股市特质风险与股票收益率相关关系的实证研究

An empirical investigation on the relationship between the idiosyncratic risk and the stock market return

本文将股票特质风险以系统性风险因子形式引入资产定价过程,实证检验了中国股票市场特质波动率与股票收益率的关系。本文按照股票特质波动率的大小构造投资组合,将高特质波动率组合的加权平均收益率与低特质波动率组合的加权平均收益率之差作为特质风险因子IVF。实证结果表明:(1)特质风险因子对股票市场有一定的预测作用。特质风险因子越高,预期股票市场收益率越高;高波动市场环境下的预期股票市场波动率也越高。(2)特质风险因子可以解释股票收益的截面差异。股票收益与特质风险因子IVF之间的相关性越大,投资者要求的风险溢酬越高,股票期望收益率就越高。

Based on traditional asset pricing models, idiosyncratic risks can be well-diversified through portfolio selection, and only systematic risk is priced.However, idiosyncratic risks might not be fully diversified as there are frictions and imperfect information in the market. Researchers have different views onthe effect of idiosyncratic risks on stock returns, depending on model specifications for measuring idiosyncratic risks as well as sample data. In this paper, weexamine the relationship between idiosyncratic risks and stock market return by incorporating idiosyncratic risks as a source of systematic risks un-captured bybeta coefficients in China＇s stock market. First, we estimate idiosyncratic volatility as a standard deviation of daily residuals in the regression timessquare root of number of days based on the Fama-French by employing a sample data from Shanghai Stock Exchange for the period from 3 January 1996 to 31 December 2011. We construct different portfoliosbased on idiosyncratic volatility and use the difference of the weigh average rate of return of the portfolio with high volatility and that of the portfolio with lowvolatility as a systematic risk-based factor IVF. We allocate stocks into two portfolios by size based on whether their month of end market equity is above orbelow the median market equity. Further, we construct six size-idiosyncratic volatility portfolios with two dimensions of size and idiosyncratic volatility. Second, we examine the ability of systematic idiosyncratic risk factors in predicting future market return and future market variance by utilizing a regimeswitching model. We find that IVF can be used to forecast future market return or volatility. The idiosyncratic risk-based factor is positively correlated to stockmarket return, and is also positively correlated to stock market variance in the high variance regime. Third, we examine to what extent cross-sectional differences in return for portfolio sorted by idiosyncratic volatility and size can be explained bydifferences in exposure to the computed systematic idiosyncratic risk factor. Betas are first estimated using the variance-covariance method with a rollingwindow of 24 months. Afterwards, we examine the extent to which exposure to the systematic idiosyncratic risk factor is priced in a cross section of portfoliosvia cross-sectional regressions of asset returns against estimated betas. We find that IVF can be used as a risk factor in the cross section. The higher thecorrelation between stock returns and 1VF, the higher the risk premium demanded by the investors. The sensitivity of investors to the idiosyncratic risk factor ishigher for the high idiosyncratic volatility portfolio.