Stock Returns, Volatility, and Trading Volume: Evidence from the Chinese Stock Markets

ABSTRACT This paper examines three important issues in Chinese stock markets. First, we examine the behavior of stock returns, volatility, and trading volume in Chinese stock markets. Second, we investigate the contemporaneous and causality relationship among stock returns, volatility, and trading volume at the Shanghai and Shenzhen Stock Exchanges over the period from the beginning of the Shanghai stock market in December 1990 (April 1991 for the Shenzhen stock market) to June 1999. And lastly, we examine the linkage between the Shanghai Stock Exchange and the Shenzhen Stock Exchange. We first find that monthly return volatility and trading volume volatility exhibit strong autocorrelation. However, further empirical tests reject the existence of unit roots. Monthly returns do not exhibit autocorrelation for the Shanghai Stock Exchange Index. With a three variable autoregressive (VAR) model, we then find that return volatility affects stock returns at both Chinese stock markets. There exists a bi-directional causality between return volatility and trading volume volatility. Interestingly, we further find that returns do not cause return volatility directly at both Chinese stock markets, but instead affect trading volume volatility at the Shanghai Stock Exchange. Finally, we find a strong linkage between the Shanghai Stock Exchange and the Shenzhen Stock Exchange in terms of returns, return volatility, and volume volatility. JEL: G12, G15 Keywords: VAR model; Causality; Linkage in stock markets I. INTRODUCTION Previous research has shown that an individual firm's stock return volatility rises after stock prices fall. Two popular explanations of this finding are the leverage effect and time-varying risk premiums. The leverage effect predicts that a decrease in a firm's stock price reduces the value of equity, and therefore, increases the debt ratio of the firm. As a result, the risk associated with the firm increases, causing higher stock return volatility. The time-varying risk premium argues that an expected increase in stock return volatility increases the risk of holding the stock. To compensate for the additional risk, investors require a higher expected risk premium. As a consequence, we should observe an immediate stock price decline. If price and quantity are two fundamentals in a financial demand and supply system, then the importance of trading volume and its information content should not be ignored when we study the financial market interactions. Unfortunately most research has focused on the behavior and relationship between returns and return volatility, while less study has been done on trading volume volatility. The relationship among returns, return volatility, and trading volume volatility has received far less attention. This paper examines three important issues related to the relationship among stock returns, volatility, and trading volume in Chinese stock markets. First, we examine the behavior of stock returns, volatility, and trading volume. Second, we investigate the contemporaneous and causality relationship among stock returns, volatility, and trading volume at the Shanghai and Shenzhen Stock Exchanges over the period from the beginning of these markets (from December 1990 for the Shanghai Stock Exchange and from April 1991 for the Shenzhen Stock Exchange) to June 1999. Lastly, we examine the linkage between the Shanghai Stock Exchange and the Shenzhen Stock Exchange. We first find that monthly return volatility and trading volume volatility exhibit strong autocorrelation. However, further empirical tests reject the existence of unit roots. Monthly returns do not exhibit autocorrelation for the Shanghai Stock Exchange Index. In contrast with the previous findings with the U.S. data, we find a strongly positive contemporaneous relationship between returns and volatility for both Chinese stock markets. With a three variable VAR model, we further discover that return volatility affects stock returns at both Chinese stock markets. …