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Can the evolution of implied volatility be forecasted? Evidence from European and US implied volatility indices

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  • Konstantinidi, Eirini
  • Skiadopoulos, George
  • Tzagkaraki, Emilia

Abstract

We address the question whether the evolution of implied volatility can be forecasted by studying a number of European and US implied volatility indices. Both point and interval forecasts are formed by alternative model specifications. The statistical and economic significance of these forecasts is examined. The latter is assessed by trading strategies in the recently inaugurated CBOE volatility futures markets. Predictable patterns are detected from a statistical point of view. However, these are not economically significant since no abnormal profits can be attained. Hence, the hypothesis that the volatility futures markets are efficient cannot be rejected.

Suggested Citation

  • Konstantinidi, Eirini & Skiadopoulos, George & Tzagkaraki, Emilia, 2008. "Can the evolution of implied volatility be forecasted? Evidence from European and US implied volatility indices," Journal of Banking & Finance, Elsevier, vol. 32(11), pages 2401-2411, November.
    • Handle: RePEc:eee:jbfina:v:32:y:2008:i:11:p:2401-2411
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    1. Ser‐Huang Poon & Peter, F. Pope, 2000. "Trading volatility spreads: a test of index option market efficiency," European Financial Management, European Financial Management Association, vol. 6(2), pages 235-260, June.
    2. Fernandes, Marcelo & Medeiros, Marcelo C. & Scharth, Marcel, 2014. "Modeling and predicting the CBOE market volatility index," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 1-10.
    3. Chris Brooks & M. Currim Oozeer, 2002. "Modelling the Implied Volatility of Options on Long Gilt Futures," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 29(1&2), pages 111-137.
    4. Guidolin, Massimo & Timmermann, Allan, 2003. "Option prices under Bayesian learning: implied volatility dynamics and predictive densities," Journal of Economic Dynamics and Control, Elsevier, vol. 27(5), pages 717-769, March.
    5. Fama, Eugene F & French, Kenneth R, 1988. "Permanent and Temporary Components of Stock Prices," Journal of Political Economy, University of Chicago Press, vol. 96(2), pages 246-273, April.
    6. Wayne E. Ferson & Sergei Sarkissian & Timothy T. Simin, 2003. "Spurious Regressions in Financial Economics?," Journal of Finance, American Finance Association, vol. 58(4), pages 1393-1413, August.
    7. Chris Brooks & M. Currim Oozeer, 2002. "Modelling the Implied Volatility of Options on Long Gilt Futures," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 29(1‐2), pages 111-137.
    8. Franck Moraux & Patrick Navatte & Christophe Villa, 1999. "The Predictive Power of the French Market Volatility Index: A Multi Horizons Study," Review of Finance, European Finance Association, vol. 2(3), pages 303-320.
    9. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-862, November.
    10. repec:bla:jfinan:v:58:y:2003:i:4:p:1393-1414 is not listed on IDEAS
    11. 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.
    12. Sílvia Gonçalves & Massimo Guidolin, 2006. "Predictable Dynamics in the S&P 500 Index Options Implied Volatility Surface," The Journal of Business, University of Chicago Press, vol. 79(3), pages 1591-1636, May.
    13. Pong, Shiuyan & Shackleton, Mark B. & Taylor, Stephen J. & Xu, Xinzhong, 2004. "Forecasting currency volatility: A comparison of implied volatilities and AR(FI)MA models," Journal of Banking & Finance, Elsevier, vol. 28(10), pages 2541-2563, October.
    14. Hentschel, Ludger, 2003. "Errors in Implied Volatility Estimation," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 38(4), pages 779-810, December.
    15. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
    16. GIOT, Pierre, 2005. "Implied volatility indexes and daily Value at Risk models," LIDAM Reprints CORE 1840, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    17. repec:bla:jfinan:v:53:y:1998:i:6:p:2059-2106 is not listed on IDEAS
    18. Dajiang Guo, 2000. "Dynamic Volatility Trading Strategies in the Currency Option Market," Review of Derivatives Research, Springer, vol. 4(2), pages 133-154, May.
    19. Banerjee, Prithviraj S. & Doran, James S. & Peterson, David R., 2007. "Implied volatility and future portfolio returns," Journal of Banking & Finance, Elsevier, vol. 31(10), pages 3183-3199, October.
    20. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    21. Dotsis, George & Psychoyios, Dimitris & Skiadopoulos, George, 2007. "An empirical comparison of continuous-time models of implied volatility indices," Journal of Banking & Finance, Elsevier, vol. 31(12), pages 3584-3603, December.
    22. Charles J. Corrado & Thomas W. Miller, 2006. "Estimating Expected Excess Returns Using Historical And Option‐Implied Volatility," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 29(1), pages 95-112, March.
    23. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
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