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Dynamic estimation of volatility risk premia and investor risk aversion from option-implied and realized volatilities

Listed author(s):
  • Hao Zhou
  • Tim Bollerslev
  • Michael S. Gibson

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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Volume (Year): (2005)
Issue (Month): ()
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Handle: RePEc:fip:fedgpr:y:2005:x:32
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