GCOE Discussion Paper Series
Global COE Program

Human Behavior and Socioeconomic Dynamics
Discussion Paper No.94

Are Chinese Stock Investors Watching Tokyo?
An Analysis of Intraday High-Frequency Data fromTwo Chinese Stock Markets and the Tokyo Stock Market

Kenjiro Hirayama and Yoshiro Tsutsui

October 2009GCOE Secretariat
Graduate School of Economics

OSAKA UNIVERSITY

1-7 Machikaneyama, Toyonaka, Osaka, 560-0043, Japan

Are Chinese Stock Investors Watching Tokyo?
An Analysis of Intraday High-Frequency Data fromTwo Chinese Stock Markets and the Tokyo Stock
MarketKenjiro Hirayama(Kwansei Gakuin University)
Yoshiro Tsutsui(Osaka University)
October 2009

ABSTRACT
Intraday minute-by-minute data from the Tokyo, Shanghai, and Shenzhen stock
exchanges from January 7, 2008, to January 23, 2009, are analyzed to investigate theinteraction between the Japanese and Chinese stock markets. We focus on two windowsof time during which all three stock exchanges trade shares simultaneously, and specify
appropriate lags in vector autoregression (VAR) estimations. Granger causality tests,
variance decompositions, and impulse response functions show that, while Tokyo is
impacted by Chinese stock price movements, China is relatively isolated. This implies
that investors in Japan are more internationally oriented and alert to foreign markets
than those in China.
JEL Classification Numbers: G14, G15, F36
Keywords: international linkage of stock prices, high frequency data, inefficiency, overreaction,
China

Correspondence:

Kenjiro Hirayama
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

Yoshiro Tsutsui
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

1.

Introduction

The growth of Chinese stock markets has been phenomenal. Less than 20 years since
their establishment (in 1990 and 1991, respectively), the combined market
capitalization of the Shanghai and Shenzhen stock exchanges at the end of 2007 wasroughly 4.4 trillion dollars, which trails Tokyo’s 4.8 trillion dollars by a narrowmargin, and Tokyo is second only to New York. China’s stock markets, as well as her
economy, now exert significant influence on other countries. When the Shanghai
Composite Index dropped by 8.84% on February 27, 2007, it precipitated drops instock prices in all the major stock markets of the world. However, this huge drop
may have been a sign of weakness or inefficiency in the Chinese stock markets. Thisinefficiency, if it exists, should affect not only Chinese investors, but also investors inother countries because of international linkage of stock prices as well asinternational portfolio holdings. To the best of our knowledge, the efficiency andperformance of Chinese stock markets has not yet been examined in detail. In thispaper, we attempt to shed light on this issue.
There is much literature on the international linkage of stock prices (e.g., Jeonand von Furstenberg (1990), Hirayama and Tsutsui (1998), Masih and Masih (1999),
Heimonen (2002), Bessler and Yang (2003), Worthington, Katsura, and Higgs (2003),
and Darrat and Zhong (2005)). Almost all studies report on a bidirectional causality
between pairs of stock prices. However, while Chinese stock prices are little affectedby other markets, some major stock markets do respond to changes in Chinese stock
prices (Huang, Yang, and Hu (2000), Groenewold, Tang, and Wu (2004), and Zhang
2008)).1 This finding suggests that Chinese stock markets are isolated from others
and that their efficiency may still be in a fledgling stage. We find the lack of response1

Chen and Liu (2008) analyze volatility spillover effects between China and other markets. They
found causality from China to other markets, but not vice versa.
1

of Chinese stock prices to other markets interesting and worth exploring.
The primary purpose of this paper is to clarify whether Chinese stock markets
are really isolated from markets in other countries. If such isolation exists, the nextquestion is whether and when it will disappear. In this sense, a recent event comes tomind: the global stock market meltdown of September/October 2008. Although it
started in the U.S.A., other stock markets responded, magnifying the negative effects.
Chinese stock prices also plunged during this tumultuous period. Considering thewide-spread international spillover effects after Black Monday of 1987 (Tsutsui andHirayama (2009), pp. 170-171), it may be that Chinese stock markets underwent‘globalization’ after the meltdown of 2008 and that their stock prices will henceforth
be routinely and significantly swayed by global stock market movements.
Investigation of this possibility is one of the purposes of this study.
The second purpose of this paper is to establish a methodology forinvestigating international stock price spillover effects using tick or high-frequency
data. Most studies on this subject utilize daily, weekly, or monthly closing prices.
However, vector autoregression (VAR) estimation using such data is an indirect
method of measuring inter-market responses. For example, when studying New Yorkand Tokyo, it should be noted that the two stock exchanges are not open
simultaneously. When the New York stock market closes, Tokyo cannot respondbecause it is not yet open. It is only at market opening at 09:00 Japan Standard Time
(JST) that Tokyo stock prices can respond to New York. As daily data typicallyconsist of closing prices, all other sorts of information that drive stock prices during
the day contaminate the effect of New York on the Tokyo market. By using
high-frequency data, we can investigate the effect of the New York market at the
opening of the Tokyo market, unequivocally capturing the linkage.
2

While using daily data produces a murky picture, when the stock markets
under investigation are open for trading at the same time, analysis of bidirectional
simultaneous causality becomes feasible and can yield interesting results. We canthus conduct direct tests of spillover effects with real-time, simultaneous data fromthe Tokyo, Shanghai, and Shenzhen markets and propose an appropriate method forsuch an analysis.
There is a line of research that utilizes intraday data from simultaneously
open stock exchanges. Most such studies, however, focus on stocks that arecross-listed on two stock exchanges.2 To the best of our knowledge, there are only
two papers that analyze intraday stock price indexes for several stock exchanges
observed simultaneously. One is Èerny and Koblas (2008) who utilized
high-frequency data on stock price indexes from New York and six European cities.
Using data of different frequencies, the authors found that mutual information istransmitted very rapidly, so that an empirical analysis of stock price linkages using
daily data may not accurately capture the true reactions. The other is Égert andKočenda (2007) who studied stock price spillover effects among six European cities
using five-minute data. Although they found no cointegration, they reported evidenceof short-term Granger causality. However, as will be explained in Section 2.3, neitherof these papers seems to adopt appropriate lags in VAR estimation.
Tokyo, Shanghai, and Shenzhen are ideal for studying stock price spillover
effects because the markets are open simultaneously for two periods each day. Thereis a 30-min window (10:30-11:00 JST) in the morning and a 60-min window2

For example, Grammig, Melvin, and Schlag (2005) analyze three German stocks cross-listed in
New York and Frankfurt. Hupperets and Menkveld (2002) examine seven blue-chip Dutch companies
that are also listed in New York. Lok and Kalev (2006) investigate stocks of Australian and New
Zealand companies that are cross-listed in the Australian and New Zealand markets. These papers
focus on individual stocks to determine where price discovery occurs along the line developed by
Hasbrouck (1995).
3

(14:00-15:00 JST) in the afternoon when the Tokyo and Chinese stock exchanges areopen at the same time. In this paper we analyze the markets in Japan and China,
focusing on the time windows when both exchanges are open. We utilize
minute-by-minute stock returns in Granger causality tests, variance decompositions,and impulse response functions (IRF) to explore mutual interactions between the twocountries. Special attention is paid to lagged values in VAR estimation (Section 2.3),
one of the features of this paper.
The rest of this paper is organized as follows. Section 2 explains the data andmethodology. Section 3 presents estimation results. Section 4 examines whether
Chinese markets became more efficient after the global financial crisis of 2008.
Section 5 checks whether our lag methodology produces more plausible results anddiscusses possible causes of our results. The final section concludes the paper.

2. Data and Methodology
2.1. Methodology: Cointegration Tests and VAR Model Estimation
After preliminarily testing for unit roots that confirm commonly foundnonstationarity in the indexes of the three stock exchanges, we check forcointegration between each pair of stock prices. We focus on two bivariate systems:
the Tokyo-Shanghai and Tokyo-Shenzhen bivariate models, because we areinterested in the bidirectional dependence between Japanese and Chinese markets.
Shanghai-Shenzhen is analyzed merely as a reference. Since we employ bivariate
systems, the number of cointegrating ranks is one, at most, and there is no need forsystem estimation by Johansen tests. We employ Engle-Granger two-step estimation
to check for cointegration. 3 If cointegration exists, the VAR model should be

3

A practical reason for adopting this test is that econometric packages cannot handle our particular4

modified to a vector error correction (VEC) model. As shown later, our results
indicate no evidence of cointegration between pairs. Consequently, we can make ourestimations by conventional VAR models only, without VEC terms.
While long-run relationships are examined by cointegration tests, short-run
dynamic

interactions

areanalyzedbyGrangercausality

tests,

variance

decompositions computations, and IRF, which are standard tools for short-run
dynamic analysis.

2.2. Data
We analyze return spillover effects between the Japanese and Chinese
markets by focusing on pair-wise relations between Tokyo and Shanghai (orShenzhen), using the Nikkei 225 Index and Shanghai (or Shenzhen) Composite
Index. Minute-by-minute observations of these three indexes were obtained fromTickdata.com for the period from January 7, 2008, to January 23, 2009. Similar to
the Dow Jones 30 Industrials Index, Nikkei 225 is an arithmetic average of 225
representative stocks traded on the Tokyo Stock Exchange. Although the Shanghai
and Shenzhen Stock Exchanges trade A shares for domestic investors (traded in local
currency) and B shares for foreign investors (traded in US dollars in Shanghai andHK dollars in Shenzhen), only composite indexes of all shares were available to us.
Daily closing prices for our sample period are plotted in Figure 1, showing
downward trends in both Chinese markets and reflecting adjustments following the
bursting of a stock market bubble in China that started in November 2007. The
Nikkei average hovered between 12,000 and 14,000 until September/October 2008
after which it plunged to around 8000.

method of selecting lags.

5

Trading hours for the three stock exchanges are given in Figure 2. In Tokyo,
opening prices are determined by batch trading (itayose). After opening at 09:00 JST,a continuous auction takes place until 11:00. After a 90-min lunch break, theafternoon session starts at 12:30 and ends at 15:00. In both Chinese markets,
pre-market call auctions between 09:15 and 09:25 (Chinese Standard Time, CST)
determine the opening prices for the day. Continuous auction takes place between
09:30 and 11:30 for the morning session and between 13:00 and 15:00 (14:57 in thecase of Shenzhen) for the afternoon session. Since there is a one-hour time difference
between Japan and China, the only times during which all three stock exchanges are
simultaneously open for trading are 10:30 to 11:00 and 14:00 to 15:00 (JST). We
focus on these two windows of 30 minutes and 60 minutes in this study.

2.3. Data Selection
In defining the dataset we use the VAR model, which utilizes many lags. In
summary, the rules we use are characterized by the following four points: (a)minute-by-minute stock prices when the stock exchanges are simultaneously open,i.e., 10:31 to 11:00 and 14:01 to 15:00; (b) to avoid biases due to asymmetrictreatment of opening prices, we drop the first ten observations in each window. Thisdeletes the opening prices on the Chinese markets, resulting in slightly different
windows, 10:41 to 11:00 and 14:11 to 15:00, each day. Otherwise, our calculations
would include opening prices in China but not in Japan. Since stock prices at andimmediately following the openings of stock markets reflect information
accumulated during night-time non-trading hours and are more volatile than prices
during normal trading hours, it is necessary to exclude opening prices of bothmarkets to make a fair comparison; (c) time series data is constructed by sequentially6

combining data from the windows; and (d) the basic dataset constructed under rules(a) through (c), above, includes appropriate regression lags. To explain stock returns
observed at 10:41 in Tokyo, there are lagged returns on the right-hand side of the
VAR equation. Since the dataset is a combined series of data from two windows eachday, a one-period lag for the Tokyo index at 10:41 is that of 15:00 of the previous
day; a two-period lag is that of 14:59; etc. However, true lags are the index at 10:40,
10:39, etc. Only these truly relevant lags should be used as explanatory variables for
VAR equations. The same applies to the Chinese regression equations. Due todeletion of the first ten observations from each window, true lagged values areavailable from these deleted data points.
Levels and rates of change for the three indexes are provided in Table 1. The
data are obtained for 20 min in the morning and 50 min in the afternoon. For
minute-by-minute rates of change, Tokyo’s mean is smaller than those of Shanghai
and Shenzhen, but the standard deviations are about the same in all three markets.
However, the maximum and minimum in Tokyo are two to three times larger (inabsolute value) than those of China. The kurtosis of Tokyo returns is over 22.5
whereas it is only 1.5 in Shanghai and 2.2 in Shenzhen, indicating much fatter tails inthe distribution of returns in Tokyo. As with other stock price returns, theJarque-Bera test of normality strongly rejects the null of normality, but the extent of
this rejection is extreme in Tokyo, apparently due to its large kurtosis.

3. Results
3.1. Unit Root Tests
The logged levels and rates of change of the three stock price indexes are tested
for nonstationarity by the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP)7

tests. Results are shown in Table 2 . A constant term is included in the regression, but alinear time trend is not. The result is typical of stock prices, namely, levels can be
regarded as I(1) but rates of change are stationary, i.e., I(0).

3.2. Engle-Granger Tests of Cointegration
Since all three stock price levels are I(1), we next test for cointegration oflogged stock prices in each bivariate system. Results are displayed in Table 3 .
According to critical values as given by MacKinnon (1991), the null of nonstationarity
in the residuals cannot be rejected, indicating absence of cointegration for the twobivariate systems: Tokyo-Shanghai and Tokyo-Shenzhen. Thus, we can safely proceed
to estimating a conventional VAR model without error correction terms.
For reference purposes, we also compute the Engle-Granger tests for the
Shanghai-Shenzhen system. When Shanghai is the dependent variable, the null of aunit root in the regression residual cannot be rejected at a 5% confidence level.
However, when Shenzhen is the dependent variable, the null is rejected at a 10%
level. There is mild evidence of cointegration between Shanghai and Shenzhen,
which is plausible given their highly correlated movements (see Figure 1).

3.3. VAR Estimation and Granger Causality Tests
Since variables in VAR models must be stationary, we use minute-by-minuterates of change in the three stock indexes. The two bivariate VAR systems(Tokyo-Shanghai and Tokyo-Shenzhen) are estimated by ordinary least squares(OLS) with White’s heteroscedasticity-consistent variance-covariance matrix. To
determine the optimal lag order of the VAR model, we examine Ljung-Box Q
statistics for the regression residuals. They indicate that ten lags are sufficient to8eliminate serial correlation in the residuals. We thus adopt this lag order for all thefollowing VAR models.4
After estimating VAR(10) for the two bivariate systems, we conduct Granger
causality tests for each set of lagged variables. The null hypothesis is that all ten
coefficients on lagged values are zero, whose test statistic is distributed as
Chi-squared. Results are presented in Table 4 . Own lags are all highly significant.
We, however, are interested in cross terms. Shanghai Granger causes Tokyo verysignificantly, but Tokyo Granger causes Shanghai with only 3.85% significance.
Shenzhen also Granger causes Tokyo very significantly, but Tokyo’s effect on
Shenzhen is not significant. Its p-value is 41%, indicating an absence of Granger
causality from Tokyo to Shenzhen. We also compute the Granger causality tests for
the Shanghai-Shenzhen system. Cross causality is extremely significant between thetwo exchanges, indicating a very substantial mutual influence between Shanghai and
Shenzhen.
While Tokyo responds to Shanghai and Shenzhen in the sense of Granger
causality, Shanghai’s response to Tokyo is somewhat weak and Shenzhen does not
respond to Tokyo at all. These results are consistent with those of the studies on daily
return spillovers between China and other countries (Huang, Yang, and Hu (2000),
Groenewold, Tang, and Wu (2004), and Zhang (2008)).

3.4. Autocorrelation Functions
Another feature that characterizes Chinese markets is the extremely large test

4 The lag order was altered from 1 to 20 and both Akaike Information Criterion (AIC) and Schwarz
Bayesian Information Criterion (SBIC) were computed. The regressions to explain Tokyo exhibit
minimal AIC and SBIC at nine lags. But, the regressions to explain Shanghai or Shenzhen do notexhibit local minima between 1 and 20 lags. Stock prices in these exchanges seem to have a strong
and persistent serial correlation, which might imply lack of efficiency in information processing. In
any case, taking 20 lags is a bit excessive given that the morning window is only 30 min long.
9

statistic for its own lags. Table 4 shows that while Tokyo’s lags in the equation to
explain Tokyo produce a Chi-squared test statistic of 66, those of Shanghai and
Shenzhen are 15,845 and 32,905, respectively. This apparently results from verystrong serial correlation in Chinese stock returns, and may be a sign of relative
inefficiency in the Chinese stock markets. Although autocorrelation arises fromvarious reasons such as bid-ask bounces and non-synchronized trading, which may
not reflect efficiency, serial correlation reflects market inefficiency and is worth
examining. An easy way to check this is to compute autocorrelation functions (ACF).
Figure 3 shows that the magnitude of autocorrelation at one-minute lags is almost 10
times greater in Shanghai or Shenzhen than in Tokyo, suggesting informational
inefficiency in the Chinese markets.
The ACF of Tokyo exhibit spikes at five-minute intervals. However, as
Tsutsui et al. (2007) made clear, this merely reflects automatic updating of special
quotes, and is not evidence of high autocorrelation of actual prices. On the other
hand, ACF of Shanghai and Shenzhen are cyclical, having significantly andnumerically large peaks and troughs that may indicate overreaction in stock prices
and subsequent adjustments. According to the overreaction hypothesis proposed by
De Bondt and Thaler (1985), abnormal negative (positive) returns follow positive(negative) events. Although they analyzed the predicted profitability of long-term
winners and losers, the hypothesis also applies to the short-term reversal of stock
prices, e.g., Ketcher and Jordan (1994). Thus, not only the magnitude of the ACF, butalso their cycles may be evidence of informational inefficiency on the Chinese
markets.

3.5. Variance Decompositions
10

A VAR model can be converted into a vector moving average (VMA) and theforecast error variance decomposed into factors explained by each disturbance. In
Table 5 , we compute such decomposition at 30-min horizons. In the Tokyo-Shanghai
system, Shanghai accounts for only 0.361% of the forecast error variance of Tokyo’s
minute-by-minute returns, while the remaining portion (99.639%) is explained by
Tokyo’s own shocks (the latter figure not shown in the table because it is trivial). In
the decomposition of Shanghai, Tokyo explains an even smaller percent (0.231%) of
Shanghai’s variance and the rest is explained by Shanghai’s own shocks. In theTokyo-Shenzhen system, Shenzhen explains 0.314% of Tokyo’s variance, while
Tokyo explains only 0.192% of Shenzhen’s. Although Granger causality tests
indicate some causality from China to Japan, the proportion of forecast error varianceexplained by China is numerically quite small. And Tokyo’s influence on Chinese
markets is even smaller. Using the same dataset, we compute variancedecompositions for the Shanghai-Shenzhen bivariate system. Within China, theproportion explained by the other stock exchange is very large. 5 These results
suggest that the linkage between Chinese and Japanese markets is quite weak. The
same tendency can be found using daily data. Zhang (2008) reports that only 0.05%
of China’s 20-day ahead forecast error variance is accounted for by shocks to Japanand that 0.15% of Japan’s forecast error variance is explained by events in China.
Using a four-country VAR (U.S., U.K., Germany, and Japan) and daily data,
Hirayama and Tsutsui (1998) found that 6.5% of a 20-day ahead forecast error
variance of Japan is explained by the U.S. and that 2.0% of the U.S. variance isaccounted for by Japan. These magnitudes are much larger than the ones we find

While 13.5% of the forecast error variance of Shanghai is accounted for by Shenzhen, 69.2% ofShenzhen is explained by Shanghai. Thus, the influence of Shanghai over Shenzhen is substantiallygreater than the other way around.
11
5

between Japan and China.

3.6. Impulse Response Functions
Our next tool to analyze short-run interactions is IRF, which are actuallycoefficients of the VMA model. They capture how a shock to one variable arising at
a certain period affects endogenous variables in subsequent periods.
The IRF for the Tokyo-Shanghai system are plotted in Figure 4. Responses toown shocks are far greater in magnitude than responses to shocks on the other stock
market. The maximum on the ‘own’ charts is 20 times greater than on the ‘cross’charts, consistent with the results of variance decomposition. However, although the
responses of Tokyo to its own shocks dissipate very quickly (within some 10 min),Shanghai’s responses to its own shocks oscillate and are statistically quite significant
for about 30 min. This cyclical pattern may be a result of overreaction in one
direction and subsequent adjustments in the reverse direction, which also caused
strong serial correlation in Shanghai’s stock returns.
In the ‘cross’ charts, more of Tokyo’s responses are statistically significant
than those of Shanghai up to 10 lags, in agreement with results of the Granger
causality tests that indicate more significant response of Tokyo to Shanghai than of
Shanghai to Tokyo. Shanghai’s responses exhibit an oscillating pattern which may be
a sign of overreaction of the Shanghai stock market to Tokyo, although negative
responses at 8 and 9-min lags are not statistically significant.
Figure 5 shows that the IRF of the Tokyo-Shenzhen system are generally
similar to those of the Tokyo-Shanghai system. The cross IRF are qualitatively
similar to those of Tokyo-Shanghai. Tokyo’s responses to Shenzhen are slightly moresignificant than the reverse. Tokyo’s IRF dissipate monotonically, but Shenzhen’s
12oscillate, although most are not statistically significant.

4. Were the Chinese Stock Markets Transformed by the Global Financial Crisisof 2008?
4.1. The Global Financial Crisis and International Linkage of Stock Prices
In the previous section we determined that Chinese stock markets are notmuch affected by Tokyo, i.e., international return spillover effects are unidirectionalfrom China to Japan. However, during our sample period, the New York stock
market experienced a precipitous plunge that had far-reaching effects on othermarkets. Just as international stock price comovements were reinforced after Black
Monday of 1987, the 2008 global financial crisis may have strengthened
international return spillover effects so that, during our sample period, China may
have undergone changes in its responsiveness to Tokyo. In this section we examine
whether China became more responsive to Tokyo after the stock market crash of
September/October 2008. To do so, we split the sample into two subperiods andrecompute some of our tests. The first subperiod is January 7, 2008, to August 29,
2008; the second is September 1, 2008, to January 23, 2009.

4.2. Granger Causality Tests: Causality from Tokyo to China Became Stronger
The Granger causality results are presented in Table 4 . Focusing on crosseffects, we immediately notice that Chinese markets seem to have paid more
attention to developments in Tokyo in the second, turbulent, post crisis period of
2008. 6 Namely, the p-value of the Chi-squared statistic testing the explanatory
6

While Shanghai and Shenzhen significantly Granger cause Tokyo in the first period, theirsignificance declined substantially and they are barely statistically significant at a 10% confidence
level in the second period. During the second period, variability was wider in Tokyo (see Figure 1),
which probably underweighted the influence from China.
13

power of Tokyo over Shanghai is 0.1987 in the first period, but 0.0656 in the second,
indicating increased significance of Tokyo (see shaded cells). The influence of Tokyo
over Shenzhen changed similarly. According to the causality results in the second
period, the relative independence of Chinese stock prices seems to have disappeared.

4.3. Variance Decompositions: Sensitivity of Chinese Markets Became Stronger
Next, we re-estimate the forecast error variance decompositions for the two
subperiods. Results are shown in Table 5 . In agreement with the above, theexplanatory power of Tokyo over Shanghai and Shenzhen increased three and fourtimes in the second period.7 Tokyo’s share is even greater than those of Shanghai or
Shenzhen in accounting for Tokyo’s error variance in the second period. Again, thisis evidence of increasing sensitivity of the Chinese markets to Japan.

4.4. Impulse Response Functions: Response of Chinese Markets Became Stronger
The IRF for the Tokyo-Shanghai system are estimated for the two subperiods
and shown in Figure 6. Tokyo’s responses to its own shocks are greater in the second
period than in the first, probably because of much higher market volatility. Likewise,
Tokyo’s responses to shocks in Shanghai are generally greater and more prolonged in
the second period. Even starker are Shanghai’s responses to Tokyo in the second
period. In the first period, the IRF oscillated greatly with positive and negative values
but, in the second period, the IRF tended to remain positive with a larger magnitude.
The IRF of the Tokyo-Shenzhen model has almost identical results as in Figure 7.
Consistent with the findings of the Granger causality and variance decompositions
tests, the IRF also indicates a more significant response of the Chinese markets to

7

The explanatory powers of Shanghai and Shenzhen over Tokyo weakened in the second period.
14

Tokyo in the second period.

4.5. Nonetheless, Chinese Markets Have Not Been Transformed
The question remains, did the Chinese stock markets undergo a permanentchange after the global financial crisis, or were they temporarily linked to Tokyo
during this turbulent period?
To check whether the change in sensitivity of Chinese markets is permanent or
temporary, we re-compute the above tests using data from November 3, 2008, onwards,
i.e., after the rapid fall ceased. Results appear in Tables IV and V. The Granger
causality tests reveal that Shanghai and Shenzhen are not significantly explained by
Tokyo in this period, suggesting that the increased influence of Tokyo was temporary
and limited to September and October 2008. However, variance decomposition for theperiod after November 3, 2008, indicates otherwise; the proportions of forecast error
variance explained by Tokyo remain rather high (Table 5). However, the variancedecomposition results were not tested for statistical significance. We tend to trust theGranger causality tests which test statistical significance and lead to the conclusionthat the Chinese markets remain isolated.
Since serial correlation at least partially reflects market efficiency, we compare
the magnitude and pattern of ACF before and after the financial crisis. Results areshown in Figure 8. The qualitative pattern remains the same in the second period, eventhough the amplitude is slightly smaller. The same is true for ACF after November 3,
2008. Thus, efficiency in the Chinese markets seems to be unchanged in the second
period.

5. Discussion15

After analyzing stock price spillover effects between Tokyo and two Chinese
stock markets (Shanghai and Shenzhen) using a novel VAR estimation with correct
lagged values, we check in this section whether our methodology produces moreplausible results than those obtained from conventional VAR estimation. Next, weinterpret the results, considering possible causes of the international interdependenceof the stock prices.

5.1. Evaluation of Our Methodology
We use tick data from three stock exchanges and methods (a) through (d) insection 2.3 to select data for analysis. First, let us see what happens if we do not follow(d), above. Using the same dataset for the dependent variable, we change the lagvariable from the true lag to one that shifts the dependent variable just one period, as
in normal VAR estimations. Using these incorrect lag values, we may underestimate
the true effects of the lags. Results of Granger causality tests carried out by
conventional VAR estimation are shown in Table 6 . The Chi-squared statistics are
generally smaller than in Table 4 . Thus, using incorrect lag variables generally
reduces statistical significance, as we conjectured.
Next, we disregard rule (b) of subsection 2.3 and include the first ten
observations following the opening of each trade session in China. We line up the data
for the 30-min and 60-min windows contiguously. The windows include the opening
prices of the Chinese markets, but not of the Tokyo market. Since opening prices
respond to information accumulated during non-trading hours, their responsiveness isstronger than during normal trading hours. Since the data for Tokyo do not include
opening prices, comparing the magnitude of responses is unfair. Results are given in
Table 6 . Compared to Table 4 , the significance of Tokyo on Shanghai and Shenzhen
16increases, confirming our prediction that the inclusion of opening prices distorts
causality results. The difference is starker in the case of Shenzhen. There, the p-valueof the causality of Tokyo on Shenzhen is 0.4088 in Table 4 but only 0.0341 in Table
6 , leading to the wrong conclusion that Shenzhen does respond to Tokyo. On the other
hand, the p-value of the causality of Tokyo on Shanghai declines by a small amount.8
In sum, both rules of data selection, (b) and (d), are necessary to obtain undistorted
results. In particular, symmetric treatment regarding exclusion of opening prices iscritical to reach the conclusion that responses of the Chinese and Japanese markets toeach other are asymmetric.

5.2. Possible Causes of Why Chinese Markets Do Not Respond to the Tokyo Market
A main result of our analysis is that while Chinese markets seem to affect theTokyo market, the effect of Japan’s market on China seems to be almost zero. In fact,according to the Granger causality test, there is no statistically significant effect ofTokyo on Shenzhen. What causes this result?
Our sample period includes the bursting of a stock price bubble in China thatcan be interpreted as causing an asymmetric response in China.9 It can be argued thatduring the burst, stock price volatility in China became so great that it affected Japan
more than the other way round. However, Table I reveals that this is not the case.
Indeed, the maximum and minimum Tokyo returns are roughly twice those of China,
though the standard deviations are similar in all three markets. Therefore, if we extract

8

Although fundamentally very significant, the Chi-squared test statistics for own lags in the two
Chinese markets become much smaller. With this dataset, lagged values for the opening prices areinevitably those from the previous day or window, which would normally have less explanatory power
than immediate lags.
9
Another factor accounting for the asymmetry between the countries is that China lists stocks of state
enterprises, especially A shares whose liquidity is especially low. We hypothesize that these shares
make the sensitivity of Chinese stock prices generally low.

17

Tokyo’s minute-by-minute returns that fall within China’s maximum/minimum range,
they should have less significant impact on China. This would reinforce our results in
Section 3 that Chinese markets are not influenced by Tokyo.
To verify this statement, we divide Tokyo returns into those that fall within the
maximum/minimum range of Chinese returns and those beyond this range. We thentest the Granger causality of the two categories, large and small changes in Tokyo. We
found: (a) both large and small changes in Tokyo impact Shanghai at 5% but not 1%
significance, and (b) Tokyo’s large changes have no impact on Shenzhen at 5%
significance and Tokyo’s small changes have no impact even at 20% significance.
These results are basically the same as in Table 4 . This is because there are very few
data points, only 47 (0.28%) of 16,920 observations, that belong to the ‘large’ categoryof changes and the ‘small’ changes are almost identical to the whole sample, making
their explanatory power almost equal to that of the whole.
Asymmetric causality between Tokyo and China can be attributed to different
investor behaviors. We claim that Japanese investors pay attention to Chinese stock
prices in determining their portfolios, but that Chinese investors do not study Japaneseprices. This implies that Chinese investors collect less information than their Japanesecounterparts and is consistent with the long and persistent serial correlations amply
shown by IRF and ACF (Figures 3, 4, and 5).

6. Conclusion
In this paper we analyze intraday minute-by-minute data from the Tokyo,
Shanghai, and Shenzhen stock exchanges to investigate mutual interaction between
Japanese and Chinese stock markets. Specifically, we focused on a 20-min window(10:41-11:00 JST) in the morning and a 50-min window (14:11-15:00 JST) in the18

afternoon during which all three stock exchanges are simultaneously trading shares.
Our basic tool of empirical analysis was estimation of two bivariate VAR models(Tokyo-Shanghai and Tokyo-Shenzhen). Our methodology, we believe, correctly
specifies the appropriate lagged variables in VAR equations. It also avoids using thevolatile values that characterize the Chinese market after opening, thus treating thetwo markets symmetrically.
We then conduct Granger causality tests, variance decompositions, andcomputed IRF. Empirical analyses with the entire sample (from January 7, 2008, toJanuary 23, 2009) confirmed findings from earlier studies using daily data that China
is relatively isolated from other countries. The Shenzhen market, which is muchsmaller than the Shanghai market, seems to be more autonomous than Shanghai.
However, Tokyo seems to be statistically significantly impacted by Chinese stock
price movements, implying that Japanese investors are more internationally oriented
and alert to foreign markets than Chinese investors.
Another feature of the Chinese stock markets is the fact that theirminute-by-minute stock returns exhibit strong and persistent serial correlation. The
IRF to their own shocks exhibit very significant and numerically large effects, withoscillating patterns of influence. This may reflect overreaction. Namely, stock prices
overreact to new information, which is later reversed, indicating relative
informational inefficiency.
Many studies show that after Black Monday, the world’s stock markets became
more cointegrated. Likewise, the global financial crisis of 2008 might have ushered in
the internationalization of Chinese stock investors. To check, we split our sample into
two subperiods at the end of August. We found that while China did not respond to
Tokyo in the first period, it did so in the second. However, it would be hasty to19

conclude that China became permanently attentive to Tokyo from September onwards.
Indeed, if we exclude the September and October data points from the second period,
the Granger causality tests qualitatively returned to the results of the first period. Thisimplies that Chinese markets were subject to influences from Japan only during
September/October 2008, and that Chinese markets are still basically isolated fromforeign countries.

20

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22

Table 1
Stock Price Indexes of Tokyo, Shanghai, and Shenzhen
The stock price indexes used for Tokyo, Shanghai, and Shenzhen are the Nikkei 225, Shanghai Composite,
and Shenzhen Composite Indexes, which are determined at a minute-by-minute frequency. The sample
period is January 7, 2008, to January 23, 2009. Intraday data are for the 20-min period from 10:41 to 11:00
and the 50-min period from 14:11 to 15:00 (Japan Standard Time). The number of observations is 16,920.
The rates of change are not log differences, but arithmetic rates of change from the previous minute. JB is
the Jarque-Bera test of normality. The p-value is its significance.
VariableMean

St. Dev.

Tokyo

9.363880.210679.59177

Shanghai

7.93030

0.32603

Shenzhen

6.70379

Minute-by-Minute Tokyo
Rate of Change

Logged Level

Maximum Minimum

Skewness

KurtosisJB

8.85910-0.84368-1.00670

2721.750.00008.61251

7.42791

0.31680-1.08355

1110.740.00000.35473

7.36228

6.117370.22869-1.22856

1211.58

0.00000.00074

0.08298

1.07348-1.54325-0.55788

22.52593 358607.17

0.0000Shanghai0.001110.088030.49049-0.47809

0.07260

1.533791673.370.0000Shenzhen

0.00105

0.090850.52035-0.66380-0.05071

2.217453473.800.000023

p-valueTable 2
Unit Root Tests for Nonstationarity of Three Stock Price Indexes
The logged levels and rates of change of the stock price indexes of the Tokyo, Shanghai, andShenzhen Stock Exchanges are tested for nonstationarity by the Augmented Dickey-Fuller(ADF) and Phillips-Perron tests. AR gives the order of lagged dependent variables in the ADF
regression. The order increases if a serial correlation remains in the residual series at a 10%
significance level. In this table, all the series need only one lagged dependent variable toeliminate serial correlation. The sample period and intraday data are defined in Table I.
Stock Price Index

Augmented Dickey-FullerτT ( ρ$ − 1 )Phillips-Perron
AR

Z ρ$

Zτ$

Logged Level
Nikkei 225-0.87-0.4159

1-0.89-0.4245

Shanghai Composite-2.46-1.77241-2.47-1.7702

Shenzhen Composite-2.1-1.55921-2.1-1.5573

Nikkei 225-12824.09-80.0594

1-16366.08-119.0206

Shanghai Composite-6364.98-56.4053

1-5626.75-56.9562

Shenzhen Composite-9351.2-68.3671-4819.31-51.2613

Rate of Change in Index (%)

24

Table 3
Engle-Granger Tests of Cointegration for Tokyo-Shanghai and Tokyo-Shenzhen Systems
Regressions include a constant and a linear trend. For each of the three bivariate systems(including Shanghai-Shenzhen for reference), the regressor and the dependent variable arereversed to check for consistency. The t-statistic tests for a unit root in the residuals from thebivariate regressions. Critical values for the t-statistic are: -4.33 (1%), -3.78 (5%), and -3.50(10%), based on MacKinnon (1991). For data description, see Table I.

Dependent Variable

Explanatory Variable

t-statistic

Tokyo

Shanghai-1.9645

ShanghaiTokyo-2.3776Tokyo

Shenzhen-1.9011

ShenzhenTokyo-1.4356

Shanghai

Shenzhen-3.9238

Shenzhen

Shanghai-3.419425

Table 4
Granger Causality Tests for Tokyo-Shanghai and Tokyo-Shenzhen Systems
Granger causality tests are conducted by testing the hypothesis that all ten
coefficients of the lagged variables are zero. The test statistic is distributed as
Chi-squared. The full period is from January 7, 2008, to January 23, 2009, andcontains 16,920 observations. The first (pre-financial crisis) period is from January 7
to August 29, 2008, with 10,584 observations. The second (post-financial crisis)
period is from September 1, 2008, to January 23, 2009, with 6336 observations. The
period after November 3, 2008 (after the rapid fall of prices ceased) has 3672
observations. The p-value of the Chi-squared statistic testing for the explanatory
power of Tokyo over Shanghai is 0.1987 for the first period, but 0.0656 in the second,
indicating an increased influence of Tokyo after the global financial crises of
September/October 2008 (shown in shaded cells).
From

To

Full Period

First Period

Second Period

Period after November 3,
2008

Chi Squarep-value

Chi Squarep-value

Chi Squarep-value

Chi Squarep-value

Tokyo

→

Tokyo

65.63

0.000052.16

0.000049.39

0.000022.42

0.0131

Shanghai

→

Tokyo

41.56

0.000067.63

0.000017.39

0.066118.96

0.0407

Tokyo→

Shanghai

19.15

0.038513.47

0.198717.42

0.06569.52

0.4837

Shanghai→

Shanghai

15,845.29

0.000013,198.37

0.00004106.340.00002714.200.0000

Tokyo

→

Tokyo

65.54

0.000052.54

0.000049.28

0.000022.81

0.0115

Shenzhen

→

Tokyo

44.59

0.000065.2

0.000016.07

0.097716.85

0.0778Tokyo→

Shenzhen

10.37

0.408812.32

0.264417.44

0.06517.35

0.6922

Shenzhen→Shenzhen

32,905.13

0.000021,469.37

0.00004407.50.00003126.590.0000Shanghai→

Shanghai1929.820.00001672.480.0000525.230.0000306.680.0000Shenzhen→

Shanghai

1629.860.00001073.460.0000605.880.0000542.980.0000Shanghai→

Shenzhen

3826.250.00002753.01

0.00001353.620.0000860.320.0000Shenzhen→Shenzhen

4168.650.00002720.920.00001460.070.00001094.270.000026

Table 5
Variance Decomposition for Tokyo-Shanghai and Tokyo-Shenzhen Systems
Forecast error variance decomposition is computed for each system. Since thesystems are bivariate, only cross results are displayed. The remainder is accounted
for by the other variable. The forecast horizon is 30 periods (minutes). When thecausal ordering is reversed, the result is almost unchanged, because the correlation
between the two residuals is small. For definitions of the periods, see Table 4.
Forecast Error

Accounted for by:

Full Period

First Period

Second Period

After
November 3,

Variance of:

2008
Tokyo

Shanghai

0.361%

0.780%

0.366%

0.513%

ShanghaiTokyo

0.231%

0.153%

0.507%

0.640%

Tokyo

Shenzhen

0.314%

0.758%

0.315%

0.482%

ShenzhenTokyo

0.192%

0.111%

0.486%

0.675%

Shanghai

Shenzhen

13.538%13.465%13.007%17.411%

Shenzhen

Shanghai

69.152%71.130%67.390%67.768%For reference only27

Table 6
Results of Granger Causality Tests, if our Data Construction Rules are DisregardedThis table indicates the results of the Granger causality tests if the dataset is formed
disregarding our rules of construction. Column (1) shows the results if rule (d) of subsection
2.3 is violated, i.e., lags on the right-hand side of a regression are based on the usual methodof referring to values from previous periods. Column (2) shows the results if, in addition torule (d), we disregard rule (b), i.e., opening prices of the Chinese markets are included in thedataset, while those of the Tokyo market are not.
From

To

Conventional VAR Estimation

Chinese Data Including(1)

Opening Prices(2)

Chi Squarep-value

Chi Squarep-value

Tokyo

→

Tokyo

62.59

0.000059.32

0.0000Shanghai

→

Tokyo

29.55

0.001024.12

0.0073

Tokyo→

Shanghai

24.93

0.005520.75

0.0229Shanghai→

Shanghai

11,978.13

0.0000266.030.0000

Tokyo

→

Tokyo

62.29

0.000059.06

0.0000Shenzhen

→

Tokyo

33.00

0.000324.04

0.0075

Tokyo→

Shenzhen

10.75

0.377519.53

0.0341

Shenzhen→Shenzhen

16,316.52

0.0000559.320.000028

160006000
Shanghai
ShenzhenTokyo (right scale)

5000

4000

14000
12000
10000
8000

3000

6000

2000

4000
1000

2000

0

0

Figure 1. Daily closing stock prices on Tokyo, Shanghai, and Shenzhen markets. Shownby the Nikkei 225, Shanghai Composite, and Shenzhen Composite Indexes, respectively.

29

10:30

9:00

10:00

Shanghai
Shenzhen

11:00

14:00

12:30

12:00

13:00

Tokyo
09:00

14:00

Tokyo
11:00

12:30

Shanghai 16:00
Shenzhen

15:00

16:00

15:00

Figure 2. Trading hours of the Tokyo, Shanghai, and Shenzhen Stock Exchanges.
Hours are according to time in Tokyo (Japan Standard Time, JST). Overlapping
hours (windows of simultaneous trading) are shaded.

30

Figure 3. Autocorrelation functions of Tokyo, Shanghai, and Shenzhen. Minute by minute returns and 95% confidence levels shown.
Horizontal axis measures time in minutes.

31

Figure 4. Impulse responses of Tokyo-Shanghai VAR model. 95% Confidence bands are based on 10,000 random draws.
Horizontal axis measures time in minutes.
32

Figure 5. Impulse responses of Tokyo-Shenzhen VAR model. 95% Confidence bands are based on 10,000 random draws.
Horizontal axis measures time in minutes.
33

Figure 6. Impulse response functions in Tokyo-Shanghai VAR model: Split sample. Since the graphs are cluttered with so many lines,
confidence bands are not depicted. Horizontal axis measures time in minutes.
34

Note that the four graphs have different vertical scales.

Figure 7. Impulse response functions in Tokyo-Shenzhen VAR model: Split sample. Since the graphs are cluttered with so many lines,
confidence bands are not depicted.

Horizontal axis measures time in minutes. Note that the four graphs have different vertical scales.

35Figure 8. Autocorrelation functions of three stock returns: split sample. First period is from January 7 to August 29, 2008; thesecond period is from September 1, 2008, to January 23, 2009. Horizontal axis measures time in minutes.
36