Preprint2004

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

Martin P.E. Martens · Dick J.C. van Dijk · Michiel D. De Pooter

45 citations0/0 files storedRePEc: Research Papers in Economics

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Financial Risk and Volatility Modeling Stochastic processes and financial applications Forecasting Techniques and Applications

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.

Citations in this library

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Cited by

2014Forecasting return volatility: Level shifts with varying jump probability and mean reversion 2007Long Memory in the Turkish Stock Market Return and Volatility 2011Modeling volatility with time-varying FIGARCH models 2008Quarterly beta forecasting: An evaluation 2016On calibration of stochastic and fractional stochastic volatility models

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