31 Pages Posted: 25 Jan 2005 Last revised: 13 Mar 2009
Date Written: July 1, 2008
This paper proposes a method for constructing a volatility risk premium, or investor risk aversion, index. The method is intuitive and simple to implement, relying on the sample moments of the recently popularized model-free realized and option-implied volatility measures. A small-scale Monte Carlo experiment confirms that the procedure works well in practice. Implementing the procedure with actual S&P500 option-implied volatilities and high-frequency five-minute-based realized volatilities indicate significant temporal dependencies in the estimated stochastic volatility risk premium, which we in turn relate to a set of underlying macro-finance state variables. We also find that the extracted volatility risk premium helps predict future stock market returns.
Keywords: Stochastic Volatility Risk Premium, Model-Free Implied Volatility, Model-Free Realized Volatility, Black-Scholes, GMM Estimation, Return Predictability
JEL Classification: G12, G13, C51, C52
Suggested Citation: Suggested Citation
Bollerslev, Tim and Gibson, Michael S. and Zhou, Hao, Dynamic Estimation of Volatility Risk Premia and Investor Risk Aversion from Option-Implied and Realized Volatilities (July 1, 2008). FEDS Working Paper No. 2004-56; AFA 2006 Boston Meetings Paper; Journal of Econometrics, Forthcoming. Available at SSRN: https://ssrn.com/abstract=614543
By David Bates