Forward and Future Implied Volatility

International Journal of Theoretical and Applied Finance, Vol. 14, No. 3, pp. 407-432, 2010

Columbia Business School Research Paper No. 11-20

28 Pages Posted: 20 Oct 2011

See all articles by Paul Glasserman

Paul Glasserman

Columbia Business School

Qi Wu

City University of Hong Kong, School of Data Science

Multiple version iconThere are 2 versions of this paper

Date Written: June 10, 2010

Abstract

We address the problem of defining and calculating forward volatility implied by option prices when the underlying asset is driven by a stochastic volatility process.

We examine alternative notions of forward implied volatility and the information required to extract these measures from the prices of European options at fixed maturities.

We then specialize to the SABR model and show how the asymptotic expansion of the bivariate transition density in Wu {Wu10} allows calibration of the SABR model with piecewise constant parameters and calculation of forward volatility.

We then investigate empirically whether current option prices at multiple maturities contain useful information in predicting future option prices and future implied volatility. We undertake this investigation using data on options on the euro-dollar, sterling-dollar, and dollar-yen exchange rates.

We find that prices across maturities do indeed have predictive value. Moreover, we find that model-based forward volatility extracts this predicative information better than a standard "model-free" measure of forward volatility and better than spot implied volatility. The enhancement to out-of-sample forecasting accuracy gained from model-based forward volatility is greatest at longer forecasting horizons.

Suggested Citation

Glasserman, Paul and Wu, Qi, Forward and Future Implied Volatility (June 10, 2010). International Journal of Theoretical and Applied Finance, Vol. 14, No. 3, pp. 407-432, 2010, Columbia Business School Research Paper No. 11-20, Available at SSRN: https://ssrn.com/abstract=1946403

Paul Glasserman (Contact Author)

Columbia Business School ( email )

New York, NY
United States

Qi Wu

City University of Hong Kong, School of Data Science ( email )

83 Tat Chee Avenue
Kowloon
Hong Kong

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