Three Essays on Empirical Asset Pricing

This dissertation contains three essays on empirical asset pricing. In the first essay, I study the relationship between idiosyncratic volatility and expected returns of risky assets. I find that when the true asset pricing model cannot be identified, the idiosyncratic volatility obtained from a misspecified model contains information regarding the hedge portfolio in Merton’s (1973) ICAPM. Empirically, I find that from 1815 to 2018, a combination of equal-weighted idiosyncratic volatility (EWIV) and value-weighted idiosyncratic volatility (VWIV) can strongly forecast stock market returns over short- and long-term horizons. Furthermore, EWIV and VWIV jointly can explain the cross-section of average stock returns. I show that the combination of EWIV and VWIV is a proxy for the conditional covariance risk in the ICAPM. The deduction also provides new insights concerning the tail risk measure proposed by Kelly and Jiang (2014). The second essay is a joint work with Bing Han. We propose a new and robust predictor of stock market returns and real economic activities based on information from equity options. We aggregate the difference in implied volatilities of at-the-money call and put options across stocks and find that the aggregate implied volatility spread (IVS) is significantly and positively related to future stock market returns. We attribute the predictive power to common informed trading in equity options instead of time-varying risk premium. The third essay, coauthored with Yoontae Jeon and Raymond Kan, studies the expected option return under an extended Black-Scholes model that incorporates the presence of stock return autocorrelation. We show that expected returns of both call and put options are increasing functions of return autocorrelation coefficient of the underlying stock. We find strong empirical evidence from the cross-section of average returns of equity options to support this prediction. Average returns of calls and puts as well as straddle returns all show monotonically increasing relationship with the degree of underlying stock’s return autocorrelation coefficient. We also examine how the information on stock return autocorrelation helps investors to improve the out-of-sample performance of their portfolios.Ph.D