and their squares are all zero. According to this table, the null
hypothesis of no autocorrelation cannot be rejected in any of these variables. The ARCH LM test
statistic, LM 10ARCH , can be used to test the null hypothesis that there is no ARCH effect up to lag order
10. The LM 10ARCH test statistic indicates that this hypothesis cannot be rejected at the 5% significancelevel, suggesting that there is no ARCH effect remaining in the standardized residuals. These results
provide support for the specification of the selected AR-FIGARCH model.
We now regard the conditional variance estimated by the variance equation as a measure of thevolatility of SHCOMP, and proceed to analyze the return/volatility spillovers between China andJapan.

12

4.2 Is the Spillover Effect of Chinese Stock Market Different between the China-related Index and
the Market Index?
We repeat the same procedure described above to determine the lag order of theAR(k)-FIGARCH(p, d, q) model, as explained in Section 2.2. The model applied to the CRX turned
out to be AR(4)-FIGARCH(1, d, 1) and that of the MI was AR(30)-FIGARCH(1, d, 1). Given this2
model, we then determin the lag order of the exogenous variable ( r CHN ,t (i ) and ˆ CHN,t ( i ) ) on theright-hand side of equations (1) and (2) by minimizing the AIC. The optimal lag order turned out to be
1 for the CRX model and 3 for the MI model. The results of our test of Hypothesis 1 are presented inTable 3. 10 The Ljung-Box statistics indicate non-rejection of the null hypothesis of no serial
correlation in the residuals and their squares. The ARCH LM test also indicates that there is no ARCH
effect. Hence, our model can be regarded as suitably specified.
We first consider return spillover effects. The sum of the parameters indicating spillover fromSHCOMP to MI, 1, MI  2, MI  3, MI , is 0.0147.11 The parameter of spillover effect from SHCOMP to
CRX, 1,CRX , is 0.0315, which is significant at the 1% level. This parameter is more than twice thesum of the parameters on MI, vindicating Hypothesis 1. This result suggests that the impact of China
on China-related firms is stronger than its impact on the other firms.
Next, we find that the parameters to capture the volatility spillovers are almost all insignificant
statistically. The volatility of SHCOMP does not affect the volatility of either CRX or MI, leading us
to conclude that there is no observable volatility spillover from China to Japan.

4.3 Return Spillover Mechanism: Further Investigation
While SHCOMP affects CRX with only one lag, its statistically significant effect on MI is observed

10
11

To make the table readable, estimated coefficients on the autoregressive terms are omitted from the table.
If we add statistically significant parameters – i.e., only (  1, M I   3, M I ) – the value is 0.0154.

13

with lag 1 and lag 3. What does this difference imply? We should note that changes in MI may include
those in CRX. Thus, when SHCOMP affects MI, this may arise through stocks included in CRX
and/or through stocks not included in CRX.
To compare these two possible channels of spillover effects, we added the current value and afirst lag of CRX ( rCRX ) to the right-hand side of the MI return equation, and run an OLS regression.
The reason for adding the current value of CRX is that the China-related stocks included in CRX arealso part of the MI. In other words, changes in CRX also represent simultaneous changes in MI. If
SHCOMP affects MI through changes in CRX, then CRX must be significant and the significance ofSHCOMP would be lost in the return equation. The estimation results are displayed in Table 4. The
first column presents OLS estimates of equation (1). (Note, however, coefficients on the own lags ofMI are omitted to save space.) The first lag of SHCOMP is considerably significant and the second lagis also significant at the 5% level. The second column (labeled “Model A”) presents results when thecurrent value and the first lag of CRX are added to the right-hand side. The estimated coefficients onthese additional variables are not only highly significant but also quite large numerically. The
coefficient on the first lag of SHCOMP decreases dramatically (about 1/8 of its value in the Base
Model) and it becomes insignificant statistically. However, the coefficient on the third lag of
SHCOMP remains significant at the 5% level. We also tried adding two more lags of CRX to theequation, and the estimation results are shown in the columns (labeled “Model B”). Parameter
estimates remain essentially the same as in Model A. This result implies that the effect of the first lagof SHCOMP on MI as revealed by Table 3 actually works entirely through the influence of CRX
stocks. The effect of SHCOMP on MI arises with a three-period lag possibly through effects oncompanies not included in CRX.
To repeat, the first lag of SHCOMP affects MI mainly through effects on CRX, and has little
direct effect on stocks other than those in CRX. This result can be verified by a path analysis (Asher,14

1983). For simplicity’s sake, we suppose that MI is affected by its own first lag, the current value of
CRX, and a first lag of SHCOMP, and that CRX is affected by its own first lag and the first lag of
SHCOMP. The results of the path analysis are graphed in a path diagram (Figure 2). Numerical valuesof this diagram do not change substantially even if we increase the lag orders of MI and CRX. The
diagram shows that the direct effect of SHCOMP on MI is about 1/10 of the indirect effect throughCRX. Thus, the spillover effect from SHCOMP to MI arises largely through effects on CRX.
Test results in this subsection give evidence that spillover effects from China to Japan are mainly
caused by transactions by international investors in China-related Japanese stocks, and these spillovers
are observed in returns but not in volatility.

[Insert Table 4 and Figure 2 about here]

4.4 Are Spillover Effects Getting Stronger in Recent Years?
This subsection presents test results of the hypothesis that, as the Chinese economy grew in its
importance in the world, its spillover effects became stronger. To be specific, we estimate our modelfor each one-year interval sequentially and compare the parameter estimates across subsamples. Since
there are many studies such as Rozeff and Kinney (1976) and Ariel (1987) that report existence of
seasonal patterns in spillover effects, we chose a one-year subsample to average out seasonalpatterns.12
Table 5 displays the estimation results of the model we use to examine return and volatility
spillovers from SHCOMP to CRX, and Table 6 shows those from SHCOMP to MI. In all thesubsamples, the lag orders of the AR(k)-FIGARCH(p,d,q) model were determined by the same
method as in the previous subsections. In the second row of the tables are shown optimal lag orders

12

Many such anomalies have been found in stock markets, e.g. day of the week effects, monthly effects, and thehalf-year effects, etc.

15

for each subsample.13[Insert Table 5 about here][Insert Table 6 about here]

The parameter  , which captures return spillover effects, is statistically significant at lag 1 (i.e.

1 ) both in the CRX and MI equations. The influence of SHCOMP gradually increases over time inboth CRX and MI returns. For example, in 2004, the parameter 1 is -0.0004 in the CRX equation(0.0009 in the MI equation) and it is not significantly different from 0. It starts to take on positive,
significant values in 2006, gradually grows in magnitude, and eventually reaches 0.0661 (0.0389 in
the MI equation), which is significant at the 1% level in 2011.
The path of  as time elapses is plotted in Figure 3, in order to intuitively understand thephenomenon. The estimate of  in the CRX equation is plotted as a solid line and that of the MI
equation a dotted line. Both grew larger over time. This is consistent with Hypothesis 2. The responseof CRX is also larger than that of MI. The difference between the two parameters is also plotted in the
figure, and it also grows wider in more recent years.
We find no clear evidence of increasing magnitude of the parameter  (which measuresvolatility spillovers) in Tables 4 and 5. The result with the entire sample in Section 4.2 that spillover
effects are observed only in returns but not in volatility thus holds true in the case of annual
subsamples as well.

[Insert Figure 3 about here]

13

Only in one case did we fail to eliminate serial correlation and ARCH effects, even though we increased theautoregressive lags up to 50; this was the subsample of year 2010 for the MI model. For this subsample, the lags were
determined by the minimum AIC principle.

16

5. Conclusion
In this paper we measured international stock price spillovers by focusing on the relationshipbetween the Chinese and Japanese stock markets. The Chinese stock market is special, because it
affects other markets while not being greatly affected by them. This is probably due to the fact that
Chinese ownership of foreign stocks is forbidden and foreign ownership of Chinese stocks is strictly.
In other words, the Chinese stock market is a striking natural experiment in one-way international
spillovers. A second special feature of this paper is the use of high-frequency data during overlapping
trading hours between the two countries; this enables us to examine real-time spillover effects.
Beyond merely identifying spillover effect, we investigated the mechanism by which spillover effects
arise. We advanced the hypothesis that the international investors who trade China-related stocks inJapan in response to Chinese stock prices are the agents of the return/volatility spillovers from China
to Japan. To test this hypothesis we constructed our own high-frequency China-related stock priceindex (CRX) from 50 Japanese companies with substantial operations in China. We estimated thereturn/volatility spillovers between the Shanghai Composite Index (SHCOMP) and CRX, andcompared these to spillovers between SHCOMP and the Japanese market as a whole. We hypothesizedthat changes in Chinese stock prices should lead to larger changes in CRX than in the Japanese Market
Index (MI).
Our results indicate that CRX responds to SHCOMP more strongly than does MI. This vindicates
our hypothesis that the return spillover effects are more pronounced in China-related stocks. In
particular, the immediate effect of SHCOMP arises only in China-related stocks with a one-period lag,and the effect of SHCOMP on other companies occurs with a three-period lag. Volatility spillover
effects were, however, not observed. Information transmission from China to Japan is detected mainly
in returns, but not in volatility.
We also tested a hypothesis that the Chinese influence on Japan has been getting stronger more17

recently. Our estimation again revealed no significant volatility spillovers, but the return spillover
effects from SHCOMP to either CRX or MI indeed became larger in recent years. Also, the responseof CRX has became stronger relative to MI over the years. As the Chinese economy surpassed Japanin the size of real GDP to become the second largest economy in the world, the exposure of
China-related Japanese companies to China increased proportionately, giving rise to stronger
spillovers from China to Japan.
One problem with our approach in this paper is its use of MI. MI is a stock price index based on
all the companies listed in Section 1 of the TSE (1749 companies as of October 2013); thus it includes
the 50 companies included in the China-related index we constructed. Responses of MI to China
include those of non-CRX stocks. While the estimations of Section 4.3 have shown that CRX
responds to SHCOMP immediately and that stocks not included in CRX respond with a three-periodlag, the examination of this mechanism would be easier and more direct if we had a non-CRX index tocapture the delayed responses. However, compilation of such an index is too demanding and daunting
for us to achieve in this paper. 14 This is left as a future research item.

14

One might think that non-CRX index can be easily calculated as a difference between the TOPIX and the CRX, but
this is not the case because the TOPIX is not a simple weighted average but is adjusted for the so-called Free Float
Weight (the proportion of shares held for short-term investment horizon), which requires close examination ofshareholder distribution of each stock.

18

Appendix A. Compilation of China-related Stock Index
The list of companies included in our China-related stock index is entirely based on thecompanies adopted in the Nikkei China Related Stock Index 50, compiled and released by the Nikkei,
Inc. See Appendix Table A below for the details.
Our intraday tick data database contains around 30 or 40 billion records for over 1700 companies
listed in the TSE for the period from November 4, 2003 to November 18, 2011 (1973 trading days).
We extracted at 5-minute intervals prices of the 50 companies included in the CRX and computed acapitalization-weighted average of those 50 prices. Specifically, we computed the index as follows:
50

CRX t ( i )    tl( i ) ptl( i ) , where
l 1

l 

Vt l ptl(i )

 Vp
50

l 1 tllt (i ).

lptl(i ) is the stock price of company l on day t and time sequence i, and Vt is the number of shares on

day t for company l. Price data were taken from the “Tick Stocks Multiple Quotes” database released
by Nikkei Shimbun Digital Media. The data on the number of shares was extracted from Nikkei
NEEDS Financial-QUEST.
In constructing CRX, we encountered a few problems. We list below those problems and how wedealt with them.
At the beginning of our sample period (November 4, 2003), not all of the 50 companies werelisted. Seven & I Holdings (Code: 3382) went public in September 2005, Mitsubishi Chemical
Holdings (Code: 4188) in October 2005, and JX Holdings (Code: 5020) in April, 2010. Before
these IPOs, therefore, the CRX does not include these companies.



There are some stocks at certain time points during a given day for which there is no price data
due to no successful matching of orders. We filled this missing observation with the most recent
value. We may justify this treatment on the grounds that, since no new information is produced,
the old price information can be regarded as valid. Jeon and von Furstenberg (1990) adopt this19method for filling bank holidays, albeit at the daily frequency.


Between January 19, 2006 and April 21, 2006 the afternoon session started at 13:00 instead of
12:30 in order to process heavy trading volume in the morning session. This was caused by acorporate scandal involving the then-popular IT startup company called “Livedoor.” Since nodata were available for this half hour between 12:30 and 13:00, we copied the closing price of themorning session to this period.



Until January 4, 2008, there was only a morning session for the first trading day (January 4) andthe last trading day (December 30) of the year. For these days, all the values for the afternoon
session were filled by the closing price of the morning session.

20

Appendix Table A.
Security

List of Companies Included in the China-related Index
Security

Company Name

Code

Company Name

Code

2502

Asahi Group Holdings, Ltd.

6503

Mitsubishi Electric Corporation

2503

Kirin Holdings Company, Limited

6701

NEC Corporation

2802

Ajinomoto Co., Inc.

6752

Panasonic Corporation

3382

Seven & I Holdings Co., Ltd.

6753

Sharp Corporation

3402

Toray Industries, Inc.

6762

TDK Corporation

4005

Sumitomo Chemical Company, Limited

6902

Denso Corporation

4063

Shin-Etsu Chemical Co., Ltd.

6954

FANUC Corporation

4183

Mitsui Chemicals, Inc.

6971

Kyocera Corporation

4188

Mitsubishi Chemical Holdings Corporation

6981

Murata Manufacturing Company, Ltd.

4452

Kao Corporation

7011

Mitsubishi Heavy Industries, Ltd.

4911

Shiseido Company, Limited

7201

Nissan Motor Co., Ltd.

5020

JX Holdings, Inc.

7203

Toyota Motor Corporation

5108

Bridgestone Corporation

7267

Honda Motor Co., Ltd.

5201

Asahi Glass Company, Limited

7731

NIKON Corporation

5401

Nippon Steel & Sumitomo Metal Corporation

7751

CANON Inc.

5405

Sumitomo Metal Industries, Ltd

8001

ITOCHU Corporation

5411

JFE Holdings, Inc.

8002

Marubeni Corporation

5713

Sumitomo Metal Mining Co., Ltd.

8031

Mitsui & Co., Ltd.

5802

Sumitomo Electric Industries, Ltd.

8035

Tokyo Electron Limited

6301

Komatsu, Ltd.

8053

Sumitomo Corporation (Sumitomo Shoji Kaisha, Ltd.)

6305

Hitachi Construction Machinery Co., Ltd.

8058

Mitsubishi Corporation

6326

Kubota Corporation

8113

Unicharm Corporation

6367

Daikin Industries, Ltd.

8267

AEON Co., Ltd.

6501

Hitachi, Ltd.

9104

Mitsui O.S.K.Lines, Ltd.

6502

Toshiba Corporation

9983

Fast Retailing Co., Ltd.

Source: Nikkei, Inc., http://indices.nikkei.co.jp/en/nkave/index/component?idx=nkcrs50

21Appendix B

Removal of Intraday Periodicity by FFF

We removed the intraday periodicity in the 5-minute return volatility by applying Flexible
Fourier Form (FFF). 15 Figure A below plots the mean volatility at 5-minute intervals (absolute valuesof 5-minute returns during one day were averaged over the sample period) before and after theseasonal adjustment for the three indices, MI, CRX, and SHCOMP. 16 Figure B plots the
autocorrelation functions of absolute 5-minute returns up to 540 lags for MI and CRX and up to 480
lags for SHCOMP. The solid line in these Figures plots the original series, which exhibits apronounced pattern of intraday periodicity. The dotted line exhibits the adjusted series, which does notshow any intraday periodicity. The removal of intraday periodicity has therefore been achieved quite
successfully by applying FFF.
One notable feature is that the volatility of all three indices is highly serially correlated. The
dotted lines of Figure B all lie far above the 5% critical value and decline very slowly over the 10-day
period. This suggests that these series follow a long-memory process.

15

For details of the FFF, see Andersen and Bollerslev (1997) Appendix B, pp.152-155 or Andersen and Bollerslev(1998) pp.235-239.
16
Following Andersen and Bollerslev (1997, 1998), Andersen et al. (2000) and many others, we proxied the volatilityby the absolute values of returns.

22

.20

.35

.30

TOPIX
.18

CRX

SHCOMP

.30
.25

.16

.25

.14
.20

.20

.12
.15

.10

.15
.10

.08
.06

.05
5

10

15

20

25

30

35

40

45

50

.10
5

10

15

20

25

30

35

40

45

50

5

10

15

20

25

30

35

40

45

Figure A. Mean Intraday Volatilities at 5-minute Intervals
Notes: The absolute values of original 5-minute returns, | rt ( i ) | , are plotted in the solid line and their periodicity-adjusted series are plotted in the dotted line. They are averaged
across days over the entire sample period. There are 54 such values per day for MI and CRX and 48 for SHCOMP.

.35

.4

.28
CRX

TOPIX
.30

SHCOMP
.24

.3

.25

.20

.20

.16
.2

.15

.12

.10

.08

.1

.05

.04
.0

.00
50 100 150 200 250 300 350 400 450 500.00
50 100 150 200 250 300 350 400 450 500

50

100 150 200 250 300 350 400 450

Figure B. Autocorrelation Coefficients of Intraday Volatility
Notes: Autocorrelation coefficients of the absolute values of original 5-minute returns are displayed by the solid line and those of the adjusted values by the dotted line. The
horizontal axis measures lags up to 540 for MI and CRX (10 days times 54 per day) and up to 480 for SHCOMP (10 days times 48 per day). The straight dotted line depicts the 5%
significance level of the null hypothesis that each autocorrelation coefficient is zero.

23

References
Andersen, T. G. and T. Bollerslev (1997), “Intraday Periodicity and Volatility Persistence in Financial
Markets,” Journal of Empirical Finance, 4(2-3), pp. 115-158.
Andersen, T. G. and T. Bollerslev (1998), “Deutsche Mark–Dollar Volatility: Intraday Activity
Patterns, Macroeconomic Announcements, and Longer Run Dependencies,” The Journal of

Finance, 23(1), pp.219-265.
Andersen, T. G., T. Bollerslev and J. Cai (2000), “Intraday and Interday Volatility in the Japanese
Stock Market,” Journal of International Financial Markets, Institutions and Money, 10(2),pp.107-130.
Andersen, T. G., T. Bollerslev, F. X. Diebold, and P. Labys (2001), “The Distribution of Exchange
Rate Volatility,” Journal of the American Statistical Association, 96, pp.42-55.
Ariel, R. A. (1987), “A Monthly Effect in Stock Returns,” Journal of Financial Economics, 18(1),pp.161-174
Asher, H. B. (1983), Causal Modeling, Newbury Park CA: Sage Publications Inc.
Bae, K-H. and G. A. Karolyi (1994), “Good News, Bad News and International Spillovers of Stock
Return Volatility between Japan and the U.S.,” Pacific-Basin Finance Journal, 2(4), pp.
405-438.
Baillie, R. T., T. Bollerslev and H. O. Mikkelsen (1996), “Fractionally Integrated Generalized
Autoregressive Conditional Heteroskedasticity,” Journal of Econometrics, 74(1), pp.3-30.
Baur, D. and R. C. Jung (2006), “Return and Volatility Linkages between the US and the German
Stock Market,” Journal of International Money and Finance, 25(4), pp.598-613
Bollerslev, T. (1986), “Generalized Autoregressive Conditional Heteroskedasticity,” Journal of

Econometrics, 31(3), pp.307-327.
Box, G. E. P. and G. M. Jenkins (1976) Time Series Analysis: Forecasting and Control, San Francisco:24Holden-Day.
Caporale, G. M, N. Pittis and N. Spagnolo (2006), “Volatility Transmission and Financial Crises,”

Journal of Economics and Finance. 30(3), 376-390.
Christofi, A. and A. Pericli (1999), “Correlation in Price Changes and Volatility of Major Latin
American Stock Markets”, Journal of Multinational Financial Management, 9(1), pp.79-93.
Chung, C-F (1999), “Estimating the Fractionally Integrated GARCH Model,” Working Paper, Instituteof Economics, Academia Sinica, 1999.
Égert, B. and E. Kočenda (2007), “Interdependence between Eastern and Western European Stock
Markets: Evidence from Intraday Data,” Economic Systems, 31(2), pp.184-203.
Enders, W. (2003), Applied Econometric Time Series, 2nd ed., Hoboken, NJ: Wiley.
Engle, R. F. (1982), “Autoregressive Conditional Heteroskedasticity with Estimates of the Variance ofUnited Kingdom Inflation,” Econometrica, 50(4), pp.987-1007.
Engle, R. and F. K. Kroner (1995), “Multivariate Simultaneous Generalized ARCH,” Econometric

Theory, 11(1), pp.122-150.
Gallant, A. R. (1981), “On the Bias in Flexible Functional Forms and an Essentially Unbiased Form:
The Fourier Flexible Form,” Journal of Econometrics, 15(2), pp.211-245.
Hamao, Y., R. W. Masulis and V. Ng, (1990), “Correlations in Price Changes and Volatility acrossInternational Stock Markets,” Review of Financial Studies, 3(2), pp.281-307.
Harju, K., and S. M. Hussain (2008), “Intraday Return and Volatility Spillovers Across International
Equity Markets,” International Research Journal of Finance and Economics, 22, pp.205-220.
Jeon, B. N. and G. M. Von Furstenberg (1990), “Growing International Co-Movement in Stock Price
Indexes ,” Quarterly Review of Economics and Business, 30(3), pp.15-30.
Jeong, J.-G. (1999), “Cross-border Transmission of Stock Price Volatility: Evidence from theOverlapping Trading Hours,” Global Finance Journal, 10(1), pp.53-70.

25

Karolyi, G. A. (1995), “A Multivariate GARCH Model of International Transmissions of Stock
Returns and Volatility: The Case of the United States and Canada,” Journal of Business andEconomic Statistics, 13(1), pp.11-25.
Kim, S-J. (2005), “Information Leadership in the Advanced Asia–Pacific Stock Markets: Return,
Volatility and Volume Information Spillovers from the US and Japan,” Journal of the Japaneseand International Economies, 19(3), pp.338-365.
Koopman, S. J., B. Jungbacker, and E. Hol. (2005), “Forecasting Daily Variability of the S&P 100
Stock Index Using Historical, Realised and Implied Volatility Measurements.” Journal of

Empirical Finance, 12(3), pp.445-475.
Laurent, S. and Perets J. P. (2002) “G@RCH 2.2: An Ox Package for Estimating and Forecasting
Various ARCH Models,” in M. McAleer and L. Oxley (eds.), Contributions to Financial

Econometrics: Theoretical and Practical issues, pp.211-250, Oxford: Blackwell Publishing Ltd.

Lin, W. L., R. F. Engle and T. Ito (1994), “Do Bulls and Bears Move Across Borders?
International Transmission of Stock Returns and Volatility as the World Turns,”
Review of Financial Studies, 7(3), pp. 507-538.
Liu, C. and S. Chen (2008), “The Volatility Spillover between Chinese and Major International Stock
Markets: New Evidence from a Causality-in-Variance Approach,” World Economic Papers, 5,
pp.30-44. (in Chinese)
Ljung, G. M. and G. E. P. Box (1978), “On a Measure of Lack of Fit in Time Series Models,”

Biometrika, 65(2), pp.297-303.
Longin, F. and B. Solink (1995), “Is the Correlation in International Equity Returns Constant: 1960–
1990?” Journal of International Money and Finance, 14(1), pp. 3–26.
Ng, A. (2000), “Volatility Spillover Effects from Japan and the US to the Pacific–Basin,” Journal ofInternational Money and Finance, 19(2), pp. 207–233.

26

Nishimura, Y. and M. Men (2010), “The Paradox of China’s International Stock Market Co-movement:
Evidence from Volatility Spillover Effects between China and G5 Stock Markets,” Journal of

Chinese Economic and Foreign Trade Studies, 3(3), pp.235-253.
Nishimura, Y., Y. Tsutsui and K. Hirayama, (2012), “Return and Volatility Spillovers between
Japanese and Chinese Stock Markets: An Analysis of Overlapping Trading Hours with
High-frequency Data,” Discussion Papers in Economics and Business, No.12-01, Osaka
University.
Rozeff, M. S. and W. R. Kinney, Jr. (1976), “Capital Market Seasonality: The Case of Stock Returns,”

Journal of Financial Economics, 3(4), pp.379-402.
Theodossiou, P. and U. Lee (1993), “Mean and Volatility Spillovers across Major National Stock
Markets: Further Empirical Evidence,” Journal of Financial Research, 16(4), pp.337-350.
Tsutsui, Y. and K. Hirayama (2005), “Estimation of the Common and Country-Specific Shock to
Stock Prices,” Journal of the Japanese and International Economies, 19(7), pp.322-337.
Tsutsui, Y. and K. Hirayama (2013), “Are Chinese Stock Investors Watching Tokyo? International
Linkage of Stock Prices Using Intraday High-Frequency Data,” Japanese Journal of Monetaryand Financial Economics, 1(1), pp. 37-57.
Tustsui, Y., K. Hirayama, T. Tanaka, and N. Uesugi (2007), “Special Quotes Invoke Autocorrelation in
Japanese Stock Prices,” Asian Economic Journal, 21(4), pp.369-389.
Xekalaki, E. and S. Degiannakis (2010), ARCH Models for Financial Applications, John Wiley &Sons, New York.
Yamamoto, T. (1988) Keizaino Jikeiretu

Bunseki (Time Series Analysis in Economics), Tokyo :Sobunsha Publishing Company. (in Japanese)
Zellner, A. and F. Palm (1974), “Time Series Analysis and Simultaneous Equation Econometric
Models,” Journal of Econometrics, 2(1), pp.17-54.

27

Figures and Tables
Table 1. Basic Statistics of 5-Minute Returns of the Two Markets
Sample period: Nov. 4, 2003, to Nov. 18, 2011 (T=33444)
Mean

Std.Dev

Kurtosis

Skewness

LB10

LB102

CRX

-0.00020.210187.37860.275098.05217156

MI0.0002

0.117619.5414-0.3379326.5112406

SHCOMP

0.00120.2121

8.22240.046336.8809340.9Notes: Let T denote the number of observations and ˆ the standard deviation. Then the standard error of the mean,
kurtosis, and skewness is ˆ / T , 6 /T , and 24 /T respectively. The standard deviations of the mean of CRX,
MI, and SHCOMP are 0.0011, 0.0006, and 0.0012 respectively. The standard errors of the kurtosis and skewness are
0.0134 and 0.0268 respectively. LB10 and LB102 are the Ljung-Box statistics to check for absence of serial correlation
up to 10 lags in the standardized residuals and their squares.

28

Table 2. Estimation of AR-FIGARCH Model of SHCOMP
Sample period: Nov. 4, 2003, to Nov. 18, 2011 (T=33444)

Parameter

Coefficient

Std.Errorc0.0009(0.0009)1

0.1001***(0.0070)2-0.0551***(0.0062)3-0.0109(0.0061)40.0265***(0.0060)

0.0298***(0.0066)d0.3950***(0.0183)10.3023***(0.0207)1

0.6116***(0.0220)L.L.

10444.31

AIC-0.6240

LB10

11.399LB102

7.9460

LM10ARCH

0.7875

Notes: *** and ** denote significance at the 1% and 5% level respectively. Numbers in parentheses are robust standard
errors based on the quasi-maximum likelihood approach. L.L. is log likelihood. LB10 and LB102 are the Ljung-Box
statistics with lag 10 for standardized residuals and their squares, respectively. LM 10ARCH is the ARCH LM test statistic
for the null hypothesis that there is no ARCH effect up to order ten.

29

Table 3. Return and Volatility Spillover Effects from SHCOMP to CRX and MI
Sample period: Nov. 4, 2003, to Nov. 18, 2011 (T=33444)
CRX

MI

1

0.0315***(0.0041)0.0089***(0.0024)

2---0.0007(0.0026)

3--0.0065**(0.0026)

1-0.0021(0.0110)0.0251(0.0197)2--0.0067(0.0192)3---0.0185(0.0112)

0.1045***(0.0216)0.0079***(0.0021)d0.4783***(0.0207)0.3548***(0.0200)

1

0.2516***(0.0355)

0.3075***(0.0334)

1

0.5852***(0.0436)0.5901***(0.0389)

L.L.

17606.26

31253.67

AIC-1.0523-1.8667LB10

7.04072.7738

LB102

10.19017.643

LM 10ARCH

1.0266

1.7842

Notes: *** and ** denote significance at the 1% and 5% level respectively. Numbers in parentheses are robust standard
errors based on the quasi-maximum likelihood approach. L.L. is log likelihood. LB10 and LB102 are the Ljung-Box
statistics with lag 10 for standardized residuals and their squares, respectively. LM 10ARCH is the ARCH LM test statistic
for the null hypothesis that there is no ARCH effect up to order ten. The results on mean equations are omitted to save
space.

30

Table 4. Investigation of the Mechanism of Return Spillover Effects from China to Japan
Sample period: Nov. 4, 2003, to Nov. 18, 2011 (T=33444)
Base Model

Model A

Model B

1

0.0128***(0.0030)0.0017(0.0021)0.0017(0.0021)20.0065**(0.0030)-0.0001(0.0021)-0.0001(0.0021)

30.0029(0.0030)

0.0044**(0.0021)0.0045**(0.0021)0--0.4012***(0.0022)0.4011***(0.0022)1--0.0704***(0.0031)0.0697***(0.0031)

2

-

-

-

-

-0.0044(0.0031)

3

-

-

-

-

-0.0008(0.0031)R2

0.01180.51370.5137

Notes: *** and ** denote significance at the 1% and 5% level respectively. Numbers in parentheses are standard errors.
R 2 is adjusted R-squared. The estimation results on the AR terms ( i , i  1,,30 ) are omitted to save space.
Base Model:rTOPIX , t (i )  cTOPIX  1rTOPIX ,t (i 1)    30rTOPIX ,t (i  30)  1rCHN ,t ( i 1)    3rCHN ,t ( i  3)   TOPIX , t (i )Model A:rTOPIX , t (i )  cTOPIX  1rTOPIX ,t ( i 1)    30 rTOPIX ,t ( i  30)  1rCHN ,t (i 1)    3rCHN ,t ( i  3)  0 rCRX , t (i )  1rCRX ,t (i 1)   TOPIX ,t (i )Model B:rTOPIX ,t ( i )  cTOPIX  1rTOPIX , t ( i 1)    30 rTOPIX ,t ( i  30)  1rCHN , t (i 1)    3rCHN , t ( i  3)  0 rCRX , t ( i )    3rCRX ,t ( i  3)   TOPIX ,t ( i )Estimation method is ordinary least squares.

31

Table 5

Return and Volatility Spillover Effects from SHCOMP to CRX in Different Periods

2004

2005

2006

2007

2008

2009

2010

2011

Model

AR(4)
FIGARCH(1,d,1)
SHCOMP(1)AR(3)
FIGARCH(1,d,1)
SHCOMP(1)AR(4)
FIGARCH(2,d,1)
SHCOMP(1)AR(3)
FIGARCH(1,d,1)
SHCOMP(3)AR(4)
FIGARCH(1,d,1)
SHCOMP(2)AR(5)
FIGARCH(1,d,1)
SHCOMP(1)AR(5)
FIGARCH(2,d,1)
SHCOMP(3)AR(5)
FIGARCH(1,d,1)
SHCOMP(1)1-0.0004(0.0137)-0.0028(0.0089)0.0272(0.0154)

2

-

-

-

3

-

-

-

1

0.0584(0.0692)-0.0154***(0.0043)

0.0934(0.0764)

2

-

-

-

3

-

***

***

1

0.0187(0.0049)
0.2733***(0.0536)0.2007(0.1835)
0.3925(0.2111)0.0124(0.0020)0.2159***(0.0425)0.5046**(0.1978)
0.5817***(0.1983)

2

-

-

L.L.

2457.04

3498.60

AIC-1.1765

LB10***

0.0361***(0.0108)0.0169(0.0102)0.0422***(0.0114)----0.0504(0.0530)
0.0593(0.0653)

0.0279(0.0602)

-

-

-

**

0.0637***(0.0111)0.0117(0.0125)
0.0123(0.0116)-0.0811(0.0704)
0.3046**(0.1261)-0.1794**(0.0700)
0.0262*(0.0155)
0.4050***(0.0544)0.0000(0.5027)0.2882(0.5582)
0.1058(0.1971)

0.0661***(0.0164)-0.0246(0.0483)-

0.2333(0.2245)0.4961***(0.0979)
0.1681*(0.0881)
0.4865***(0.1243)0.0347(0.0169)
0.3789***(0.0528)0.2821***(0.0667)
0.5742***(0.0805)---

2171.292550.93-40.030

1280.183

3105.922648.68-1.6708-1.0253-1.2150

0.0255-0.6180-1.4987-1.45076.25454.50974.5486

7.63587.82973.64273.84927.7720

LB102

4.14448.3292

11.216

3.7842

1.47296.53541.75648.7777

LM 10ARCH

0.40030.85011.20080.38620.15240.64040.17440.8792

N. Obs.

4158

4176

4212

4176

4158

4104

4122

3635


d

1

0.0230(0.0084)
0.3570***(0.0560)
0.1232(0.1183)0.3548***(0.1219)0.0740**(0.0342)0.0287***(0.0077)-0.0112(0.0073)
0.0069(0.0067)0.0010(0.0301)
0.0278(0.0348)-0.0380**(0.0186)
0.0244***(0.0085)
0.2732***(0.0480)0.0000(1.2807)0.0907(1.1930)0.0406(0.0222)0.7932***(0.1138)0.1037**(0.0507)0.8268***(0.0756)

Notes: . *** and ** denote significance at the 1% and 5% level respectively. Numbers in parentheses are robust standard errors based on the quasi-maximum likelihood approach.
L.L. is log likelihood. LB10 and LB102 are the Ljung-Box statistics with lag 10 for standardized residuals and their squares, respectively. LM 10ARCH is the ARCH LM test statistic
for the null hypothesis that there is no ARCH effect up to order ten. The results on mean equations are omitted to save space.

32

Table 6

Return and Volatility Spillover Effects from SHCOMP to MI in Different Periods

2004

2005

2006

2007

2008

2009

2010

2011

AR(1)
FIGARCH(1,d,1)
SHCOMP(3)AR(1)
FIGARCH(1,d,1)
SHCOMP(1)AR(4)
FIGARCH(1,d,1)
SHCOMP(1)AR(2)
FIGARCH(1,d,1)
SHCOMP(1)AR(1)
FIGARCH(1,d,1)
SHCOMP(2)AR(1)
FIGARCH(1,d,1)
SHCOMP(1)AR(5)
FIGARCH(1,d,1)
SHCOMP(1)-0.0078(0.0053)
-

-

-

-

-

-

-

-

-0.0601(0.0001)0.0504***(0.0006)

0.0250(0.0217)-0.0173(0.0370)--10.0009(0.0084)0.0155*(0.0091)0.0011(0.0089)0.0488(0.1178)-0.0705(0.0744)0.0679(0.0473)
0.0063***(0.0014)0.2197***(0.0443)0.0000(0.5011)0.1559(0.5251)

0.0210**(0.0089)

AR(3)FIGARCH(1,d,1)
SHCOMP(3)

L.L.

Model

0.0170**(0.0071)

0.0230***(0.0014)0.0071(0.0046)

0.0292***(0.0070)0.0389***(0.0120)-0.0033(0.0043)0.0298(0.0263)

-

-

-

-

41.5161***(9.9671)
0.2911***(0.0477)
0.4792***(0.1230)
0.6786***(0.1112)81.2897***(21.4430)
0.3228***(0.0484)
0.2052***(0.0722)0.4892***(0.0991)0.0040(0.0047)-0.0096(0.0052)
0.0093(0.0048)0.0439(0.0386)-0.0193(0.0389)-0.0114(0.0154)90.8033***(22.2850)
0.3346***(0.0437)0.2037**(0.0860)
0.4910***(0.0994)4579.505757.28

3727.253852.28

1598.593050.104538.783659.09

AIC-2.1970-2.7535-1.7646-1.8383-0.7646-1.4825-2.1983-2.0067

LB10

11.0163.865817.4047.48002.55208.7993

7.16573.8658LB102

3.94374.46876.53915.1812

9.12497.823225.078***4.4687***

0.4665

1
2
3
1
2
3


d

1

0.0227(0.0237)
-

***

-

-

-

-

0.0375***(0.0123)
0.3694***(0.0539)0.1714(0.0915)
0.4651***(0.1124)0.0191***(0.0038)
0.4693***(0.0154)0.3743***(0.0076)0.7809***(0.0091)

0.0076***(0.0037)
0.3556***(0.0924)
0.3416***(0.0616)
0.6360***(0.0636)0.0057***(0.0017)0.3571***(0.0794)
0.3997***(0.0552)
0.6661***(0.0792)

ARCH
100.39520.28720.66030.51660.91570.69842.5176

N. Obs.

4158

4176

4212

4176

4158

4104

4122

LM

3635

Notes: . *** and ** denote significance at the 1% and 5% level respectively. Numbers in parentheses are robust standard errors based on the quasi-maximum likelihood approach.
L.L. is log likelihood. LB10 and LB102 are the Ljung-Box statistics with lag 10 for standardized residuals and their squares, respectively. LM 10ARCH is the ARCH LM test statistic
for the null hypothesis that there is no ARCH effect up to order ten. The results on mean equations are omitted to save space.

33

(CST) 9:30

11:30

13:00

15:00

China

China

(GMT)
0:00

(JST) 9:00

1:00

2:00

3:00

4:00

Japan
11:00

12:30

5:00

Japan

6:00

7:00

15:00

Figure 1. Trading Hours of the Japanese and Chinese Stock Exchanges.
Notes: The timeline in the middle represents Greenwich Mean Time (GMT). The opening and closing hours of themorning and afternoon sessions of Japan are in Japan Standard Time (JST) and those of China are in China Standard
Time (CST). Overlapping trading hours (windows of simultaneous trading) are shaded.

34

MI(-1)- 0.07
← e1

MI

0.7

0.003

rsh(-1)

0.7

0.04

← e2

CRX

0.999
0.01

CRX(-1)Figure 2.

Path Diagram

Note: Values in the figure are standardized coefficients. e1 and e2 are random terms.

35

.07

CRX

MI

Difference

2007

2008

.06
.05
.04
.03
.02
.01
.00
-.01
2004

2005

2006

2009

2010

Figure 3. Responses of Japanese Stock Price against
Chinese Stock Price Movements Estimated Annually2011

Notes: The graph plots the parameter  of equation 1, which measures the response of Japanese stock returns against
lagged Chinese stock returns. The solid line is for CRX (  CRX ), the dotted line is for TOPIX (  MI ), and the broken line
is for the difference between the two (  CRX   MI ).

36