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Modeling and Forecasting S&P 500 Volatility: Long Memory, Structural Breaks and Nonlinearity

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Author Info

  • Martin Martens

    () (Faculty of Economics, Erasmus Universiteit Rotterdam)

  • Dick van Dijk

    () (Faculty of Economics, Erasmus Universiteit Rotterdam)

  • Michiel de Pooter

    () (Faculty of Economics, Erasmus Universiteit Rotterdam)

Abstract

The sum of squared intraday returns provides an unbiased and almost error-free measure of ex-post volatility. In this paper we develop a nonlinear Autoregressive Fractionally Integrated Moving Average (ARFIMA) model for realized volatility, which accommodates level shifts, day-of-the-week effects, leverage effects and volatility level effects. Applying the model to realized volatilities of the S&P 500 stock index and three exchange rates produces forecasts that clearly improve upon the ones obtained from a linear ARFIMA model and from conventional time-series models based on daily returns, treating volatility as a latent variable.

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Bibliographic Info

Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 04-067/4.

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Date of creation: 09 Jun 2004
Date of revision:
Handle: RePEc:dgr:uvatin:20040067

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Web page: http://www.tinbergen.nl

Related research

Keywords: Realized volatility; high-frequency data; long memory; day-of-the-week effect; leverage effect; volatility forecasting; smooth transition;

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References

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