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

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Author Info
Martin Martens () (Faculty of Economics, Erasmus Universiteit Rotterdam)
Dick van Dijk () (Faculty of Economics, Erasmus Universiteit Rotterdam)
Michiel de Pooter () (Faculty of Economics, Erasmus Universiteit Rotterdam)

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Abstract

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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Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 04-067/4.

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Date of creation: 09 Jun 2004
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Handle: RePEc:dgr:uvatin:20040067

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Related research
Keywords: Realized volatility high-frequency data long memory day-of-the-week effect leverage effect volatility forecasting smooth transition

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Find related papers by JEL classification:
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Offer Lieberman & Peter C. B. Phillips, 2006. "Refined Inference on Long Memory in Realized Volatility," Cowles Foundation Discussion Papers 1549, Cowles Foundation, Yale University. [Downloadable!]
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  2. Chun Liu & John M Maheu, 2007. "Are there Structural Breaks in Realized Volatility?," Working Papers tecipa-304, University of Toronto, Department of Economics. [Downloadable!]
  3. Maurício Yoshinori Une & Marcelo Savino Portugal, 2005. "Can fear beat hope? A story of GARCH-in-Mean-Level effects for Emerging Market Country Risks," Econometrics 0509006, EconWPA. [Downloadable!]
  4. Michiel de Pooter & Martin Martens & Dick van Dijk, 2005. "Predicting the Daily Covariance Matrix for S&P 100 Stocks Using Intraday Data - But Which Frequency to Use?," Tinbergen Institute Discussion Papers 05-089/4, Tinbergen Institute, revised 03 Jan 2006. [Downloadable!]
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  5. Fulvio Corsi & Uta Kretschmer & Stefan Mittnik & Christian Pigorsch, 2005. "The Volatility of Realized Volatility," CFS Working Paper Series 2005/33, Center for Financial Studies. [Downloadable!]
  6. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold,, 2003. "Some Like it Smooth, and Some Like it Rough: Untangling Continuous and Jump Components in Measuring, Modeling, and Forecasting Asset Return Volatility," CFS Working Paper Series 2003/35, Center for Financial Studies. [Downloadable!]
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