Uncovering The China’s Stock Market Variance Prediction

Including 25 potential variables we present a comprehensive study on China’s stock market variance prediction with sample from 1995 to 2018. Contrary to many previous studies, we find most economic activity variables have neglected forecasting power and scaled price ratio have a nonlinear correlation with future variance. Based on the evidence provided by the Bayesian model averaging and the out of sample test, the stock market turnover provides appropriate additional information while illiquidity proposed by Pástor and Stambaugh (2003) has a strong prediction ability in the quarterly data. JEL classification: G12; C22 Keyword: China’s stock market; Time-Varying Stock Market Variance; Conditional Variance; Realized Variance; Bayesian model averaging; Out-Of-Sample ∗Cheng is at the Research Center of Applied Finance, Dongbei University of Finance and Economics; Email: chenghangch@qq.com. Guo is at the Department of Finance and Real Estate, University of Cincinnati; Email: hui.guo@uc.edu. Shi is at the Research Center of Applied Finance, Dongbei University of Finance and Economics; Email: ydshi@263.net. We thank Xiaoman Li, Mingyong Song, Chao Wang, Jinle Wang, Sanfa Wang, Tongtong Wang, Shijie Zheng and seminar participants at the University of Dongbei University of Finance and Economics. Shi acknowledges the financial support of the Fundamental Research Funds for the National Natural Science Foundation of China [Grant Nos. 71471031, 71772030, 71702025], the major project of the National Social Science Foundation of China [Grant No. 14AZD089], Distinguished Professor Support Plan of Liaoning Province [Grant No. [2018]35]. Stock market volatility has a pivotal role in academic research on finance, in particular, it is critical to understanding the time seires pattern of the stock market return. In Merton (1973)’s ICAPM model, market risk, also known as conditional market volatility is important determinants of expected stock market return. However, a major problem with the conditional market volatility is it can not be observed directly so subsequent studies use some potential economic variables to predict volatility. Literature suggests that economic activity is among the most important factors for stock market volatility prediction. After Schwert (1989) finds little correlation between economic activity and stock market volatility, scholars use different economic variables and econometric methods to reach different conclusions. A recent study by Paye (2012) involved, they use abundant macroeconomic and financial variables to investigate the stock market volatility forecasting. Similar with Schwert (1989)’s finding, they argue that lagged volatility covers a lot of future economic condition which means the predicted benefits are small when additional economic activity variables are included. Nonejad (2017) investigates whether information from financial and macroeconomic variables is helpful in predicting volatility in a comprehensive Bayesian model averaging framework. Wang, Wei, Wu, and Yin (2018) mention that crude oil volatility can predictive stock volatility and provides different information from traditional macro variables. In this paper we attempt to uncover the predictability of China’s stock market volatility. Chen, Jiang, Li, and Xu (2016) draws our attention to U.S. economic variables which can forecast the future monthly volatilities of the Chinese stock market. The study by Cai, Chen, Hong, and Jiang (2017) offers probably the most comprehensive empirical analysis of China’s stock market variance prediction. To be specific, they find some of the 13 variables, such as, dividend-price ratio, inflation, turnover and changes in the M1 money supply positively and significantly forecast the Chinese stock market volatilities. Perhaps the most serious disadvantage of their research is that the sample spans from January 1997 to December 2012. Based on the framework of their research, we employ 25 potential economic variables commonly used in the literature and extend the sample, from January 1995 to December 2018. To investigate the stable relation between potential economic variables and stock market volatility, two sub-samples results in monthly and quarterly data frequency are also reported, from 1995 to 2007, and from 2008 to 2018, respectively. Following Nonejad (2017), we use Bayesian model averaging to find the most appropriate predictor in the 25 potential economic variables. When the auto-correlation of stock returns caused by artificial market mechanism is high, the original calculation method of volatility underestimates the real volatility. Since December 26 of 1996, the Chinese stock market has imposed a daily price limit of 10%, we highly emphasize the impact of the original calculation on market volatility. Adjusted