Modeling the Dynamics of Correlations Among Implied Volatilities

The Review of Finance, Forthcoming

40 Pages Posted: 15 Jul 2012 Last revised: 2 Oct 2015

See all articles by Robert F. Engle

Robert F. Engle

New York University - Leonard N. Stern School of Business - Department of Economics; New York University (NYU) - Department of Finance; National Bureau of Economic Research (NBER)

Stephen Figlewski

New York University - Stern School of Business

Date Written: May 2, 2014

Abstract

Implied volatility (IV) reflects both expected empirical volatility and also risk premia. Stochastic variation in either creates unhedged risk in a delta hedged options position. We develop EGARCH/DCC models for the dynamics of volatilities and correlations among daily IVs from options on 28 large cap stocks. The data strong support a general correlation structure and also a 1-factor model with the VIX index as the common factor. Using IVs from stocks that are either highly correlated with the target stock's IV or in the same industry together with the VIX can significantly improve hedging of individual IV changes.

Keywords: stochastic volatility, hedging correlation, implied volatility, GARCH

JEL Classification: G13, G12, C32

Suggested Citation

Engle, Robert F. and Figlewski, Stephen, Modeling the Dynamics of Correlations Among Implied Volatilities (May 2, 2014). The Review of Finance, Forthcoming. Available at SSRN: https://ssrn.com/abstract=2108358 or http://dx.doi.org/10.2139/ssrn.2108358

Robert F. Engle

New York University - Leonard N. Stern School of Business - Department of Economics ( email )

269 Mercer Street
New York, NY 10003
United States

New York University (NYU) - Department of Finance

Stern School of Business
44 West 4th Street
New York, NY 10012-1126
United States

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Stephen Figlewski (Contact Author)

New York University - Stern School of Business ( email )

44 West 4th Street
Department of Finance Suite 9-160
New York, NY 10012-1126
United States
212-998-0712 (Phone)
212-995-4220 (Fax)

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