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Forecasting Volatility with Copula-Based Time Series Models

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Author Info

  • Oleg Sokolinskiy

    ()
    (Erasmus University Rotterdam)

  • Dick van Dijk

    ()
    (Erasmus University Rotterdam)

Abstract

This paper develops a novel approach to modeling and forecasting realized volatility (RV) measures based on copula functions. Copula-based time series models can capture relevant characteristics of volatility such as nonlinear dynamics and long-memory type behavior in a flexible yet parsimonious way. In an empirical application to daily volatility for S&P500 index futures, we find that the copula-based RV (C-RV) model outperforms conventional forecasting approaches for one-day ahead volatility forecasts in terms of accuracy and efficiency. Among the copula specifications considered, the Gumbel C-RV model achieves the best forecast performance, which highlights the importance of asymmetry and upper tail dependence for modeling volatility dynamics. Although we find substantial variation in the copula parameter estimates over time, conditional copulas do not improve the accuracy of volatility forecasts.

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Bibliographic Info

Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 11-125/4.

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Date of creation: 05 Sep 2011
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Handle: RePEc:dgr:uvatin:20110125

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Web page: http://www.tinbergen.nl

Related research

Keywords: Nonlinear dependence; long memory; copulas; volatility forecasting;

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References

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  1. Martens, Martin & van Dijk, Dick & de Pooter, Michiel, 2009. "Forecasting S&P 500 volatility: Long memory, level shifts, leverage effects, day-of-the-week seasonality, and macroeconomic announcements," International Journal of Forecasting, Elsevier, vol. 25(2), pages 282-303.
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  7. Jean-David FERMANIAN & Olivier SCAILLET, 2004. "Some Statistical Pitfalls In Copula Modeling For Financial Applications," FAME Research Paper Series rp108, International Center for Financial Asset Management and Engineering.
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  9. Martens, M.P.E. & van Dijk, D.J.C., 2006. "Measuring volatility with the realized range," Econometric Institute Research Papers EI 2006-10, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
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  18. Brendan K. Beare, 2010. "Copulas and Temporal Dependence," Econometrica, Econometric Society, vol. 78(1), pages 395-410, 01.
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