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

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.