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Score-Driven Models for Realized Volatility

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  • Harvey, A.
  • Palumbo, D.

Abstract

This paper sets up a statistical framework for modeling realised volatility (RV ) using a Dynamic Conditional Score (DCS) model. It first shows how a preliminary analysis of RV, based on fitting a linear Gaussian model to its logarithm, confirms the presence of long memory effects and suggests a two component dynamic specification. It also indicates a weekly pattern in the data and an analysis of squared residuals suggests the presence of heteroscedasticity. Furthermore working with a Gaussian model in logarithms facilitates a comparison with the popular Heterogeneous Autoregression (HAR), which is a simple way of accounting for long memory in RV. Fitting the two component specification with leverage and a day of the week effect is then carried out directly on RV with a Generalised Beta of the second kind (GB2) conditional distribution. Estimating logRV with an Exponential Generalised Beta of the second kind (EGB2) distribution gives the same result. The EGB2 model is then fitted with heteroscedasticity and its forecasting performance compared with that of HAR. There is a small gain from using the DCS model. However, its main attraction is that it gives a comprehensive description of the properties of the data and yields multi-step forecasts of the conditional distribution of RV.

Suggested Citation

  • Harvey, A. & Palumbo, D., 2019. "Score-Driven Models for Realized Volatility," Cambridge Working Papers in Economics 1950, Faculty of Economics, University of Cambridge.
  • Handle: RePEc:cam:camdae:1950
    Note: ach34, dp470
    as

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    File URL: http://www.econ.cam.ac.uk/research-files/repec/cam/pdf/cwpe1950.pdf
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    References listed on IDEAS

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    1. Fulvio Corsi & Stefan Mittnik & Christian Pigorsch & Uta Pigorsch, 2008. "The Volatility of Realized Volatility," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 46-78.
    2. Andrew J. Patton & Kevin Sheppard, 2015. "Good Volatility, Bad Volatility: Signed Jumps and The Persistence of Volatility," The Review of Economics and Statistics, MIT Press, vol. 97(3), pages 683-697, July.
    3. Tata Subba Rao & Granville Tunnicliffe Wilson & Andrew Harvey & Rutger-Jan Lange, 2017. "Volatility Modeling with a Generalized t Distribution," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(2), pages 175-190, March.
    4. Audrino, Francesco & Corsi, Fulvio, 2010. "Modeling tick-by-tick realized correlations," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2372-2382, November.
    5. Engle, Robert F. & Gallo, Giampiero M., 2006. "A multiple indicators model for volatility using intra-daily data," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 3-27.
    6. André Lucas & Bernd Schwaab & Xin Zhang, 2014. "Conditional Euro Area Sovereign Default Risk," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(2), pages 271-284, April.
    7. Muller, Ulrich A. & Dacorogna, Michel M. & Dave, Rakhal D. & Olsen, Richard B. & Pictet, Olivier V. & von Weizsacker, Jacob E., 1997. "Volatilities of different time resolutions -- Analyzing the dynamics of market components," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 213-239, June.
    8. Ole E. Barndorff‐Nielsen & Neil Shephard, 2002. "Econometric analysis of realized volatility and its use in estimating stochastic volatility models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(2), pages 253-280, May.
    9. Michele Caivano & Andrew Harvey, 2014. "Time-series models with an EGB2 conditional distribution," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(6), pages 558-571, November.
    10. McDonald, James B. & Xu, Yexiao J., 1995. "A generalization of the beta distribution with applications," Journal of Econometrics, Elsevier, vol. 69(2), pages 427-428, October.
    11. Francisco Blasques & Paolo Gorgi & Siem Jan Koopman & Olivier Wintenberger, 2016. "Feasible Invertibility Conditions and Maximum Likelihood Estimation for Observation-Driven Models," Tinbergen Institute Discussion Papers 16-082/III, Tinbergen Institute.
    12. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, vol. 71(2), pages 579-625, March.
    13. Bollerslev, Tim & Patton, Andrew J. & Quaedvlieg, Rogier, 2016. "Exploiting the errors: A simple approach for improved volatility forecasting," Journal of Econometrics, Elsevier, vol. 192(1), pages 1-18.
    14. James Mitchell & Kenneth F. Wallis, 2011. "Evaluating density forecasts: forecast combinations, model mixtures, calibration and sharpness," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(6), pages 1023-1040, September.
    15. Drew Creal & Siem Jan Koopman & André Lucas, 2013. "Generalized Autoregressive Score Models With Applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(5), pages 777-795, August.
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    Keywords

    EGARCH; GB2 distribution; HAR model; heteroscedasticity; long memory; weekly volatility pattern;
    All these keywords.

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