China's Stock Market Trading And Non-trading Time Volatility Study

In the past ten years, China stock market developed rapidly and attracted worldwide attention. However, comparing to foreign ripe exchange market, China stock market exhibits high-risky features such as abnormal magnitude of trade, high change of stock, high frequency and amplitude of stock price volatility and so on. Volatility is a normality trait of stock market, but it means the gather of risk when the frequency and amplitude exceed certain demarcation. This paper studies the feature and reason of stock price volatility in combination with Behavioral Finance and is helpful to comprehend investors' behavior. Moreover it is significant of their investing decision-making and the development of China stock market.This paper studies first the trading and non-trading time return variances effect in China stock market. The sample is the daily returns from January 1 of 1994 to December 29. The results show that the variances of four weekdays returns is 5.06 times weekday return variances in Shanghai stock market and 4.97 in Shenzhen stock market. However, table 2 shows that the variance for a three-day weekend returns is only 39.9% higher than the variance for a normal one-day return in Shanghai stock market and 52.6% in Shenzhen stock market. The empirical results show that stock returns are more volatile during exchange trading hours than during non-trading hours. French and Roll consider three possible explanations for the observed variance pattern.— high trading-time volatility is caused by public information which is more likely to be observed during normal business hours;— high trading-time volatility is caused by private information which is more likely to affect prices when the exchanges are open;— high trading-time volatility is caused by pricing errors that occur during trading. This paper tests by examining the autocorrelations of the daily returns. Neither public information nor private information will generate significant serial correlation,while the stock returns should be serially correlated under the trading noise hypothesis. Consequently, it tests the autocorrelations of stock returns and examines the actual-to-implied variance ratios for holding periods. The study of autocorrelations in Shanghai stock market and Shenzhen stock market supports the trading noise hypothesis. However, for the average autocorrelations are small in absolute magnitude, it is hard to gauge their economic significance. To estimate the importance of the trading noise hypothesis, this paper compares daily return variances with return variances for longer holding periods. It indicates that pricing error is one of the reasons on high trading-time return variances in China stock market. Approximately 5～27% of the daily variance is caused by mispricing.This paper then adopts the vector autoregression (VAR) method to study the fraction of the variation in stock returns that can be attributed to various types of macroeconomic news. It uses monthly macroeconomic news and indices from January 1996 to August 2002. The empirical results show that macroeconomic news can only explain about 30% of the variance in stock returns, and all the variables appear to have a less significant effect on stock returns. The empirical analysis from macroscopic policies shows that policies appear to have a significant effect on China stock market. It can increase the volatility range of market when a policy is promulgated.