Dynamic estimation of volatility risk premia and investor risk aversion from option-implied and realized volatilities |
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Authors: | Tim Bollerslev Michael Gibson Hao Zhou |
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Affiliation: | 1. Department of Economics, Duke University, Post Office Box 90097, Durham NC 27708, USA;2. NBER, USA;3. CREATES, Denmark;4. Risk Analysis Section, Federal Reserve Board, Mail Stop 91, Washington DC 20551, USA |
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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 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 indicates significant temporal dependencies in the estimated stochastic volatility risk premium, which we in turn relate to a set of macro-finance state variables. We also find that the extracted volatility risk premium helps predict future stock market returns. |
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Keywords: | G12 G13 C51 C52 |
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