Dynamic Estimation of Volatility Risk Premia and Investor Risk Aversion from Option-Implied and Realized Volatilities

CREATES Research Paper 2007-16

48 Pages Posted: 23 Jun 2008 Last revised: 25 Sep 2009

See all articles by Tim Bollerslev

Tim Bollerslev

Duke University - Finance; Duke University - Department of Economics; National Bureau of Economic Research (NBER)

Michael S. Gibson

Federal Reserve Board

Hao Zhou

Tsinghua University - PBC School of Finance

Multiple version iconThere are 2 versions of this paper

Date Written: April 2007

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&P 500 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.

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

Suggested Citation

Bollerslev, Tim and Gibson, Michael S. and Zhou, Hao, Dynamic Estimation of Volatility Risk Premia and Investor Risk Aversion from Option-Implied and Realized Volatilities (April 2007). CREATES Research Paper 2007-16. Available at SSRN: https://ssrn.com/abstract=1150068 or http://dx.doi.org/10.2139/ssrn.1150068

Tim Bollerslev (Contact Author)

Duke University - Finance ( email )

Durham, NC 27708-0120
United States
919-660-1846 (Phone)
919-684-8974 (Fax)

Duke University - Department of Economics

213 Social Sciences Building
Box 90097
Durham, NC 27708-0204
United States

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Michael S. Gibson

Federal Reserve Board ( email )

Washington, DC 20551
United States
1-202-452-2495 (Phone)

Hao Zhou

Tsinghua University - PBC School of Finance ( email )

No. 43, Chengfu Road
Haidian District
Beijing, 100083
China
86-10-62790655 (Phone)

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