Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5096776 | Journal of Econometrics | 2011 | 11 Pages |
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.
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Tim Bollerslev, Michael Gibson, Hao Zhou,