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Non-linear modelling and forecasting of S&P 500 volatility

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  • Verhoeven, Peter
  • Pilgram, Berndt
  • McAleer, Michael
  • Mees, Alistair

Abstract

This paper investigates the use of a flexible forecasting method based on non-linear Markov modelling and canonical variate analysis, and the use of a prediction algorithm to forecast conditional volatility. We assess the dynamic behaviour of the model by forecasting volatility of a stock index. It is found that the non-linear non-parametric model based on canonical variate analysis forecasts stock index volatility significantly better than the GJR-GARCH(1,1)-t model due to the flexibility in accommodating multiple dynamic patterns in volatility which are not captured by its parametric counterpart.

Suggested Citation

  • Verhoeven, Peter & Pilgram, Berndt & McAleer, Michael & Mees, Alistair, 2002. "Non-linear modelling and forecasting of S&P 500 volatility," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 59(1), pages 233-241.
    • Handle: RePEc:eee:matcom:v:59:y:2002:i:1:p:233-241
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    References listed on IDEAS

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    Cited by:

    1. A. B. M. Rabiul Alam Beg & Sajid Anwar, 2014. "Detecting volatility persistence in GARCH models in the presence of the leverage effect," Quantitative Finance, Taylor & Francis Journals, vol. 14(12), pages 2205-2213, December.
    2. Beg, A.B.M. Rabiul Alam & Anwar, Sajid, 2012. "Sources of volatility persistence: A case study of the U.K. pound/U.S. dollar exchange rate returns," The North American Journal of Economics and Finance, Elsevier, vol. 23(2), pages 165-184.
    3. Huarng, Kunhuang & Yu, Hui-Kuang, 2005. "A Type 2 fuzzy time series model for stock index forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 353(C), pages 445-462.

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