Forecasting Extreme Volatility of FTSE-100 with Model Free VFTSE, Carr-Wu and Generalized Extreme Value (GEV) Option Implied Volatility Indices

Posted: 26 Feb 2013

See all articles by Sheri M. Markose

Sheri M. Markose

University of Essex - Department of Economics

Date Written: March 1, 2012

Abstract

Since its introduction in 2003, volatility indices such as the VIX based on the model-free implied volatility (MFIV) have become the industry standard for assessing equity market volatility. MFIV suffers from estimation bias which typically underestimates volatility during extreme market conditions due to sparse data for options traded at very high or very low strike prices, Jiang and Tian (2007). To address this problem, we propose modifications to the CBOE MFIV using Carr and Wu (2009) moneyness based interpolations and extrapolations of implied volatilities and so called GEV-IV derived from the Generalised Extreme Value (GEV) option pricing model of Markose and Alentorn (2011). GEV-IV gives the best forecasting performance when compared to the model-free VFTSE, Black-Scholes IV and the Carr-Wu case, for realised volatility of the FTSE-100, both during normal and extreme market conditions in 2008 when realised volatility peaked at 80%. The success of GEV-IV comes from the explicit modelling of the implied tail shape parameter and the time scaling of volatility in the risk neutral density which can rapidly and flexibly reflect extreme market sentiments present in traded option prices.

Keywords: Extreme Events,VFTSE, Model-Free Implied Volatility, Generalized Extreme Value Distribution, Implied Tail Index, Volatility Forecasting

JEL Classification: C13, C16, G01

Suggested Citation

Markose, Sheri M., Forecasting Extreme Volatility of FTSE-100 with Model Free VFTSE, Carr-Wu and Generalized Extreme Value (GEV) Option Implied Volatility Indices (March 1, 2012). Available at SSRN: https://ssrn.com/abstract=2224180

Sheri M. Markose (Contact Author)

University of Essex - Department of Economics ( email )

Wivenhoe Park
Colchester CO4 3SQ
United Kingdom
01206 87 2742 (Phone)

Here is the Coronavirus
related research on SSRN

Paper statistics

Abstract Views
339
PlumX Metrics