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

Authors:
Peter Verhoeven;Berndt Pilgram;Michael McAleer;Alistair Mees
Affiliations:
School of Economics and Finance, Curtin University of Technology, Perth, WA 6102, Australia;Department of Mathematics and Statistics, University of Western Australia, Perth, WA 6907, Australia;Department of Economics, University of Western Australia, Perth, WA 6907, Australia;Department of Mathematics and Statistics, University of Western Australia, Perth, WA 6907, Australia
Venue:
Mathematics and Computers in Simulation - Selected papers of the MSSANZ/IMACS 13th biennial conference on modelling and simulation, Hamilton, New Zealand, December 1999
Year:
2002

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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.