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Mean and Volatility Effect among stock markets of Mainland ... Mean and Volatility Effect among stock markets of Mainland ... Document Transcript

  • Mean, Volatility Spillover and Time-varying Conditional Dependence in Chinese Stock Markets Yi Zheng and Wing-Keung Wong RMI Working Paper No. 07/02 Submitted: January 3, 2007 Abstract This paper adopts a two-stage bivariate GARCH model to analyze the mean and volatility spillovers and time-varying conditional dependence between A and B shares in the China stock market. Impacts of US and Hong Kong on China market are also examined. We find that in the Shanghai exchange where most state-owned big companies are listed, B shares trading is more influential in the information transmission while in the Shenzhen exchange with a smaller and less-liquid market, the direction of the spillover effect reverses. We also find that there exist some time-varying dependence patterns between A and B shares in both Shanghai and Shenzhen exchanges. In addition, there is an upward trend of conditional correlation in both A and B shares after 2002. The evolution of conditional dependence is closely linked to China’s government intervene policies on its stock market. In addition, our findings reveal that Hong Kong, as a neighbor of mainland China’s economy, is more influential than the US on the Shanghai and Shenzhen stock exchanges. Keywords: China stock market, two-stage bivariate GARCH model, spillover effect. Yi Zheng Wing-Keung Wong Department of Economics Risk Management Institute and National University of Singapore Department of Economics AS5 04-07 National University of Singapore Singapore 117570 Block S16, Level 5, 6 Science Drive 2 Tel: (65) 6516-5195 Singapore 117546 Email: eaizy@nus.edu.sg Tel: (65) 6874-6014 Fax: (65) 6775-2646 Email: rmiwwk@nus.edu.sg © 2007 Wing-Keung Wong. Views expressed herein are those of the author and do not necessarily reflect the views of the Berkley-NUS Risk Management Institute (RMI).
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
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