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

Author

Listed:
  • 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

This discussion paper resulted in a publication in the 'International Journal of Forecasting', 2009, 27, 282-303. 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.

Suggested Citation

  • Martin Martens & Dick van Dijk & Michiel de Pooter, 2004. "Modeling and Forecasting S&P 500 Volatility: Long Memory, Structural Breaks and Nonlinearity," Tinbergen Institute Discussion Papers 04-067/4, Tinbergen Institute.
  • Handle: RePEc:tin:wpaper:20040067
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    References listed on IDEAS

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    More about this item

    Keywords

    Realized volatility; high-frequency data; long memory; day-of-the-week effect; leverage effect; volatility forecasting; smooth transition;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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