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

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  • Sheri M. Markose

    ()

  • Yue Peng
  • Amadeo Alentorn

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.

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Paper provided by University of Essex, Department of Economics in its series Economics Discussion Papers with number 713.

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Date of creation: 01 Mar 2012
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Handle: RePEc:esx:essedp:713

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  1. de Jong, C.M. & Huisman, R., 2000. "From Skews to a Skewed-t," ERIM Report Series Research in Management ERS-2000-12-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus Uni.
  2. Torben G. Andersen & Oleg Bondarenko, 2007. "Construction and Interpretation of Model-Free Implied Volatility," NBER Working Papers 13449, National Bureau of Economic Research, Inc.
  3. Amadeo Alentorn & Sheri Markose, 2006. "Removing Maturity Effects of Implied Risk Neutral Densities and Related Statistics," Economics Discussion Papers 609, University of Essex, Department of Economics.
  4. Christensen, B. J. & Prabhala, N. R., 1998. "The relation between implied and realized volatility," Journal of Financial Economics, Elsevier, vol. 50(2), pages 125-150, November.
  5. Becker, Ralf & Clements, Adam E., 2008. "Are combination forecasts of S&P 500 volatility statistically superior?," International Journal of Forecasting, Elsevier, vol. 24(1), pages 122-133.
  6. Becker, Ralf & Clements, Adam E. & White, Scott I., 2007. "Does implied volatility provide any information beyond that captured in model-based volatility forecasts?," Journal of Banking & Finance, Elsevier, vol. 31(8), pages 2535-2549, August.
  7. Blair, Bevan J. & Poon, Ser-Huang & Taylor, Stephen J., 2001. "Forecasting S&P 100 volatility: the incremental information content of implied volatilities and high-frequency index returns," Journal of Econometrics, Elsevier, vol. 105(1), pages 5-26, November.
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