Dynamic Implied Risk Aversion Term Structure: An Empirical Analysis Based on Shanghai Stock Exchange 50 ETF Option

Abstract

Risk preference has constantly been one of the vital issues in economics and finance. In this study, the time series and term structure of Chinese investors’ implicit risk aversion are investigated using the implicit risk aversion extraction method, and the dynamic term structure of Chinese investors’ implicit risk aversion is modeled using the Vasicek model. In the empirical process, the SSE 50 ETF option data from March 2015 to July 2018 are adopted to extract model-free risk-neutral skewness. The standard deviation, skewness, and kurtosis of reality measure are extracted using the Shanghai 50 ETF data, and then the monthly implied risk aversion time series and term structure of Chinese investors are obtained. As indicated by the results of this study, the risk preference of Chinese investors exhibits significant time series characteristics, and it will show risk-loving and risk-averse phenomena at certain times. Moreover, for different periods in the future, differences are generated in investors’ risk preferences, suggesting an aversion to short-term risk and a certain tolerance for long-term risk, i.e., there are significant characteristics of the term structure. Besides, the term structure of implied risk aversion of Chinese investors is dynamically modeled using the Vasicek model. To be specific, its first principal component can account for 90% of the change in the term structure. The “level factor” refers to the critical factor load, and the level of long-term implied risk aversion reaches 0.658. Furthermore, the term structure of implied risk aversion exhibits the characteristics of mean regression. Next, more effective research results on investors’ risk preference are achieved, and the time-varying characteristics and term structure characteristics of investors’ risk preference are investigated. The result suggests that the long-term risk preference of Chinese investors approaches 1, and there is a significant feature of “mean regression,” i.e., a vital finding of this study.