Proceedings of 3rd Global Business and Finance Research Conference
9 - 10 October 2014, Howard Civil Service International House, Taipei, Taiwan ISBN: 978-1-922069-61-0
Trading Volumes and Return Volatilities in the Stock Markets of
China and US
Mubing Zhou and Bianxia Sun*
This paper examines whether the trading volumes taking place in the US
stock markets contribute to the returns and return volatilities in Chinese
stock markets. Sample data ranges from Jan 2005 to Dec 2013, and to
consider the possible structural change caused by the 2008 financial crisis
we divide the data into two subsamples, pre-crisis and post-crisis
respectively. Empirical results show that the volume information of US
markets does not contribute to the returns of Chinese stock market, no
matter whichever sample is concerned. However, for the post-crisis sample,
the return volatilities of Chinese market are significantly influenced by the
volumes of US markets, contrary to the results on the pre-crisis sample.
These findings indicate that Chinese stock markets have been more closely
linked with the US markets since the Crisis and the trading information of
US markets can help to predict the volatilities of Chinese markets.
1. Introduction
There is an old saying in Wall Street that it takes volume to make price move. Actually, the
relationship between trading volume and price change in financial markets always
fascinates both academic researchers and financial practitioners, and much research work
has been done along this line. Karpoff (1987) reviewed many papers in this field and
pointed out that volumes were positively related to the magnitude of the price change.
Lamoureu and Lastrapes (1990) applied the GARCH model on 20 NYSE stocks and found
that volatilities were positively influenced by trading volumes. Besides these, Najandand
Yung (1991) and Sharma et al. (1996) adopted different volatility models targeting
different markets and also confirmed the positive relationship between trading volume and
return volatility.
The papers mentioned above focus on the contemporaneous relationship between trading
volume and price change. different from these studies, other research work focuses on the
lead-lag relationship between the two market variables. Blume et al. (1994) investigated
the informational role of volume and argued that volumes might be helpful in forecasting
the price movements in the short run. Chen et al. (2001) examined the Granger causality
relationship between volumes and returns using the data broadly from most of the large
stock exchanges in the world, and also proved that there was a positive association in
return volatility and lagged trading volume.
Besides research on the interactions between volume and price change in the same
market, there is another line of studies focusing on such interactions across different
markets. Lee and Rui (2002) found that the trading volume taking place in the U.S.
markets contained predictive power for the U.K. and Japanese financial markets. Gagnon
and Karolyi (2003) showed that the short-term dependence in returns and volatilities
between Japan and the US markets was sensitive to the interactions with volumes of
those markets. Using intraday high frequency data, Hussain (2011) found that intraday
*
Corresponding author : Department of Financial Mathematics and Financial Engineering, South University of Science
and Technology of China, Shenzhen, China; Phone: (86)755-88018601; Email: sun.bx@sustc.edu.cn
Proceedings of 3rd Global Business and Finance Research Conference
9 - 10 October 2014, Howard Civil Service International House, Taipei, Taiwan ISBN: 978-1-922069-61-0
trading volumes were influential on cross-border return and volatility processes between
German and British equity markets.
In this paper we are interested in the relationship between trading volume and return
volatility cross the stock markets of the US and China. More specifically, we want to check
the spillover effect of volume taking place in the US stock markets on the price change of
Chinese stock markets. This study is interesting in that China is the largest emerging
economy in the world with undeveloped and relatively closed financial markets, while US
is the largest developed economy accompanied with open and worldwide influential
financial markets. Considering the fact that China and US are located in different time
zones and there are no overlapping trading hours between their stock markets, our study
is naturally on the relationship between price changes of Chinese market and lagged
trading volume of US market.
China didn’t have its own stock market until 1990s, and since then the stock markets grew
gradually. After the reform of non-tradable shares around year 2005, Chinese stock
markets began to experience a rapid development. The Shanghai Stock Exchange
Composite Index (hereafter SHCOMP), widely recognized as the representative of
Chinese stock markets, increasing from around 1400 in the early year 2000 to more than
6000 in Oct 2007, and was then largely reversed by 2008 financial crisis. Chinese stock
markets are initially relatively closed and mainly limited to domestic investors, under the
background of restricted currency. Now there are several thousands of companies listed in
Shanghai or Shenzhen Stock Exchange, and Chinese currency is becoming more and
more important in the world. Especially after the 2008 financial crisis, with the inflow of
international capitals from developed economies to developing ones, Chinese stock
markets turn out to be more internationalized than before. We therefore in this paper
examine the volume-volatility relationship between China and US separately before and
after 2008 financial crisis.
This paper contributes to the line of research in two aspects. First, focusing on the top two
economies in the world, this paper makes a detailed study on the relationship between the
price change in one market and the trading volume in the other market. The result can
help people dig more about the economic relations between the two big and important
countries. Second, this paper regards the 2008 financial crisis as a crucial separating line
and studies the cross market interactions between volume and price change before and
after the Crisis respectively. Some studies have shown the significance of the Crisis in
changing and redefining the international financial markets, hence our study on two
subsamples with different time period can tell more about the changes caused by the
crisis and can also offer a good comparison with other research work in the field of
volume-volatility study.
The remainder of this paper is organized as follows. In the next section, the data to be
used are explained and the descriptive statistics are summarized. The empirical
methodologies are presented in Section 3 and empirical findings in Section 4. Finally,
Section 5 gives the conclusions of this paper.
Proceedings of 3rd Global Business and Finance Research Conference
9 - 10 October 2014, Howard Civil Service International House, Taipei, Taiwan ISBN: 978-1-922069-61-0
2. Data
2.1 Data description
The data set consists of the indices from both US stock market and Chinese stock market.
There are two main stock indices in China: SSE Composite Index and SZSE Component
Index. SSE Composite Index is much more comprehensive and representative than SZSE
Component Index. So we choose SSE Composite Index as our research object. Note that
SSE Composite Index is often abbreviated as Shanghai Composite Index (SHC). As for
US stock market, we use three major indices: S&P500 (SP), Nasdaq Composite Index
(Nasdaq or NSDQ) and Dow Jones Industrial Average (DJIA). These three indices reflect
different parts of US stock market: S&P500 includes a wide range of stocks from different
industries; Nasdaq mainly consists of stocks of high-tech companies, and it also includes
companies from non-US countries; DJIA is composed mainly of prestigious and powerful
companies. These indices cover the period from 4 January 2005 to 31 December 2013,
where 15 June 2008 acts like a dividing line because Lehman Brothers declared
bankruptcy this day, which pushed the 2008 financial crisis to its apex. All data is
downloaded from Wind Financial Terminal. We use log return as the rate of return.
It is generally accepted that series of trading volume usually has the characteristic of
heteroscedasticity. To lessen this phenomena, taking logarithm is a widely used technique
(in the rest of this paper, when we talk about trading volume, we are referring the volume
after taking logarithm). Moreover, due to the time difference between China and US, SHR t
(return of Shanghai Composite Index at day t) always leads USR t (return of a US stock
market at day t). So if we are to investigate the influence of US stock market on Chinese
stock market, we should study the relationship between SHR t and USR t -1 . In addition,
Chinese stock market may close on some traditional Chinese festival while US stock
market is still open, so to make our data aligned, we delete such data points.
In Figure.1, we plot the returns and trading volume of SHC and S&P from 4 Jan 2005 to
31 Dec 2013. Figure (a) and figure(c) shows the returns of these two markets. We find
that these two figures are somewhat similar to each other: both have the volatility
clustering property which suggests that a large change of return is more likely to be
followed by another large change of return and a small change of return is more likely to
be followed by another small change of return; there are many abnormal data points, or
spikes; both look to be stationary. For the trading volume, which is plotted in figure (b) and
(d), we find that they are, in some sense, stationary. Note that for LOG(SHC_VOL), we
observe a distinctive difference between periods before and after 2008: before 2008, the
trading volume series is less volatile and has a relatively small number, but after 2008,the
magnitude and volatility of trading volume increase dramatically. Such different is easier to
observe when we just plot the volume series (not taking logarithm).
Table.1 shows the descriptive statistics of stock returns and trading volume of US and
Chinese markets. Here we divide our sample into two periods, one before 2008 financial
crisis (period I) and the other after this crisis (period II). The precise dividing date is 15
Sept. 2008, the day when Lehman Brothers went bankruptcy. The reason we do so is that
after careful observation we find that stock behavior before and after 2008 financial crisis
are quite different, especially when we compute the correlation of stock prices between
the two markets, we find that correlation of period II is significantly bigger than that of
period I.
Proceedings of 3rd Global Business and Finance Research Conference
9 - 10 October 2014, Howard Civil Service International House, Taipei, Taiwan ISBN: 978-1-922069-61-0
In Table.1(a), we concludes that, like many other stock markets, neither daily return nor
daily volume is normally distributed (Jarque-Bera statistics is significantly large). The large
kurtosis indicates the existence of a high peak in return series, which is consistent with the
general knowledge of the return series of a stock. It also suggests that the probability of
Figure 1: Plot of returns and trading volume of Shanghai Composite Index ((a)(b)) and S&P500
((c)(d)).
(a)
(b)
(c)
(d)
occurring abnormal value is higher than the normal distribution, which explains the
frequent occurrence of spikes. But skewness of these series are approximately to be zero,
which shows that return series is symmetrically distributed with respect to zero.
Meanwhile, Table.1(b) lists the descriptive statistics of trading volume. The Jarque-Bera
statistics becomes much smaller and kurtosis becomes close to 3, which is the kurtosis of
normal distribution. A small skewness also indicates that volume series, like return series,
is symmetrically distributed.
Proceedings of 3rd Global Business and Finance Research Conference
9 - 10 October 2014, Howard Civil Service International House, Taipei, Taiwan ISBN: 978-1-922069-61-0
2.2 Unit root test
In the following sections, we are going to use Granger causality test and EGARCH model,
all requiring the data to be stationary, or having no unit root. So testing the existence of
unit roots is our first goal. To do this, we employ ADF (Augmented Dickey-Fuller) test and
PP (Phillip and Perron) test. Table.2 gives us the results of t-ratios of ADF test and PP test
for volume series in different markets. As we can see that all p values are significantly
approximated to be zero, which means we reject the null hypotheses of ADF test and PP
test, which both are that the target series has a unit root. In a word, Table.2 indicates that
all trading volume series have no unit roots. Note that we didn’t show the results of ADF or
PP test for return series because that return series, in most case, are unit-rootless. In fact,
we do conduct the above-mentioned tests for return series, and the results suggest, as we
predict, that no unit roots exist in any return series.
Table 1: Descriptive statistics of (a) returns and (b) trading volume (after taking logarithm) .
(a) Statistics of return
DJIA
NSDQ
SP
SHC
Period
I
II
I
II
I
II
I
II
Mean (%)
-0.0042
0.0251
-0.0079
0.0421
0.0053
0.0257
0.0570
0.0014
Std. Dev.
0.0090
0.0141
0.0107
0.0161
0.0094
0.0155
0.0250
0.0156
Skewness
-0.2220
0.0587
-0.0962
-0.1190
-0.2220
-0.1780
-0.6350
-0.1070
Kurtosis
4.800
12.50
4.010
9.487
5.110
11.70
7.000
6.400
Jarque-Bera
124
4690
38
2177
169
2919
639
600
(a) Statistics of volume
DJIA
NSDQ
SP
SHC
Period
I
II
I
II
I
II
I
II
Mean (%)
10.12
9.750
11.84
10.87
11.75
11.26
22.14
22.98
Std. Dev.
0.2488
0.4548
0.2646
0.3243
0.2201
0.4089
0.7856
0.3733
Skewness
0.0083
0.4112
0.3576
0.3120
-0.5383
0.1906
-0.2426
-0.0293
Kurtosis
4.840
3.163
4.500
4.024
5.770
2.702
2.117
2.485
Jarque-Bera
123.2
36.33
100.1
74.31
320
12.08
36.77
13.89
Table 2: Results of t-ratios of ADF test and PP test for the volume series.
ADF
Period
DJIA
NSDQ
SP
SHC
I
-33.33
(0.0000)
-32.16
(0.0000)
-34.31
(0.0000)
-30.51
(0.0000)
PP
II
-18.06
(0.0000)
-38.33
(0.0000)
-39.22
(0.0000)
-35.42
(0.0000)
Note. The p values are shown in the brackets.
I
-34.00
(0.0000)
-32.42
(0.0000)
-35.35
(0.0000)
-30.51
(0.0000)
II
-40.06
(0.0000)
-38.92
(0.0000)
-38.97
(0.0000)
-35.31
(0.0000)
Proceedings of 3rd Global Business and Finance Research Conference
9 - 10 October 2014, Howard Civil Service International House, Taipei, Taiwan ISBN: 978-1-922069-61-0
3. Methodology
3.1 Causal relationship between Chinese stock market and US stock market
To investigate the causal relationship between two variables, Granger (1969) introduced
the well-known Granger causality test. This test bases on the logic that if an event x
occurs before event y, then the historical data of x may be helpful in predicting y. If so, we
G.C
say x Granger causes y, denoted as x  y . So Granger causality is NOT the causality we
talk about in the real world; it doesn't tell us that x causes y but tells us x can be used to
make better prediction of y. Eq.1 gives us the formula to test Granger causality, which is in
the form of vector autoregressive model.
m
n
i 1
j 1
m
n
i 1
j 1
xt  c1    i xt i    j yt  j   t
yt  c2   i yt i    j xt  j  t
(1)
If the coefficients  j ’s are significant (use F test), we say that yt  j ’s are helpful in
forecasting xt , meaning that the R 2 of the linear regression is improved after adding yt  j ’s
G.C
as explanatory variables. Under this circumstance, y  x . The testing hypotheses of
Granger causality test are
H0： j =0 for all j HA： j  0 for some j
If we accept H 0 , we are rejecting the Granger causal relationship between x and y.
Moreover, if x Granger causes y and y Granger causes x, we say there is a feedback
relationship between them.
3.2 Spillover effect of US stock return and volume on Chinese stock return
US stock market is the largest stock market in the world, while Chinese stock market is
relatively small and immature. China has been trying to open its door since 1970s, so it is
reasonable to hypothesize that Chinese stock market is influenced by US stock market.
We will confirm our hypothesis with the daily data from 4 Jan 2005 to 31 Dec 2013. In
addition, we divide this period into two sub-periods, with one before 2008 financial crisis
and the other after 2008 financial crisis. The exact dividing date is 15 Sept 2008, when
Lehman Brothers went bankruptcy. We employ Eq.2 to test our hypothesis:
SHRt  c  1SHRt 1 +2USRt 1 +3USVt 1  ut
,
(2)
where SHR t is the return of SHC at day t, while USR t-1 and USVt-1 represent the return and
volume of a US stock market (for example, DJIA) at day t-1, respectively. Here we don't
use USR t or USVt is because of the time difference between China and US, as it is said in
Section 2.1. If  2 and  3 are significant, then we can draw the conclusion that there exists
a spillover relationship between Chinese and US stock markets.
Proceedings of 3rd Global Business and Finance Research Conference
9 - 10 October 2014, Howard Civil Service International House, Taipei, Taiwan ISBN: 978-1-922069-61-0
3.3 Trading volume and cross-market volatility
To solve the problem of conditional heteroskedasticity of volatility, Engel (1982) introduced
autoregressive conditional heteroskedasticity (ARCH) model. It requires the parameters of
variance equation to be positive, and people usually have to use a high order ARCH
model to describe volatility. Then Bollerslev (1986) added the lags of GARCH terms to the
variance equation, which is the well-known generalized autoregressive conditional
heteroskedasticity (GARCH) model. But GARCH model also requires the parameters of its
variance equation to be positive. To fix this problem, Nelson (1991) modified GARCH
model and introduced EGARCH model. Under the inspiration of Nelson, we use EGARCH
model to study how US trading volume a affects Chinese stock volatility. This model gives
us more flexibility to handle the real problem for it needs no positivity assumptions on
estimated parameters, and it also demonstrates asymmetric effects of positive and
negative news on stock returns. The model can be written as
ut  ht  t ,  t
i .i .d
N (0,1)
q
p
i 1
j 1
ln ht   0   (i t i   i |  t i |)    j ln ht  j  USVt 1  USVt 1IUSVt 1 ,
(3)
where
USVt

1, if USV  1

t 1
.
IUSVt 1  
USV
t
0, if
1

USVt 1
Here  t is assumed to be normally distributed. Parameter  i illustrates the magnitude of
the influence of past shocks on today's volatility, while  i measures the different impacts
of positive news and negative news. For instance, in the case  i < 0, when the shock
 t i < 0, the conditional variance will be bigger than it is when  t i > 0 but with the same
magnitude, i.e. the market tends to be more sensitive to the negativity. That is why
EGARCH model integrates the idea that bad news always have a stronger impact than
good news, especially in stock market.
Furthermore, the asymmetric effect of volume on price change or volatility has been study
for a long time (Karpoff 1987; Hussian 2011), so here we add the term IUSVt 1 to study the
asymmetric effect of US trading volume on Chinese stock volatility. When today's trading
volume increases with respect to its value yesterday, IUSVt 1 is 1; otherwise, it is 0. Suppose
 > 0, we see that positive change of US trading volume contributes more to the volatility
of Chinese stock market. However, if  < 0, a positive change of US trading volume will
decrease the volatility of Chinese stock market. Based on the above observations, we
would like to call this model EGARCH-V model
Proceedings of 3rd Global Business and Finance Research Conference
9 - 10 October 2014, Howard Civil Service International House, Taipei, Taiwan ISBN: 978-1-922069-61-0
4. Empirical results
4.1 Spillover effect and causal relationship between US and Chinese stock markets
That the measurement of liquidity such as trading volume could influence the volatility of
asset return is now a widely acknowledged notion. The purpose of this part is to test
whether such influence could exist between two different markets.
To test our hypothesis, we estimate the linear regression equation in Eq.2. The results are
reported in Table.3. First note that, in all cases,  2 is significant, meaning that USRt 1 has
an strong spillover effect on SHRt . This is consistent with our commonsense that US as
the most influential market imposes significant influence on other stock markets. On the
other hand, contrary to Gagnon and Karolyi (2003) and Hussian (2011)'s conclusion that
volume is usually negatively related to returns between two related markets, we find that
lag terms of US trading volume (the value of  3 ), in fact, contributes nothing to Chinese
stock returns, which guides us to get rid of USVt 1 in the mean equation of the following
EGARCH-V model. This disagreement with previous studies may come from the fact that
Chinese market is still underdeveloped, and it is also a relatively closed market.
Furthermore, we will drop SHRt 1 in our subsequent estimation of EGARCH-V because 1
fails to be significant in all cases.
Table 3: Results of estimation of coefficients in Eq.2.
SHRt  c  1SHRt 1 +2USRt 1 +3USVt 1  ut
c
SHC - DJIA
I
II
SHC - NSDQ
I
II
SHC - SP
I
II
1
2
3
0.0255
(0.3644)
-0.0125
(0.1820)
-0.0334
(0.3244)
-0.0251
(0.3719)
0.3206
(0.0000)***
0.2055
(0.0000)***
-0.0025
(0.3757)
0.0013
(0.1815)
-0.0220
(0.4632)
-0.0157
(0.2804)
-0.0315
(0.3503)
-0.0292
(0.2968)
0.2578
(0.0000)***
0.2041
(0.0000)***
0.0020
(0.4508)
0.0014
(0.2821)
0.0263
(0.4728)
-0.0132
(0.2696)
-0.0317
(0.3475)
-0.0274
(0.3271)
0.3115
(0.0000)***
0.2016
(0.0000)***
-0.0022
(0.4830)
0.0012
(0.2812)
Notes. The p values are shown in the brackets. *** indicates significance level at 1% level.
Regarding the results of Granger causality shown in Table.4, it can be seen that in all
cases, we reject the null hypothesis that SHR Granger causes USR but accept the
hypothesis that USR Granger causes SHR. This suggests that daily returns in US market
do have a leading effect on Chinese stock market, but the inverse is not true. This agrees
with and explains the findings in Table.3. An interesting observation is that during period II,
the F statistics is much larger than it is in period I. This may indicate that the 2008
Proceedings of 3rd Global Business and Finance Research Conference
9 - 10 October 2014, Howard Civil Service International House, Taipei, Taiwan ISBN: 978-1-922069-61-0
financial crisis strengthens the causal relationship between these two markets. But the
direction doesn't change, that is US imposes influence on China, not the inverse.
On the other hand, when examining the relationship between USV and SHR, we find that
neither of them can Granger cause the other one in all cases. This verifies our finding in
Table.3 that US trading volume doesn't possess a spillover effect on Chinese stock
returns. Though we may expect that some relation to be existing between these two
variables, we still accept this result since China is not so close related to US as stock
markets in other developed countries such as UK or Germany. Even the 2008 financial
crisis didn't make the connected.
After the analysis, we can safely draw the conclusion that the daily return of US stock
market, before and after the 2008 financial crisis, has a great spillover impact on the daily
return of Chinese stock market, but the latter doesn't have too much influence on the
former. Moreover, trading volume of US stock market seems to play an insignificant role
between these two markets.
4.2 Volatility-volume relation and the asymmetric effect of volume
In the previous section, we take away SHRt 1 and USVt 1 in Eq.2 because these two terms
are actually insignificantly different from 0. However, after taking these two terms, we
found that the residuals become correlated. So we add several lag terms of SHRt to the
mean equation to make residuals uncorrelated. After several trials, we found that for
period I, the best mean equation is
Table 4: Results of Granger causality tests.
F statistic at Period I
F statistic at Period II
4.030
15.32
(0.0000)***
(0.0000)***
0.6288
0.7212
(0.5964)
(0.5393)
0.0400
0.9997
(0.9894)
(0.3921)
1.854
0.9181
(0.1353)
(0.4314)
2.672
20.50
(0.0460)**
(0.0000)***
0.2812
0.2573
(0.8386)
(0.8561)
0.0237
0.8895
(0.9951)
(0.4459)
1.032
0.7544
SHC - DJIA
G .C
H A：USR  SHR
G .C
H A：SHR USR
G .C
H A：USV  SHR
G .C
H A：SHR USV
SHC - NSDQ
G .C
H A：USR  SHR
G .C
H A：SHR USR
G .C
H A：USV  SHR
G .C
H A：SHR USV
Proceedings of 3rd Global Business and Finance Research Conference
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(0.3772)
(0.5198)
4.210
18.02
(0.0056)***
(0.0000)***
0.6626
0.6399
(0.5750)
(0.5894)
0.1834
0.7246
(0.9077)
(0.5373)
0.9933
1.159
SHC – SP
G .C
H A：USR  SHR
G .C
H A：SHR USR
G .C
H A：USV  SHR
G .C
H A：SHR USV
(0.4234)
(0.3242)
Notes. Here we gives H A , instead of H 0 . So if p value is very large, then we reject H A . We use
three lag terms. The p values are shown in the brackets. *** and ** indicate significance at 1% and
5% level, respectively.
SHRt  a1USRt 1  b1SHRt 3  b2 SHRt 13  ut ,
and for period II, the best mean equation is
SHRt  a1USRt 1  b1SHRt 7  b2 SHRt 32  ut .
With the new mean equation, the results of the estimation of EGARCH-V(1,1) model are
listed in Table.5. Let's first focus on the results in Period I. Insignificant values of  and 
are found for these three markets, which indicates that US trading volume has no spillover
effect on SHC, and of course no asymmetrical effect either. Interestingly, for S&P  is
insignificant, but  is significant at the level of 1%. But this doesn't verify the asymmetrical
effect of trading volume for S&P since an insignificant  has already indicates that trading
volume has no predictive power for return volatility, not mentioning the asymmetrical effect
of trading volume.
Proceedings of 3rd Global Business and Finance Research Conference
9 - 10 October 2014, Howard Civil Service International House, Taipei, Taiwan ISBN: 978-1-922069-61-0
Table 5: Results of estimation of coefficient of EGARCH-V(1,1) model.
EGARCH-V(1,1) model.:
Period I: SHRt  a1USRt 1  b1SHRt 3  b2 SHRt 13  ut
Period II: SHRt  a1USRt 1  b1SHRt 7  b2 SHRt 32  ut
ut  ht  t ,  t
i .i .d
N (0,1)
ln ht  0  1 t 1   1 |  t 1 |  1 ln ht 1  USVt 1  USVt 1IUSVt1
Panel A
a1
b1
b2
SHC – DJIA
I
II
SHC – NSDQ
I
II
SHC - SP
I
II
0.3352
(0.0000)***
0.0836
(0.0196)**
0.1160
(0.0005)***
0.2227
(0.0000)***
0.0647
(0.0152)**
-0.0713
(0.0090)***
0.2367
(0.0000)***
0.0776
(0.0286)**
0.1180
(0.0004)***
0.1981
(0.0000)***
0.0666
(0.0121)**
-0.0692
(0.0139)**
0.3153
(0.0000)***
0.0868
(0.0136)**
-0.0868
(0.0035)***
0.2146
(0.0000)***
0.0681
(0.0097)***
-0.0712
(0.0104)**
-0.2908
(0.2723)
-0.0359
(0.0121)**
0.2151
(0.0000)***
0.9670
(0.0000)***
-0.0155
(0.5902)
0.0061
(0.1558)
-1.010
(0.0023)***
-0.0337
(0.0029)***
0.0669
(0.0010)***
0.9512
(0.0000)***
0.0501
(0.0089)***
0.0104
(0.0047)***
-0.3907
(0.0811)*
-0.0360
(0.0047)***
0.2037
(0.0000)***
0.9704
(0.0000)***
0.0010
(0.9621)
0.0004
(0.9233)
-0.7254
(0.0069)***
-0.0153
(0.0652)*
0.0515
(0.0003)***
0.9740
(0.0000)***
0.0461
(0.0083)***
-0.00073
(0.0102)**
-0.2592
(0.4674)
-0.0370
(0.0104)**
0.2117
(0.0000)***
0.9655
(0.0000)***
-0.0203
(0.5367)
0.0117
(0.0027)***
-0.9870
(0.0054)***
-0.0222
(0.0406)**
0.0611
(0.0014)***
0.9562
(0.0000)***
0.0485
(0.0130)**
0.0028
(0.3735)
Panel B
0
1
1
1


Notes. The p values are shown in the brackets. ***, ** and * denote the significance level of 1%,
5% and 10%, respectively
During period II, situation changes. In all cases, significant  is observed, meaning that
trading volume begins to convey information about return volatility. This confirms our initial
hypothesis that trading volume interacts with other information and together makes the
price to move. There are several reasons accounting for this distinction before and after
2008 financial crisis. One possible explanation is that 2008 financial crisis made many
investors understands that Chinese stock market is not an island which has little
communication with the outside but an integrated part of the global economy. Before
2008, China stock market is mainly a policy market, and international events don't affect it
too much. But during 2008 financial crisis, SHC plunged from more 6000 to nearly 2000, a
two thirds loss. Then people suddenly realized that China could also be severely affected
by the rest of world! Nonetheless, this is only one plausible reason from the psychological
view.
Proceedings of 3rd Global Business and Finance Research Conference
9 - 10 October 2014, Howard Civil Service International House, Taipei, Taiwan ISBN: 978-1-922069-61-0
In addition, coefficient 1 is very high (above 0.95) in all cases, meaning that there exists
strong persistence of return volatility or that the influence of old data persists to influence
new data.
Moreover, during period II, DJIA and NSDQ have a significant  . Recall that  represents
the asymmetrical effect of trading volume: if today's trading volume increases with respect
to yesterday, IUSVt  1 ; otherwise, IUSVt  0 . One interesting point is that although  for both
DJIA and NSDQ are significant, their signs are different, with DJIA having a positive 
(0.0104) and Nasdaq having a negative  (-0.0073). And  for S&P500 is insignificant.
So three US markets exhibit three different patterns.
5. Concluding Remarks
The relationship between trading volume and price change is always one of the focuses in
financial markets. This paper empirically examines the impact of trading volume in the US
stock market on the return (or return volatility) of the Chinese stock market. From the
perspective of return, the US market is found to Granger-cause the Chinese market, but
no evidence shows the role of US trading volumes in influencing the Chinese stock
returns. To confirm our empirical results, three main stock indexes in the US are used in
this paper, and the sample data is divided into two subsamples by considering the big
shock of 2008 financial crisis to the financial markets worldwide. In each case, the trading
volume of the US stock market fails to significantly affect the stock return in China.
However, as far as the return volatility is concerned, there is a quite different story. Before
the 2008 financial crisis, the trading volume of the US stock market doesn’t contribute to
the return volatility of Chinese stock market, no matter whichever stock index of the U.S. is
used. After the crisis, the US trading volumes reveal their significant role in positively
influencing the stock return volatility in China. Our empirical findings based on the sample
data after the crisis are consistent with the results in Lee and Rui (2002) and Gagnon and
Karolyi (2003), where the volume-volatility relationship between US and other stock
markets are studied.
Summarizing the empirical results in this paper, we can find that Chinese stock market is
becoming much more closely related to the US markets, especially after the break out of
2008 financial crisis. Before the crisis, even as the most influential financial markets in the
world, the trading volume information of the US markets cannot manifest their power in
helping predict the return or volatility of Chinese stock market. Whereas, with the closer
linkage among different economies caused by the crisis, the US trading volumes begin to
play their role in influencing the stock volatility in China, though they are still lack of
influential power on Chinese stock return. With the ever-integration of Chinese market into
worldwide financial markets, a further study on the interactions between trading volume
and price change among different economies will be feasible and interesting.
Proceedings of 3rd Global Business and Finance Research Conference
9 - 10 October 2014, Howard Civil Service International House, Taipei, Taiwan ISBN: 978-1-922069-61-0
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