Dynamic Estimation of Volatility Risk Premia and Investor Risk Aversion from Option-Implied and Realized Volatilities
Duke University - Finance; Duke University - Department of Economics; National Bureau of Economic Research (NBER)
Michael S. Gibson
Federal Reserve Board
Tsinghua University - PBC School of Finance
July 1, 2008
FEDS Working Paper No. 2004-56
AFA 2006 Boston Meetings Paper
Journal of Econometrics, Forthcoming
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.
Number of Pages in PDF File: 31
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
Date posted: January 25, 2005 ; Last revised: March 13, 2009
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