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

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Tinbergen Institute Discussion Paper ; No. 04-067/4

Classification and Topics

• Classification: Wirtschaft ; Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes ; Forecasting Models; Simulation Methods ; International Financial Markets
• Subject: 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

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Martens, Martin ; van Dijk, Dick ; de Pooter, Michiel
• Event: Veröffentlichung
• (who): Tinbergen Institute
• (where): Amsterdam and Rotterdam
• (when): 2004

Further information

• Handle: http://hdl.handle.net/10419/86255
• Last update: 10.03.2025, 11:45 AM CET