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.
|Keywords||day-of-the-week effect, high-frequency data, leverage effect, long memory, realized volatility, smooth transition, volatility forecasting|
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). Retrieved from http://hdl.handle.net/1765/6630