Modeling and Forecasting S&P 500 Volatility: Long Memory, Structural Breaks and Nonlinearity - Martin Martens; Dick van Dijk; Michiel de Pooter (Tinbergen Institute, 2004)
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: C22; C53; G15; 330; Realized volatility; high-frequency data; long memory; day-of-the-week effect; leverage effect; volatility forecasting; smooth transition; Börsenkurs; Volatilität; Strukturbruch; Zeitreihenanalyse; Stochastischer Prozess; Theorie; ARMA-Modell