خوشه نوسانات و روزهای غیر تجاری در بازار بورس چینی

In this paper we analyze volatility dynamics in the Chinese stock markets comparing the EGARCH with the GJR GARCH model. The empirical results, which are quite stable under the alternative specifications, reflect the different dynamics due to the market segmentation in domestic A-shares and foreign B-shares. For the daily returns on A-shares we find there is highly significant impact of the number of nontrading days on volatility, as well as a significant reduction of volatility by introducing the price change limit. The evidence varies more mixed for the B-shares. For the analysis of the impact of news on volatility we propose a modification of the news impact curve. Using the concept of a conditional news impact curve we show that in periods of high volatility there is a potential acceleration of the news impact in the GJR GARCH model, while the news impact remains invariant under the EGARCH approach. The theoretical comparison is confirmed by the empirical results.

In this paper we analyze volatility dynamics in the Chinese stock markets comparing the EGARCH with the GJR GARCH model. The empirical results, which are quite stable under the alternative specifications, reflect the different dynamics due to the market segmentation in domestic A-shares and foreign B-shares. For the daily returns on A-shares we find there is highly significant impact of the number of nontrading days on volatility, as well as a significant reduction of volatility by introducing the price change limit. The evidence varies more mixed for the B-shares. For the analysis of the impact of news on volatility we propose a modification of the news impact curve. Using the concept of a conditional news impact curve we show that in periods of high volatility there is a potential acceleration of the news impact in the GJR GARCH model, while the news impact remains invariant under the EGARCH approach. The theoretical comparison is confirmed by the empirical results.

In this paper, we analyze volatility clustering in the Chinese stock markets comparing the EGARCH with the GJR GARCH models with the GED innovation. We find that the two approaches perform quite similarly. The market segmentation in the domestic A-shares and the foreign B-shares is reflected in the dynamics of the conditional mean and the conditional variance. The impact of the number of nontrading days on volatility appears to be significant for the A-shares and Composite indices, but not for the B-share indices. The introduction of price change limits is shown to have opposite effects on the return volatility of the domestic shares as compared to the B-shares. Regarding the A-share indices and the Composite indices, the reintroduction of a daily price change limit in December 1996 was successful in reducing the deterministic volatility component significantly. In case of the B-share indices, however, we find a significant upward shift in the volatility level for the period after introducing the price change limit. With respect to the asymmetric reaction of the predicted volatility to good and bad news, we find that there exsits a stronger volatility response to bad news for the A-share indices and the Composite indices, although the asymmetry is significant at the 5% level only for the Shanghai A-index. This does not hold, however, for the B-share indices. There is an adverse asymmetric reaction with good news increasing the volatility more than bad news in Shenzhen B-shares as documented by Yeh and Lee (1997). We also find that the distribution is unusually fat-tailed and significantly different from the normal distribution in all cases, which is consistent with the findings of Su and Fleisher (1998). The application of an EGARCH model was motivated by a theoretical comparison of the impact of news on volatility in different types of ARCH models. We introduced the concept of a CNI curve and showed that while for EGARCH the news impact does not depend on the volatility environment, the GARCH model, either in its standard version or in its asymmetric modification by Glosten, Jagannathan, and Runkle (1993), displays an acceleration of the news impact in periods of high volatility, thereby creating a potential for overshooting volatility predictions. We found that, depending on the parameter values, the EGARCH can well be at least as robust in periods of high volatility as the GJR GARCH model. In fact, using the basic variance decomposition used by Engle and Ng, our empirical analysis does not provide any evidence that the EGARCH model overstates the predicted volatility. Thus our application of the EGARCH and GJR model to the Chinese stock market does not confirm that the EGARCH model is inferior to the GJR model.