Abstract
JEL Classification: G14, G15, G18
Introduction
In rational models of stock price formation, investors base their decisions on fundamental asset values. In contrast to this view, the last two decades have seen a rapidly growing body of behavioral finance literature dealing with deviations from such rational behavior.
Introduction
Among other things, some stock market participants may base their investment decisions on past returns performance. Buying after prices have risen and selling in response to price declines irrespective of fundamental values is usually called positive feedback trading or momentum investing. The presence of positive feedback traders may destabilize stock markets by driving prices away from fundamentals (De Long et al., 1990b) and impeding arbitrage (De Long et al., 1990a; Brunnermeier and Nagel, 2004 ).
Introduction
In the case of Chinese stock markets, there is widespread popular belief that the tremendous growth in recent years has attracted sentiment-driven investors. Eun and Huang (2007) for instance cite the Wall Street Journal (August 22, 2001 ) comparing domestic stock markets to "casinos, driven by fast money flows in and out of stocks with little regard for their underlying value." A voluminous literature suggests that domestic Chinese investors indeed engage in non-fundamental trading. Mei et al. (2005) analyze domestic investors' trading activity, building on a model where heterogeneous beliefs and short-sale constraints imply a positive relationship between a speculative stock price component and trading volume. Their empirical tests confirm that uninformed trading by domestic investors has an impact on bubbles in A-share prices. This is in line with conclusions from a recent paper by Fong (2008) . He shows that A-share returns stochastically dominate B-share returns, which leads to a rejection of risk-based explanations of the A-share premium.
Introduction
Furthermore, a number of studies provide evidence of herding and trend-chasing by domestic Chinese investors. Measures of return dispersion analyzed by Tan et al. (2008) indicate herding in both Shanghai and Shenzhen markets during the 1994 -2003 period.
Introduction
Investors in A-share markets appear to herd in response to positive market returns, as well as high levels of trading volume and volatility. A recent contribution by Li et al. (2009) comparing volume-based herding measures for institutional and individual investors trading in the Shanghai A-share market arrives at similar conclusions. The authors also find that institutions herd more than do private investors and that institutional (individual) herding occurs primarily in small (large) stocks.
Introduction
In related work studying the relationship between institutional transactions and volatility, Li and Wang (2008) argue that institutional investors have stabilized Chinese stock markets. They show that institutional investors engage in herding but not in momentum trading. However, their dataset only covers constituents of the Shanghai 180 index and does not distinguish between transactions of foreign and domestic institutions. Kling and Gao (2008) construct an index of Chinese institutional investors' sentiment. They present evidence that domestic institutional investor sentiment is driven by previous market returns, implying a positive feedback process. Finally, the study by Chen et al. (2005) looking at individual brokerage accounts finds that account holders buy stocks in response to short-term trends but care less about long-term past performance.
Introduction
Summing up, there is a large body of evidence of sentiment-driven trading by Chinese domestic investors in the A-share market. Such trading activity may cause asset prices to deviate from their fundamental values, which leads to a distortion in investment and consumption decisions and a potential misallocation of capital. It also increases the probability of stock price bubbles and subsequent crashes. In this sense, sentiment traders may threaten financial stability and put economic allenkimberly@example.net.
Introduction
At the same time, the rapid growth of the Chinese economy requires domestic firms to raise capital from abroad. Chinese regulators therefore had to open financial markets to foreign institutional investors. Moreover, foreign institutions were thought to follow rational long-term investment strategies dampening speculative activity (Walter and Howie, 2006) .
Introduction
In the public debate about financial market liberalization, however, foreign institutional investors are often alleged to exert a potentially destabilizing influence on emerging financial markets. Such concerns are partly fueled by empirical evidence that capital flows are highly sensitive to past returns (Froot et al., 2001) . In addition, a large body of literature argues that foreign institutional investors in emerging markets tend to herd and engage in positive feedback trading (Choe et al., 1999; Borensztein and Gelos, 2003; Kaminsky et al., 2004; Chen et al., 2008) . These findings suggest that financial liberalization may have unintended consequences.
Introduction
In this paper, we are interested in whether foreign investors indeed have a destabilizing effect on Chinese stock markets in terms of trend-chasing behavior. This issue is particularly relevant in the case of a transition economy that has gradually opened its financial markets to foreign capital while trying to maintain financial stability. The liberalization of A-share markets in Shanghai and Shenzhen provides us with a unique opportunity to shed light on the impact foreign institutional investors cast on the price formation process. Originally, A-share ownership was restricted to domestic investors, notorious for We analyze trend-chasing behavior before and after this regulatory regime shift within the Sentana and Wadhwani (1992) heterogeneous agents framework. By comparing parameter estimates across pre-liberalization and post-liberalization sample periods, we can draw conclusions about destabilizing feedback trading by both investor groups.
Introduction
The remainder of the text is structured as follows. Section 2 characterizes stock markets in Shanghai and Shenzhen, focusing on regulatory peculiarities. Section 3 lays out the Sentana-Wadhwani heterogeneous agents model and describes our empirical approach. A description of the dataset can be found in Section 4. Estimation results are discussed in Section 5, while Section 6 concludes.
Chinese Stock Market Regulation and Investor Structure
Since their reopening on November 26, 1990 and April 11, 1991, respectively Stock markets in Shanghai and Shenzhen were originally segmented into A and B shares according to ownership restrictions. Initially, domestic citizens could only buy and sell A shares, whereas foreign investors were only allowed to trade in B shares. Even though both categories were identical in terms of ownership rights, market capitalization of the latter segment remained low. As of December 2007, total market capitalization of A shares traded in Shanghai (Shenzhen) was about 170 (40) times the total value of B shares. Moreover, B shares typically traded at a considerable discount vis-à-vis A shares (Fernald and Rogers, 2002 (Chan and Yu (2003) for details). In what follows, our analysis focuses on the liberalization of A-share markets, which are more liquid and much larger in terms of market capitalization relative to Bshare markets.
Chinese Stock Market Regulation and Investor Structure
Given the short period of time since the reopening of Chinese exchanges, domestic public interest in stock markets is enormous. The large number of trading accounts in Shanghai and Shenzhen reveals widespread participation. In contrast to mature markets, institutional investors such as pension funds, mutual funds and insurance companies account for a marginal fraction of total stock investment (Kling and Gao, 2008) . Correspondingly, less then 1% of A-share brokerage accounts in Shanghai and Shenzhen are owned by institutional investors (Walter and Howie, 2006) . In contrast to this, the large number of private trading accounts is often interpreted as a sign of extraordinarily active stock market participation among domestic households (Li and Wang, 2008) . However, Walter and Howie (2006) argue that the largest fraction of total trading volume can be attributed to privately managed (gray-market) investment funds sponsored by wealthy individuals, notorious for their speculative activity. Comparing feedback trading before and after the abolition of ownership restrictions thus allows us to draw conclusions about the impact of foreign institutional trading on a market dominated by presumably sentiment-driven domestic investors.
Feedback Trading and Stock Return Autocorrelation
Building on earlier work by Shiller (1984) and Cutler et al. (1991) , Sentana and Wadhwani (1992) This approach has been widely used in the empirical literature on feedback trading. Applications to index returns include not only the US and the UK stock market (Sentana and Wadhwani, 1992) , but also mature European markets (Koutmos, 1997) , Japan (Watanabe, 2002) , as well as emerging markets in Asia (Koutmos and Saidi, 2001) and Eastern Europe (Bohl and Siklos, 2008) . Antoniou et al. (2005) use the model to analyze feedback trading in stock markets before and after the introduction of futures markets, arguing that feedback trading on spot markets is significantly lower after the inception of futures trading.
Feedback Trading and Stock Return Autocorrelation
In the framework of Sentana and Wadhwani (1992) , there are two types of investors.
Feedback Trading and Stock Return Autocorrelation
The first group are smart money traders who base their investment decision on risk and return considerations. Their demand for stocks can be modeled as
Feedback Trading and Stock Return Autocorrelation
where S t is the fraction of stocks they hold, E t−1 (R t ) − α is the expected excess return of stocks over the risk-free rate, and µ t measures the risk of stock holding. In what follows, we define the raw return on stocks as R t = P t /P t−1 − 1, where P t is the index value at the end of period t. In a mean-variance framework, the time-varying risk measure will be an increasing function of the conditional variance of stock returns, µ t = µ(σ 2 t ) with µ (σ 2 t ) > 0. Without loss of generality, we can follow Koutmos (1997) and assume that risk is linearly related to volatility, µ(σ 2 t ) = λ · σ 2 t . As Sentana and Wadhwani (1992) point out, market equilibrium in a model with investors of this type alone yields the familiar capital asset pricing model of Sharpe (1964) , Lintner (1965) and Mossin (1966) .
Feedback Trading and Stock Return Autocorrelation
The second group of investors are feedback traders who trade stocks in response to previous returns. Their demand for stocks is given by
Feedback Trading and Stock Return Autocorrelation
where R t−1 denotes the return in the previous period. This demand function captures both types of feedback trading. Positive (negative) feedback traders buy (sell) stocks when previous returns are positive, which implies φ > 0 (φ < 0).
Feedback Trading and Stock Return Autocorrelation
In equilibrium, markets clear so that demand from both groups sums up to 1
Feedback Trading and Stock Return Autocorrelation
Plugging in both demand functions and assuming rational expectations,
Feedback Trading and Stock Return Autocorrelation
yields an empirical model for returns given by
Feedback Trading and Stock Return Autocorrelation
Notice that in the absence of feedback traders, the stock return in period t is only affected by the first two terms, the level of the risk-free rate and the riskiness of the investment.
Feedback Trading and Stock Return Autocorrelation
This view is consistent with the CAPM. However, if the stock market is populated by both smart money and feedback traders, the contemporaneous stock return also depends on its own lagged value. The model predicts negative or positive autocorrelation of returns, depending on the sign of the parameter φ, i.e. whether feedback trading is of the positive or negative kind.
Feedback Trading and Stock Return Autocorrelation
Moreover, the model implies that the amount of serial return correlation crucially depends on the level of conditional stock return volatility σ 2 t . Sentana and Wadhwani (1992) argue that the influence of feedback trading on returns is stronger, the higher the conditional variance of returns. Intuitively, rational investors reduce their demand during periods of high volatility, whereas feedback traders are only concerned about past returns, ignoring such risks. Therefore, the market share of sentiment-driven investors and thus the influence of feedback trading on stock returns rises during periods of high volatility. The interaction between the demand of feedback traders and stock return volatility is reflected in the multiplicative term in Equation 4.
Feedback Trading and Stock Return Autocorrelation
When testing the implications of the model, we modify Equation 4 along the lines of Sentana and Wadhwani (1992) and assume that autocorrelation depends linearly on volatility. For our baseline model, the mean equation for log returns (r t = ln(R t ) = ln(P t ) − ln(P t−1 )) is given by
Feedback Trading and Stock Return Autocorrelation
Notice that a negative (positive) point estimate of φ 1 implies that the marginal effect of conditional volatility on stock return autocorrelation is negative (positive). In the SentanaWadhwani framework, this is consistent with the notion of positive (negative) feedback trading, corresponding to a positive (negative) parameter φ in the demand function of the sentiment-driven investor group. The parameter φ 0 captures serial correlation induced for instance by nonsynchronous trading (Lo and MacKinlay, 1990) , transaction costs (Mech, 1993) or time-varying expected returns (Conrad and Kaul, 1988) .
Feedback Trading and Stock Return Autocorrelation
Finally, we use a GARCH(1,1) approach to modeling conditional volatility of log returns
Feedback Trading and Stock Return Autocorrelation
Our analysis focuses on feedback trading by domestic (mainly individual) versus foreign institutional investors, that is a shift in the parameter φ 1 . In order to formally test the hypothesis that this change is statistically significant, we follow Antoniou et al. (2005) and estimate the following variant of Equation 5a
Feedback Trading and Stock Return Autocorrelation
where D t denotes a dummy variable that takes on the value of one after the liberalization date of the A-share market. We then test the hypotheses H 0,1 : φ 0,1 = φ 0,2 and H 0,2 : φ 1,1 = φ 1,2 .
Feedback Trading and Stock Return Autocorrelation
In addition to the baseline model given by Equations 5b and 6, we consider two modifications. First, following Sentana and Wadhwani (1992) , we investigate whether positive feedback trading is more intense during market downturns. The modified mean equation for the asymmetric model is given by
Feedback Trading and Stock Return Autocorrelation
which implies that the composite coefficient on lagged returns is
Feedback Trading and Stock Return Autocorrelation
A significantly positive (negative) point estimate of φ 2 indicates that return autocorrelation is more negative (positive) during bear (bull) markets, which in the context of our model can be interpreted as evidence of more (less) positive feedback trading. As discussed by Sentana and Wadhwani (1992) , this view is consistent with margin traders being forced to liquidate their positions as well as time-varying risk aversion in response to negative changes in total wealth.
Feedback Trading and Stock Return Autocorrelation
Second, we consider a modified model where serial correlation is an exponential function of conditional volatility as suggested by LeBaron (1992) . The mean equation for this exponential model is
Feedback Trading and Stock Return Autocorrelation
whereσ is the unconditional variance of log returns, which in empirical applications is replaced by its sample analogue. Our heterogeneous agents model predicts that, if feedback trading is of the positive kind, the parameter estimate of φ 1 should carry a positive sign, as opposed to the expected negative value for φ 1 in the baseline model. Watanabe (2002) estimates this variant of the Shiller-Sentana-Wadhwani model for Japanese stock index returns and finds a better empirical fit than for the baseline model.
Data
Daily observations for the value-weighted Shanghai and Shenzhen A-share indices are collected from Thomson Financial Datastream. Assuming continuous compounding, daily returns are computed as the difference in logs between closing values, r t = ln(P t ) − ln(P t−1 ).
Data
We choose the sample period to be symmetric around the liberalization date. It covers the period between January 2, 1997 and December 28, 2007. This also excludes observations from the early days of both stock exchanges, which suffer from poor data quality due to infrequent trading (Fong, 2008) . We divide our total sample into subsamples before and after We expect the A-share market entrance of QFIIs during the post-liberalization period to affect the return dynamics of large and small stocks differently as empirical evidence suggests that institutional trend-chasing and herding mainly occurs in stocks with low market capitalization (Lakonishok et al., 1992; Li et al., 2009) . Therefore, we construct size portfolios for the A-share segment of each market. We obtain daily data on close prices and market capitalization of A-share stocks listed in Shanghai and Shenzhen. Our portfolio formation procedure is as follows: On the last trading day in a given year j − 1, we rank stocks traded for example in the Shanghai A-share segment according to their market capitalization on that particular day. Based on this ranking, we assign all stocks to quintiles for the following year j. If trading in a certain stock is discontinued during year j, the composition of the respective size portfolio is rebalanced at the beginning of year j + 1. Finally, we calculate value and equally-weighted returns for each portfolio on a given day t.
Data
Summary statistics for index and value-weighted portfolio returns (not shown) suggest that all time series exhibit moderate negative skewness and considerable excess kurtosis.
Data
Comparing sample means across the five portfolios, we find that daily log returns are highest (lowest) for stocks in the low (high) market capitalization quintile.
Empirical Results
We estimate the empirical models outlined in Section 3 by quasi maximum likelihood assuming normally distributed errors. The parameter estimates for all structural coefficients in Equations 5b and 6 can be determined directly using constrained maximization methods implemented under RATS 6.2. Since summary statistics discussed in Section 4 suggest that all stock return time series exhibit excess kurtosis, we correct standard errors for potential non-normality as suggested by Bollerslev and Wooldridge (1992) . Both Akaike and Schwarz information criteria favor the parsimonious GARCH(1,1) specification over higher order alternatives. indices. We start our discussion by looking at parameter estimates for the volatility equation. Both volatility clustering (β 0 ) and GARCH effects (β 1 ) are highly significant. The stationarity condition β 0 + β 1 < 1 is fulfilled. The intercept ω is statistically different from zero at conventional levels of significance in all cases.
Empirical Results
[Insert Table 1 about here]
Empirical Results
Turning to the mean equation, we find that point estimates for λ are significant in 4 out of 6 cases. This implies that the level of returns on A shares is affected by conditional volatility as a CAPM-type model would suggest. Estimates are only insignificant for the post-liberalization period. However, this finding is not uncommon in empirical tests of the Shiller-Sentana-Wadhwani model (Koutmos, 1997; Bohl and Siklos, 2008) or of GARCH-inMean models of stock returns in general (Nelson, 1991) . For both markets and all sample periods, the intercept of the mean equation is negative and significantly different from zero in most cases. In our model, this parameter carries the interpretation of the daily risk-free interest rate. For various reasons, it appears plausible that the risk-free rate perceived by domestic investors may be exceptionally low or even negative in the case of China. Fernald and Rogers (2002) highlight the fact that the Chinese government has traditionally set interest rates on bank deposits and saving bonds close to zero or even negative in real terms, which amounts to an effective taxation of capital. They argue that such a lack of investment alternatives implies that a CAPM-type representative investor may require very low minimum returns.
Empirical Results
In this paper, we are primarily interested in whether foreign institutions entering the A-share market after liberalization have sided with feedback traders. In fact, our results seem to suggest the opposite. For both Shanghai and Shenzhen, we obtain for the pre- We further investigate the trend chasing behavior of both investor groups along 5 lines.
Empirical Results
First, we test whether the change in the parameters of interest is statistically significant following the procedure of Antoniou et al. (2005) . Estimation results for the modified baseline model including a post-liberalization dummy (Equations 6 and 7) are reported in Table 2 . In line with the results for subsamples in Table 1 , we find that the parameter estimate of φ 1 is negative and significant during the period when only domestic investors could trade in A shares and insignificant afterward. In addition, we can reject the hypothesis of equal coefficients at the 1% level of significance. This corroborates our conclusion that most Chinese individual investors have engaged in feedback trading during the earlier sample period whereas foreign investors seem to have sided with rational investors who base their decision on risk-return considerations.
Empirical Results
[Insert Table 2 about here] Second, using the date when A-share ownership restrictions where officially abolished to divide the sample may appear somewhat arbitrary. The presumed structural change in the return generating process may have occurred at a later point as institutional traders entered the market during the post-liberalization period. Therefore, we explore alternative sample splits by means of standard structural break detection methods in the spirit of earlier literature (Chiang, 1988) . Specifically, we compute the cumulative sum (CUSUM) of recursive standardized residuals, which serves as a qualitative indicator of structural change in our model (Harvey, 1981) . As can be seen from Figure 1 , the CUSUM gradually increases after the market entrance of QFIIs. This is in line with the notion of an increasing fraction of institutional trading causing a break in the structural parameters of our model.
Empirical Results
We identify peaks in CUSUM and the liberalization date as potential switching points and employ Chow tests to formally test their significance. Table 3 [Insert Figure 1 and Table 3 (Fong, 2008; Ma and McCauley, 2002) . This implies that a significant feedback trading parameter for this sample period may be due to the influence of domestic traders circumventing ownership restrictions. Estimation results for the pre-liberalization period should therefore be interpreted with caution. As a matter of fact, we obtain a preliberalization point estimate of φ 1 that is significant at the 5% level for Shenzhen, which is the smaller market in terms of market capitalization and trading volume. However, the parameter estimate is highly insignificant in the case of Shanghai. Thus, as expected, we find only weak if any evidence of feedback trading before the liberalization date. By contrast, there is strong evidence of positive feedback trading after the market entrance of domestic traders. In line with our initial hypothesis, the estimate of φ 1 is statistically different from zero at the 1% level of significance for both markets. Also, the coefficient is considerable larger in magnitude compared to the earlier period. Summing up, estimation results for B-share returns support our main conclusions.
Empirical Results
[Insert Table 4 about here]
Empirical Results
Fourth, we estimate the Sentana-Wadhwani model for value-weighted returns on individual size portfolios, ranging from 1 (low market capitalization) to 5 (high market cap-italization). Tables 5 and 6 [Insert Tables 5 and 6 about here]
Empirical Results
Finally, we investigate whether feedback trading occurs primarily during market declines.
Empirical Results
We estimate the asymmetric model given by Equations 6 and 8 for the total sample period.
Empirical Results
In the case of Shanghai (Shenzhen), the point estimate for the additional parameter φ 2 (not shown) is positive and significant at the 5% (1%) level. As detailed in Section 3, we can infer that serial correlation in stock returns is more negative (positive) after a price decrease (increase). The influence of positive feedback trading appears even stronger during bear markets.
Empirical Results
The same picture emerges when looking at a plot that compares conditional return autocorrelation implied by the baseline model (calculated as ρ impl t =φ 0 +φ 1 σ 2 t ) to the value of the A-share index in Figure 2 . Implied serial correlation is most negative during market downturns, as for instance during the Asian Financial Crisis of 1997 /1998 . Sentana and Wadhwani (1992 suggest that such a relationship can be explained by models where risk-aversion declines strongly with wealth, making portfolio insurance strategies rational. Alternatively, they conjecture that margin traders, who are forced to sell their positions after prices have fallen, contribute to positive feedback trading during such periods. Although Chinese authorities have only recently officially legalized margin trading (Wall Street Journal, October 6, 2008) , there is evidence that domestic investors have engaged in leveraged stock trading in the past (Girardin and Liu, 2005) . This makes the margin trading interpretation of our results quite plausible.
Empirical Results
[Insert Figure 2 about here]
Empirical Results
To further verify the robustness of our empirical results, we conduct additional tests where the empirical approach is modified in several respects. In additional estimations of the baseline model, we explore the effect of extending the sample period to 1994 -2007.
Empirical Results
Empirical results (not shown) confirm our main conclusions on feedback trading by foreign institutional and domestic investors. Moreover, as discussed in Section 3, serial correlation of stock returns can be modeled as an exponential function of conditional volatility. Estimation results, which are available from the authors on request, are similar across both exchanges. For the pre-liberalization period, the effect of the exponential transformation of volatility on return autocorrelation is significantly positive (φ 1 > 0) whereas the point estimate for φ 0 is negative and significant at the 5% level. At the same time, the corresponding parameter estimates for the post-liberalization period are not statistically different from 0 at conventional levels of significance. This result lends further support to our conclusion that QFIIs entering the Chinese A-share market after December 1, 2002 have reduced the proportion of feedback traders in this segment and thus stabilized the market.
Conclusions
The emerging stock markets of China are among the fastest growing in the world. A large body of literature provides evidence that sentiment-driven domestic investors play an important role in these markets. In this paper, we consider a natural experiment, namely the ownership liberalization of domestic A-share markets in December 2002. This regulatory regime shift allows us to investigate the impact of foreign institutional investors on the price formation process. Our analysis focuses on the relative propensity of domestic individual versus foreign institutional investors to follow trend-chasing trading strategies. Previous research has mainly documented destabilizing feedback trading by foreign institutions in emerging markets (Choe et al., 1999; Kaminsky et al., 2004; Chen et al., 2008) . Our empirical results suggest the opposite. We find strong evidence of positive feedback trading before liberalization whereas such effects disappear thereafter. The observed parameter shift is statistically significant and robust to variations in the empirical model.
Conclusions
The conclusions are twofold. First, we can infer that domestic investors driving the market in the earlier period followed trend-chasing trading strategies. This empirical result lends support to anecdotal evidence comparing Chinese stock markets to casinos. More importantly, it is in line with previous empirical work suggesting speculative investment behavior of Chinese individual traders (Mei et al., 2005; Fong, 2008) as well as a strong relationship between private investors' stock purchases and short-term past performance (Chen et al., 2005) . Moreover, our findings confirm previous evidence that individual investors engage in positive feedback trading (Bange, 2000) . We also find that trend-chasing behavior is more dominant during market declines. This is consistent with the notion of intensified positive feedback trading by margin traders who are forced to liquidate their position after market declines. Unfortunately, because margin trading has been officially legalized only recently, we do not have data necessary to further investigate this issue. Second, our findings have regulatory implications. Sentiment-driven investor behavior is of concern for security market regulators since positive feedback trading together with rational investors' bandwagoning can destabilize financial markets (De Long et al., 1990b) .
Conclusions
Contrary to conventional wisdom and our initial hypothesis, foreign institutional investors entering the market after November 2002 did not engage in feedback trading. By contrast, the influence of trend-chasing on A-share markets in Shanghai and Shenzhen has diminished after the market entrance of foreign investors. We can therefore conclude that market liberalization has reduced the probability of speculative bubbles in stock prices and thus contributed to a sustainable development of Chinese stock markets. The variance equation is given by
Conclusions
where rt denotes the log return (multiplied by 100) on the Shanghai and alternatively Shenzhen A-share index. P-values are based on robust Bollerslev and Wooldridge (1992) standard errors. *,** ,*** denote statistical significance at the 10%, 5% and 1% level, respectively. The total sample period is January 2, 1997 -December 28, 2007. The 'pre' subsample ends on November 29, 2002, which is the last trading day before ownership liberalization. The 'post' subperiod begins on December 2, 2002. 
Conclusions
The variance equation is given by σ
Conclusions
where rt denotes the log return (multiplied by 100) on the Shanghai and alternatively Shenzhen A-share index. Dt is a dummy variable that takes on the value of 1 after December 1, 2002. P-values are based on robust Bollerslev and Wooldridge (1992) standard errors. *,** ,*** denote statistical significance at the 10%, 5% and 1% level, respectively. The sample period is January 2, 1997 -December 28, 2007. 
Conclusions
The variance equation is given by
Conclusions
where rt denotes the log return (multiplied by 100) on the Shanghai and alternatively Shenzhen B-share index. P-values are based on robust Bollerslev and Wooldridge (1992) standard errors. *,** ,*** denote statistical significance at the 10%, 5% and 1% level, respectively. The total sample period is January 2, 1996 -December 29, 2006. The sample is split on February 28, 2001, which marks the B-share liberalization date. where rt denotes the value-weighted log return (multiplied by 100) on portfolio i. Stocks are sorted into 5 portfolios according to market capitalization at the last trading day of the previous year, ranking from 1 (low market cap) to 5 (high market cap). P-values are based on robust Bollerslev and Wooldridge (1992) standard errors. *,** ,*** denote statistical significance at the 10%, 5% and 1% level, respectively. The total sample period is January 2, 1997 -December 28, 2007. The 'pre' subsample ends on November 29, 2002, which is the last trading day before ownership liberalization. The 'post' subperiod begins on December 2, 2002. where rt denotes the value-weighted log return (multiplied by 100) on portfolio i. Stocks are sorted into 5 portfolios according to market capitalization at the last trading day of the previous year, ranking from 1 (low market cap) to 5 (high market cap). P-values are based on robust Bollerslev and Wooldridge (1992) standard errors. *,** ,*** denote statistical significance at the 10%, 5% and 1% level, respectively. The total sample period is January 2, 1997 -December 28 

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