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

Martin Martens (), Dick van Dijk () and Michiel De Pooter
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Martin Martens: Faculty of Economics, Erasmus Universiteit Rotterdam

No 04-067/4, Tinbergen Institute Discussion Papers from Tinbergen Institute

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.

Keywords: Realized volatility; high-frequency data; long memory; day-of-the-week effect; leverage effect; volatility forecasting; smooth transition (search for similar items in EconPapers)
JEL-codes: C22 C53 G15 (search for similar items in EconPapers)
Date: 2004-06-09
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