Nonparametric Option-implied Volatility

Abstract

The speaker proposes a nonparametric estimator for spot volatility from short-dated options. The estimator is based on forming portfolios of options with different strikes that replicate the (risk-neutral) conditional characteristic function of the underlying price in a model-free way. The separation of volatility from jumps is done by making use of the dominant role of the volatility in the conditional characteristic function over short time intervals and for large values of the characteristic exponent. The latter is chosen in an adaptive way in order to account for the time-varying volatility. He derives a feasible joint central limit theorem for the proposed option-based volatility estimator and existing high-frequency return-based volatility estimators. The limit distribution is mixed-Gaussian reflecting the time-varying precision in the volatility recovery. Numerical experiments show the efficiency gains from the newly-developed option-based volatility extraction.

About the speaker

Prof. Viktor Todorov received his PhD in Economics from Duke University in 2007. He joined the Northwestern University afterwards and is currently the Harold H Hines Jr Professor of Risk Management and Professor of Finance at the Kellogg School of Management.

Prof. Todorov’s research focuses on theoretical and empirical asset pricing, derivatives and econometrics. His research also covers robust estimation of asset pricing models using high-frequency financial data as well as the identification and modeling of jump risk premium combining information from options markets.

Prof. Todorov was elected a fellow of the Journal of Econometrics (2014) and a fellow of the Society for Financial Econometrics (2013).