Comparative Study Of Asset Pricing Models Based On Skewness

Traditional capital asset pricing theory generally assumes that asset returns follow a symmetric normal distribution and that the volatility of returns is measured by variance.However,in reality,the distribution of stock market returns often exhibits asymmetric characteristics.Therefore,it is necessary to consider the impact of skewness in asset pricing.Currently,scholars have proposed different ways to measure skewness and have conducted extensive research on the relationship between skewness and expected stock returns,with the main focus being on systematic skewness and idiosyncratic skewness.Based on the A-share market in China,scholars have found that systematic skewness has a significant risk premium,and idiosyncratic skewness also affects the equilibrium price of stocks,with a negative correlation between expected idiosyncratic skewness and individual stock returns.According to scholars,the direct use of historical variables as a proxy for ex-post skewness generates some error.Therefore,in addition to the expected idiosyncratic skewness of individual stocks,it is also necessary to conduct research on the predictive systematic skewness of individual stocks in order to fill the gaps in existing research.Therefore,the main focus of this paper is on the prediction and pricing of systematic skewness.Its significance lies in providing asset allocation references for Chinese investors by studying the relationship between individual stock systematic skewness and asset returns,and also contributing to maintaining stability in the Chinese stock market.In addition,the study of expected systematic skewness for individual stocks enriches relevant asset pricing theories and provides new ideas and references for skewness prediction.Firstly,based on Langlois’(2020)research method and combined with the skewness characteristics of the A-share market in China,this paper constructs a model for predicting individual stock expected systematic skewness that is suitable for the A-share market in China.After obtaining the time series of predictive systematic skewness for individual stocks,this paper uses univariate and bivariate portfolio analysis methods and finds that investment portfolios with lower expected systematic skewness have higher expected returns,which is theoretically consistent with ex-ante systematic skewness.Secondly,after discovering the negative correlation between expected systematic skewness and portfolio returns,this paper compares the difference in explanatory power of the constructed ex-post expected systematic skewness factor and the ex-ante systematic skewness factor on excess stock returns.Specifically,this paper refers to the methods of Fama-French(1993)and Novy-Marx(2013),constructs relevant factors through long-short portfolios,and builds two five-factor models based on the above systematic skewness.The results show that the predictive systematic skewness factor is an important explanatory variable for excess stock returns,and its explanatory power is superior to the ex-ante covariation skewness factor.Lastly,further analysis based on GRS results shows that the five-factor model incorporating predictive systematic skewness performs better than other models.In summary,this paper innovatively proposes a method for predicting individual stock systematic skewness based on the A-share market in China,introduces the predictive systematic skewness factor into the four-factor model,and finds that this factor is superior to the systematic skewness factor.The research in this paper enriches the effective factor library suitable for the Chinese market,has theoretical significance for improving the applicability of factor models in the Ashare market in China,and also enriches the stock selection strategies for Chinese investors.