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Finance and Economics Discussion Series logo links to FEDS home page Dynamic Estimation of Volatility Risk Premia and Investor Risk Aversion from Option-Implied and Realized Volatilities
Tim Bollerslev, Michael Gibson, and Hao Zhou

Abstract: 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 suggests that the procedure works well in practice. Implementing the procedure with actual S&P 500 option-implied volatilities and high-frequency five-minute-based realized volatilities results in 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, Monte Carlo, return predictability

Full paper (452 KB PDF)

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Last update: October 19, 2004