2.
I. Introduction
Concurrent with its rapid economic growth, the China stock market, initiated in early 1990’s, has
been expending tremendously in the last two decades. Despite being disadvantaged by their short
histories, and the coexistence of political and regulatory burdens, both Shanghai and Shenzhen
exchanges in China have been attracting a huge influx of domestic and foreign investments
amounting to a capitalization of US$464.29 billion in 1378 listed companies and more than
attracting up to 72 million registered investors in 2005. One unique feature of the Chinese stock
market is its segmented trading system. First, dual listing is not allowed, resulting in a company
being restricted to be listed in only one of the two exchanges: Shanghai or Shenzhen. However, a
listed company in either exchange can issue two types of shares: “A” shares for domestic investors
and “B” shares for foreign investors, including overseas Chinese residing in Hong Kong, Macau and
Taiwan. Thus, the equity of the same firm could be traded at the same time and in the same
exchange, but at different “markets” with different prices traded by domestic and foreign investors.
The segmentation in the Chinese stock market has garnered great amount of attention from
investors and the spillover effects between their A and B shares in both Shanghai and Shenzhen
exchanges are often a source of heated debate. The companies listed in the Shenzhen Stock
Exchange are mainly smaller export-oriented companies while those listed in the Shanghai Stock
Exchange are mainly state-owned enterprises, many of them are virtually monopolistic suppliers to
the domestic markets. Due to the absence of cross-listing, different sensitivities drawn by the same
common factor between the two stock exchanges could be due to the different natures of the listed
companies. Hence, a comparative study of the behaviors of the Shanghai and Shenzhen stock
exchanges and their corresponding A and B shares would shed light on how specific categories of
companies response to the common market factor. A detailed investigation of the dependence
dynamics between A and B shares could lead to better understanding of the behaviors of domestic
and foreign investors. If markets are efficient, any information regarding firm specific or common
market factors should be reflected in prices of both A and B shares and also result in the same degree
of price changes simultaneously as both A and B shares are issued by the same company. However,
in practice, as the two shares are traded by distinct groups of investors, departure from the perfect
1
3.
dependence situation will reveal the existence of asymmetric information and different behaviors of
domestic and foreign investors. In addition, studying the impact of the outside world on the China
stock market is also an important issue, especially after China accelerates its deregulation rules in its
financial markets, removing many restrictions previously imposed on its financial markets. Such
resultant changes will spur the influx of investment capital from the international investors eager to
cash in China’s evolving financial markets.
Bailey (1994), one of the earliest papers on the China stock market, reported simple statistics
and regression results in China indices while Ma (1996) conducted a cross sectional analysis to
explain the puzzling pricing of B shares. Kim and Shin (2000) further conducted cross
autocorrelation and Granger causality test to investigate the interactions among China stocks.
Recently, Boo and Zhang (2000) employed a co-integration technique to analyze the information
diffusion between A and B shares. As GARCH modeling has also shed some light by the analysis of
the information transmission among markets (Hamao and Masulis, 1990; Liu and Pan, 1997; Lean
and Wong, 2004), GARCH modeling has since grown in popularity. Su and Fleisher (1998) is one of
the earlier papers that apply GARCH (1,1) model to characterize the risk and return behaviors in the
China stock market. Recently, Brooks and Ragunathan (2003) studied the volatility spillover effect
between A and B shares by using univariate GARCH method. However, the univariate GARCH
modeling has its limitations in detecting the conditional correlation among markets. To circumvent
this problem, we study the risk and return behavior of the Chinese stock market by utilizing a two-
stage bivariate GARCH (BGARCH) model. We find that in the Shanghai exchange, B shares are
more influential in the information transmission. However, in the smaller and less liquid Shenzhen
exchange, the spillover effect direction reverses. In addition, our results reveal the existence of time-
varying interdependence patterns between A and B shares in both exchanges and find that the
evolution of conditional dependence is closely linked to specific events, especially on the
announcements of China’s government intervention policies on the stock market. We also find that
Hong Kong, as a neighbor of mainland China’s economy, has more influence than the superpower
economy in the world, namely the US market on Shanghai and Shenzhen stock exchanges.
2
4.
II. Data and Methodology
The weekly indices in terms of US dollars spanning fourteen years from 1992 to 2005 has being
analyzed in our paper include Shanghai A share index (SH A), Shanghai B share index (SH B),
Shenzhen A share index (SZ A), Shenzhen B share index (SZ B), S&P 500 index (SP) and Hang
Seng index (HSI). The first four indices represent indices in China stock market traded in the
Shanghai Securities Exchange (SHSE) and Shenzhen Securities Exchange (SZSE) in which A (B)
shares are restricted to being traded by domestic (foreign) investors. The indices obtained from
Datastream International have been adjusted according to dividend, allotment and share split. We
use weekly equity indices in our study to alleviate the effects of noise characterizing daily or higher
frequency data. Wednesday indices are used to avoid the week-day effect as stock markets are
known to be more volatile on Monday and Friday (Lo and MacKinlay, 1988).
Let Ria ,t and Rib ,t to be the log-returns of the A- and B-share indices in the SH and SZ
exchanges respectively and Rus ,t and Rhk ,t to be the log-returns of SP and HSI respectively. Given
the information set I t −1 containing information up to time t-1, we extend the two-factor model of Ng
(2000) to a BGARCH framework to investigate the impacts of US and HK on A- and B-share
indices in the SH and SZ exchanges such that:
Ra ,t = β a + γ a1 Ra ,t −1 + γ b1 Rb ,t −1 + λusa Rus ,t −1 + λhka Rhk ,t −1 + ε a ,t
Rb ,t = β b + γ b2 Rb ,t −1 + γ a 2 R a ,t −1 + λusb Rus ,t −1 + λhkb Rhk ,t −1 + ε b ,t
ε a ,t = ea ,t + φhka ehk ,t + ψ usa eus ,t 1
(1)
ε b ,t = eb ,t + φhkb ehk ,t + ψ usb eus ,t
⎡ e a ,t / I t −1 ⎤
where ⎢ ⎥ ~ N (0, ∑ t ) .
⎣ e b ,t / I t −1 ⎦
The variance-covariance matrix, ∑ t , follows the BEKK model of the form:
1
For simplicity, we skip listing the ARCH (ARCH(A->A), ARCH(A->B), ARCH(B->A) and ARCH(B->B)) and
GARCH (GARCH(A->A), GARCH(A->B), GARCH(B->A) and (GARCH(B->B)) terms in the equations.
3
5.
p q
∑ t = A0 A0 + ∑ Ai (ε t −i ε t′−i )Ai′ + ∑ B j ∑ t − j B′j
′ (2)
i =1 j =1
where A0 is a lower triangular matrix, and Ai ' s and B j ' s are unrestricted matrices. For simplicity,
we set p=1 and q=1 in our study. The information transmission between US and HK is investigated
by adopting the BGARCH(1,1) to model the conditional mean and conditional variance of their
corresponding returns such that:
⎡ Rus ,t ⎤ ⎡α us , 0 ⎤ ⎡α us ,1α us , 2 ⎤ ⎡ Rus ,t −1 ⎤ ⎡ε us ,t ⎤
⎢ ⎥=⎢ ⎥+⎢ ⎥⎢ ⎥+⎢ ⎥
⎣ Rhk ,t ⎦ ⎣α hk , 0 ⎦ ⎣α hk ,1α us , 2 ⎦ ⎣ Rhk ,t −1 ⎦ ⎣ε hk ,t ⎦ (3)
⎡ε us ,t ⎤ ⎡1, χ t −1 ⎤ ⎡eus ,t ⎤ ⎡σ 2 0 ⎤
where ε t = ⎢ ⎥ =⎢ ⎥⎢ ⎥ = X t −1et , et I t −1 ~ N (0, ∑ ushk ) , ∑ ushk = ⎢ us ,t 2 ⎥ , (4)
⎢ε hk ,t ⎥ ⎣0,1 ⎦ ⎢ehk ,t ⎥ ⎢0 σ hk ,t ⎦
t t
⎣ ⎦ ⎣ ⎦ ⎣
and χ t −1 , σ us ,t , σ hk ,t are computed by Cholesky decomposition.
2 2
In this framework, the mean spillover effects between the A- and B-share indices are
reflected by the parameters γ b1 and γ a 2 ; the mean spillover effects from US and HK to A-share and
B-Share indices are revealed in the parameters λusa , λusb , λhka and λhkb ; while the volatility spillover
effects from US and HK to A- and B-share indices are reflected in the parameters φusa , φusb , φhka and
φhkb respectively. This model enables us to investigate the spillover effects between the A- and B-
share indices as well as spillover effects from the external factors – UK and HK -- to the A- and B-
share indices. However, common information may exist to drive both US and HK markets. To
circumvent this problem, the innovation from US and HK is further assumed to be orthogonalized
such that HK return shock is driven by a purely idiosyncratic shock and by the US return shock as
given by (4).
III. Empirical Results and Discussion
We first exhibit in Table 1 some descriptive statistics for the returns of all the indices being studied
in this paper. The table shows that, compared with US and HK, all the indices in the Chinese stock
4
6.
market are characterized by much lower mean returns but higher volatilities. This differs from the
results found in Bekaert and Harvey (1997) that distinguishing features of emerging market returns
display high mean returns and higher volatilities, demonstrating that the China market is unique even
among the emerging markets. The low means and high volatilities could attribute to a number of
reasons. First, almost all the domestic investors in China are unsophisticated, with little experience
or skill in trading, they tend to subscribe to a severe form of “herd mentality”. Secondly, the China
market is believed to be inappropriately intervened by its central government, thereby wild rumors
and panics are highly disruptive to the equity growth and, consequently, foreign institutional
investors are discouraged from investing in the China markets.
In addition, the sample moments for the returns shown in Table 1 reveal that all of their
corresponding empirical distributions possess heavy tails and the Jarque-Bera statistic further
confirms the non-normality behavior in all the return series. Autocorrelation test also shows that the
first order correlations of both returns and squared returns are significant and prominent with the
Ljung-Box statistic which further confirms the persistence of linear dependency and show strong
non-linear dependency in the returns of all the indices. Thus, the mean equation with VAR(1)
process and the variance equation with BGARCH(1,1) could be appropriate to fit all the return series.
January 1, 1996 is used as a cut-off point to break down data into two sub-periods: the pre-
1996 (October 14, 1992 to December 31, 1995) and post-1996 (January 1, 1996 to May 31, 2005)
sub-periods to investigate the changing patterns of the spillover effects in the sub-periods as the
China market has undergone tremendous significant institutional transformation in 1996. Thereafter,
the China market became more regulated and mature and thus all of the China indices display
turning points in 1996. Applying the models in (1) to (4) to analyze the behaviors of both Shanghai
and Shenzhen exchanges in the pre-1996 and post-1996 periods, we exhibit the results in Tables 2
and 3. 2 Overall, the results display evidence of the spillover effects between the A- and B-share
indices.
2
For simplicity, we only report results for Equations (1) and (2) and skip the results for Equations (3) and (4) which are
available on request.
5
7.
For the Shanghai exchange, Table 2 shows that the mean spillover effect from B to A shares
( γ b1 =1.55, 0.88) is significant before 1996 ( γ b1 =1.55) but disappears after 1996 3 ( γ b1 =0.88). On the
other hand, Hong Kong market displays the mean spillover effect on B shares before 1996
( λhkb =2.66) and on A shares after 1996 ( λhka =-1.34). There is no mean spillover from US to both A
and B shares before 1996 but the spillover from US to B shares becomes significant after 1996
( λusb =1.30). In the BGARCH variance equation, the appealing ARCH volatility spillover effects
from B shares to A shares in both pre-1996 and post-1996 sub-periods (ARCH(B->A)=2.97, 1.29)
are significance and the GARCH spillover effect from B to A shares becomes stronger after 1996
(GARCH(B->A)=-1.81). These results show that the prices of B shares are more influential in the
Shanghai market by reflecting time dependence in the process when information flow from B shares
to the A shares is generated and volatility shocks are allowed to persist over time. As for the
volatility spillover effects from HK and US, HK affects B shares in both sub-periods: before 1996
( φhkb =4.02) and after 1996 ( φhkb =5.16) and affects A shares only after 1996 ( φhka =3.12) while US
takes no role on the dynamics of the A and B shares’ volatilities.
For the Shenzhen exchange, Table 3 shows that before 1996 the mean spillover effect
appears significantly only from A to B shares ( γ a 2 =2.54) while neither HK nor US possesses any
mean spillover effect to the A or B shares. After 1996, there is evidence of spillover effects from HK
to the B shares ( λhkb =1.41) and from US to A shares ( λusa =1.62). As for the conditional variance,
some of the ARCH effects and all the GARCH effects are significant in both directions (A to B
shares and verse visa) and in both sub-periods; implying that prices of A and B shares have feedback
effects in the Shenzhen exchange. On the other hand, we observe the spillover effect from HK to B
shares before 1996 ( φ hkb = 1.42) while all the spillover effects from both HK and US to both A and
B shares are significant in the post-1996 period ( φ hka = 2.12, φ hkb = 4.12, ψ usa = 1.33 and ψ usb =
2.04) with smaller p-values for φ hka , φ hkb ; indicating the effects from HK are stronger than those
from the US.
3
We note that in this paper “before (after) 1996” is referred to “before (after) January 1, 1996”.
6
8.
We next study the evolution of correlations between the A-share and B-share indices for both
Shanghai and Shenzhen exchanges by exhibiting Figures 1 and 2. Several comments can be made.
First, on average, correlations between the A- and B-share indices are insignificant before 1996 in
both Shanghai and Shenzhen exchanges. After 1996, the correlations become positive and more
significant in value in general; implying of a tendency for the conditional correlations to move
upwards for both markets since 1996. Secondly, the evolution of correlation of between the A- and
B-share indices in the Shanghai and Shenzhen exchanges become more mimetic in post-1996 period.
Thirdly, our results reveal that the evolution of conditional dependence of both Chinese exchanges is
closely linked to specific events, best exemplified by China’s government announcement of any
intervene policies on the stock market and economy. For instance, in the second half of 1997, the
correlations are very significant in both exchanges; reflecting the consequences of Asian Financial
crisis. Another peak of the correlation appeared in the second quarter of year 1999 after the central
government signaled its policy to encourage investment on its stock market through a commentator
article in the May 19, 1999 issue of “People’s Daily”, the major governmental newspaper in China.
In June 2001, the central government launched the policy of so called “reduction of state-owned
shares’ holdings” to convert the huge volume of non-tradable shares--most of them are state-owned
shares-- into tradable shares. This policy induced severe panic and uncertainty in the market and
hence the market crashed immediately after the announcement of this policy. However, as the policy
only applied to A-shares, only the price of A-shares were negatively affected. This leads to a neap in
dependence between A- and B-shares around June 2001. The correlations rose again and reached a
new high in June 2002, when prices of A-shares spiked on the basis of government announcement
on suspending the policy of reduction of holding of its state-owned shares. After April 2003, there
was another instance of high correlation. This is probably caused by the emergence of the SARS
epidemic which negatively impacted the sentiments of both domestic and foreign investors. On the
whole, we find that the existence of a correlationship between A and B shares sensitive to changes in
government policies on stock market, supporting the fact that China’s stock market is still subject to
heavy intervention by its central government.
At last, we report the results of the diagnostics check in Table 4 for the relevant models being
adopted by displaying the Ljung-Box tests on the standardized residuals and on the squared
standardized residuals. As all of the p-values are larger than conventional levels, we conclude that
7
9.
the fitted model is adequate and successful in capturing the dynamics in the first as well as second
moments of the return series, which in turn implies that our analysis and conclusion made are
appropriate.
IV. Conclusion
In this paper, we employ the two-stage BGARCH model to analyze the return and volatility spillover
and the dynamics of the dependence between the A- and B-share indices. We also investigate how
external factors from US and HK influence the means and volatilities of the A- and B-share indices.
Our results show that on the whole, the spillover effects between A and B shares do exist but
not strong. In the Shanghai exchange, where most state-owned big companies are listed, B-share
index is more influential in the information transmission. However, in the smaller and less liquid
Shenzhen exchange, the spillover effect direction is more from A to B shares, implying that the
domestic investors have more timely access to information than foreign investors. Thus, our
evidence from Shanghai exchange but not Shenzhen exchange supports the arguments by Chui and
Kwok (1998) that foreign investors are both better informed and have more timely access to
information than domestic investors due to being less impacted by the information barriers in
existence in China. The evidence from Shanghai market also supports the claim from Badrinath et al
(1995) that as B-share investors are mainly big financial institutions while domestic A-share holders
are relatively smaller investors, the returns of the institutional favored shares surpasses those of
institutional less favored shares. However, the evidence from Shenzhen market shows the opposite
story. One may ask the question as to why the lead-lag relationship from B-share to A-share exists
only in Shanghai, but not in Shenzhen. The reason could be that the Shenzhen stock exchange is
dominated by small firms. 4 , which are not favored by the big or foreign financial institutions. Thus,
the timeliness in obtaining information with B-share investors is relatively not so crucial in the
Shenzhen exchange. In addition, we find that the evolution of conditional dependence between A
and B shares is closely linked to specific events, especially on the announcements of China’s
government intervene policies on its stock market, inferring that the China stock market is still
strongly influenced by the central government’s policies .
4
Readers may read, for example, Chan (1993) and Bailey and Jagtiani, (1994), for more detailed explanation.
8
10.
International financial markets have become increasingly interdependent and thus, as to how
much China’s stock market has become part of the integrated world market is always an interesting
topic of debate. Overall, our results show that the impact of US and Hong Kong on Shanghai and
Shenzhen exchanges has become more significant after 1996; demonstrating that the China stock
market has become more integrated with the outside world. This is not surprising as Chinese
government has launched many policies to facilitate market-oriented reform after 1996. However,
although the US market is believed to be a dominating factor in world stock markets, our findings
show that its spillover effect to China’s market is limited compared with Hong Kong. For the
Shenzhen exchange in which both Hong Kong and US play an important role, the magnitude of the
effect from US is relatively smaller. This is not surprising as Hong Kong is the most intimate market
to China due to the economic, political as well as geographical proximity and it is the China’s largest
economics partner in terms of capital inflow and foreign direct investment (FDI) inflows. As US
could be a representation of the world factor while the stock exchange of Hong Kong, one of the
largest stock market in East Asia region, could stand for the regional factor, our results could infer
that the China stock market is only partially integrated into the world market, and it is closer to the
regional markets. Nonetheless, the overall findings provide evidence to support the view that
China’s stock market is at least partially integrated with the international stock markets.
Table 1: Descriptive Statistics for the Returns of the Weekly Stock Indices
Shanghai A Shanghai B Shenzhen A Shenzhen B S&P500 Hang Seng
Mean 0.006% 0.016% -0.057% -0.070% 0.165% 0.137%
Minimum 40.823% 26.383% 36.189% 34.727% 10.182% 13.228%
Maximum -37.291% -22.774% -41.058% -37.489% -9.041% -14.197%
St.D 5.934% 5.281% 5.410% 6.001% 2.181% 3.579%
Skewness 0.570 0.436 -0.186 0.108 -0.144 -0.459
Kurtosis 15.267*** 6.244*** 14.547*** 12.031*** 5.055*** 4.315***
Jaque-
Bera 4174.11*** 310.32*** 3670.23*** 2244.24*** 118.41*** 70.73***
p-value 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000***
rho 0.112 0.079 0.100 0.086 -0.101 0.031
LB(10) 0.013** 0.033** 0.162 0.009*** 0.011** 0.088*
Rho2 0.275 0.273 0.067 0.103 0.198 0.134
LB2(10) 0*** 0*** 0*** 0*** 0*** 0***
All weekly log-returns are calculated in US dollars, rho and rho2 are the first order serial correlations of returns and
squared returns respectively. LB(10) and LB2(10) are the p-values of Ljung-Box statistics with 10 lags.
9
11.
Table 2 : Model Specification for Shanghai A-share and B-share Indices
Mean Spillover
Oct 14, 1992 to Dec 31, 1995 Jan 1, 1996 to May 31, 2005
t-stat p-value t-stat p-value
γ a1 (A->A) -0.95 0.17 -1.17 0..10*
γ a 2 (A->B) 1.04 0.14 0.14 0.44
γ b1 (B->A) 1.55 0.06* 0.88 0.18
γ b 2 (B->B) -1.4 0.44 2.02 0..002***
λhka (HK->A) 0.17 0.43 -1.34 0.08*
λhkb (HK->B) 2.66 0.004*** -0.33 0.36
λusa (US->A) 0.89 0.18 0.13 0.43
λusb (US->B) 0.61 0.27 1.30 0.009***
Volatility Spillover
Oct 14,1992 to Dec 31, 1995 Jan 1, 1996 to May 31, 2005
t-stat p-value t-stat p-value
ARCH(A->A) 2.44 0.007*** 4.14 0.000***
ARCH(A->B) -0.89 0.18 0.02 0.49
ARCH(B->A) 2.97 0.001*** 1.29 0.09*
ARCH(B->B) 0.78 0.21 6.49 0.000***
GARCH(A->A) 1.49 0.000*** 40.16 0.000***
GARCH(A->B) 1.25 0.11 -0.53 0.29
GARCH(B->A) 0.03 0.48 -1.81 0.03**
GARCH(B->B) 2.39 0.000*** 51.8 0.000***
φhka (HK->A) -0.33 0.37 3.12 0.000***
φhkb (HK->B) 4.02 0.000*** 5.16 0.000***
ψ usa (US->A) -0.43 0.33 0.94 0.17
ψ usb (US->B) 0.54 0.39 0.73 0.23
γ a 2 represents the mean spillover from A share to B share; γ b1 represents the mean spillover from B share to A share; GARCH(A->B) and
GARCH(B->A) are volatility spillovers between A and B share respectively; λusa , λusb , λ hka , λ hkb are mean spillover effects from US and HK
to A and B shares respectively; and φusa , φusb , φ hka , φ hkb are volatility spillovers from US and HK to A and B shares respectively.
10
12.
Table 3 : Model Specification for Shenzhen A-share and B-share Indices
Mean Spillover
Oct 14, 1992 to Dec 31, 1995 Jan 1, 1996 to May 31,2005
t-stat p-value t-stat p-value
γ a1 (A->A) 0.53 0.29 0.01 0.49
γ a 2 (A->B) 2.54 0.00*** -0.66 0.25
γ b1 (B->A) -0.05 0.47 0.63 0.26
γ b 2 (B->B) 0.22 0.41 0.35 0.36
λhka (HK->A) 0.67 0.25 -0.95 0.16
λhkb (HK->B) -0.71 0.23 1.41 0.07*
λusa (US->A) 1.01 0.15 1.62 0.05**
λusb (US->B) -0.69 0.24 -0.02 0.49
Volatility Spillover
Oct 14, 1992 to Dec 31, 1995 Jan 1, 1996 to May 31,2005
t-stat p-value t-stat p-value
ARCH(A->A) 3.11 0.001*** 5.81 0.000***
ARCH(A->B) 0.77 0.21 3.04 0.001***
ARCH(B->A) -0.91 0.17 0.43 0.33
ARCH(B->B) 2.01 0.02** 4.85 0.000***
GARCH(A->A) 13.81 0.000*** 29.11 0.000***
GARCH(A->B) -1.63 0.05** -3.31 0.000***
GARCH(B->A) 1.41 0.08* -1.81 0.03**
GARCH(B->B) 30.65 0.000*** 30.25 0.000***
φhka (HK->A) 0.82 0.2 2.12 0.01****
φhkb (HK->B) 1.42 0.07* 4.12 0.000***
ψ usa (US->A) -0.14 0.44 1.33 0.09*
ψ usb (US->B) 0.03 0.48 2.04 0.02**
γ a 2 represents the mean spillover from A share to B share; γ b1 represents the mean spillover from B share to A share; GARCH(A->B) and
GARCH(B->A) are volatility spillovers between A and B share respectively; λusa , λusb , λ hka , λ hkb are mean spillover effects from US and HK
to A and B shares respectively; and φusa , φusb , φ hka , φ hkb are volatility spillovers from US and HK to A and B shares respectively.
11
13.
Table 4: Diagnostic Check for the A-Share and B-Share Indices
Shanghai A and B before 1996
Test White noise test GARCH effect test
(Ljung-Box) (Ljung-Box)
Series statistic p-value statistic p-value
Shanghai A 7.406 0.829 3.506 0.990
Shanghai B 12.534 0.404 4.104 0.981
Shanghai A and B after 1996
Test White noise test GARCH effect test
(Ljung-Box) (Ljung-Box)
Series statistic p-value statistic p-value
Shanghai A 20.070 0.079 10.05 0.611
Shanghai B 19.110 0.086 12.02 0.444
Shenzhen A and B before 1996
Test White noise test GARCH effect test
(Ljung-Box) (Ljung-Box)
Series statistic p-value statistic p-value
Shenzhen A 3.221 0.994 3.294 0.993
Shenzhen B 6.947 0.861 1.008 1.000
Shenzhen A and B after 1996
Test White noise test GARCH effect test
(Ljung-Box) (Ljung-Box)
Series statistic p-value statistic p-value
Shenzhen A 17.110 0.146 7.392 0.831
Shenzhen B 12.534 0.088 6.682 0.878
12
14.
Figure 1: Conditional correlation between the A-share and B-share indices before 1996
Conditional correlation between Shanghai A and B shares before 1996
0.5
0.3
0.1
-0.1
Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1
1992 1993 1994 1995 1996
Conditional correlation between Shenzhen A and B shares before 1996
0.8
0.5
0.3
-0.1
Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1
1992 1993 1994 1995 1996
Note: The horizontal line represents value of zero conditional correlation
13
15.
Figure 2: Conditional correlation between the A-share and B-share indices after 1996
Coditional correlation between Shanghai A and B shares after 1996
0.7
0.5
0.3
0.1
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Coditional correlation between Shenzhen A and B shares after 1996
0.7
0.5
0.3
0.1
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Note: The horizontal line represents conditional correlation being 0.2
14
16.
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