Modeling and Forecasting S&P 500 Volatility: Long Memory, Structural Breaks and Nonlinearity
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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|Tinbergen Institute Discussion Paper Series|
Martens, M.P.E, van Dijk, D.J.C, & de Pooter, M.D. (2004). Modeling and Forecasting S&P 500 Volatility: Long Memory, Structural Breaks and Nonlinearity (No. TI 04-067/4). Tinbergen Institute Discussion Paper Series. Retrieved from http://hdl.handle.net/1765/6630