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Forecast performance of implied volatility and the impact of the volatility risk premium

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  • Ralf Becker

    ()
    (Manchester)

  • Adam Clements

    ()
    (QUT)

  • Christopher Coleman-Fenn

    ()
    (QUT)

Abstract

Forecasting volatility has received a great deal of research attention, with the relative performance of econometric models based on time-series data and option implied volatility forecasts often being considered. While many studies find that implied volatility is the preferred approach, a number of issues remain unresolved. Implied volatilities are risk-neutral forecasts of spot volatility, whereas time-series models are estimated on risk-adjusted or real world data of the underlying. Recently, an intuitive method has been proposed to adjust these risk-neutral forecasts into their risk-adjusted equivalents, possibly improving on their forecast accuracy. By utilising recent econometric advances, this paper considers whether these risk-adjusted forecasts are statistically superior to the unadjusted forecasts, as well as a wide range of model based forecasts. It is found that an unadjusted risk-neutral implied volatility is an inferior forecast. However, after adjusting for the risk premia it is of equal predictive accuracy relative to a number of model based forecasts.

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Bibliographic Info

Paper provided by National Centre for Econometric Research in its series NCER Working Paper Series with number 45.

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Length: 25
Date of creation: 21 Jul 2009
Date of revision:
Handle: RePEc:qut:auncer:2009_58

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Keywords: Implied volatility; volatility forecasts; volatility models; volatility risk premium; model confidence sets;

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  1. Peter Reinhard Hansen & Asger Lunde & James M. Nason, 2003. "Choosing the best volatility models: the model confidence set approach," Working Paper 2003-28, Federal Reserve Bank of Atlanta.
  2. Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
  3. 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.
  4. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
  5. Pantula, Sastry G., 1989. "Testing for Unit Roots in Time Series Data," Econometric Theory, Cambridge University Press, vol. 5(02), pages 256-271, August.
  6. 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.
  7. Jorion, Philippe, 1995. " Predicting Volatility in the Foreign Exchange Market," Journal of Finance, American Finance Association, vol. 50(2), pages 507-28, June.
  8. Scott I. White & Adam E. Clements & Stan Hurn, 2004. "Discretised Non-Linear Filtering for Dynamic Latent Variable Models: with Application to Stochastic Volatility," Econometric Society 2004 Australasian Meetings 46, Econometric Society.
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