An Empirical Study About The Influence Of Consumption Volatility Risk And Idiosyncratic Volatility Risk On Asset Pricing In Chinese A-Share Market

Asset pricing theory is one of the core research fields in finance, which can help the investors make better decision in risk management and asset allocation. Among all the asset pricing models, CAPM, Fama-French three-factor model and Carhart four-factor model are most well-known and widely-used in theoretical research and empirical analysis. Based on those classical models, the researchers are still working hard on every possible pricing factor to build a more complete asset pricing model. This paper mainly bases on the following two aspects to accomplish the study:The first one is about the influence of consumption volatility risk on asset pricing. Consumption is a decisive factor in national economic growth, whose volatility will cause the variation of macro economy and result in the effects on stock return. With the Markov regime switching model, Boguth-Kuehn (2013) extracts the consumption volatility risk from American consumption data and empirically verifies its significant pricing power. But in the domestic research, the scholars still haven’t done any study on the pricing power of consumption volatility risk. This paper uses the analyzing method of Boguth-Kuehn (2013) with the Chinese consumption data from 2000 to 2013 and finds out that the comparatively short-period consumption growth data in China is quite smooth and the Markov regime switching model might be not appropriate to extract the consumption volatility risk factor in China.The second one is about the influence of consumption volatility risk on asset pricing. The classical asset pricing theory assumes the market to be perfect and fnctionless and only the systematic risk will be priced under this assumption. But in reality, especially for the stock market in China, due to the immaturity of the market, the investors can’t fully diversify their unsystematic risk (idiosyncratic risk), the unsystematic risk needs to be taken into consideration in asset pricing. In this paper, the daily and monthly return data of A-share stocks from Jan.1st 1997 to Dec.31th 2013 was used to measure the idiosyncratic volatility of individual stocks with the method proposed by Ang-Hodrick-Xing-Zhang (2006). Then, the idiosyncratic volatility risk factor is formed and added into the existing models like CAPM, Fama-French three-factor model and Carhart four-factor model. The result shows that the idiosyncratic volatility and return are negatively correlated with high statistical significance even after controlling for the characteristics like size, book-to-market ratio and momentum effect for the individual companies. Compared with other risk factors, the idiosyncratic volatility risk factor is with the highest Sharpe ratio and only a small proportion of its variation can be explained by other risk factors. After adding the idiosyncratic volatility risk factor, the R2 measure for the models all increased a lot. Besides, the estimated risk prices for the idiosyncratic volatility risk factor in all the models are significantly negative. Further on, this paper computes the time series properties of the idiosyncratic volatility for the individual stocks and the expected idiosyncratic volatility is estimated based on the measurement method proposed by Fu (2009). The result suggests that it is not appropriate to describe an idiosyncratic volatility process as a random walk, and the significant negative correlation is also found between the expected idiosyncratic volatility and return. But the correlation seems to be less significant compared with the correlation between idiosyncratic volatility and return.From the research results, this paper finds out that Chinese A-share market has something in common with the mature markets abroad as well as something particularity. Similar with the stock market in America, the idiosyncratic volatility and return are significantly negatively correlated and the idiosyncratic volatility risk factor formed has significance pricing power in Chinese market. But from the perspective of particularity, the consumption volatility risk factor extracted from the American consumption data with the Markov regime switching model is proved to have significance pricing power. However, due to the short period and relatively too smooth consumption data, the model is not appropriate to be used to extract the consumption volatility from the Chinese consumption data.