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Realising the future: forecasting with high frequency based volatility (HEAVY) models

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Author Info
Neil Shephard
Kevin Sheppard
Abstract

This paper studies in some detail a class of high frequency based volatility (HEAVY) models. These models are direct models of daily asset return volatility based on realized measures constructed from high frequency data. Our analysis identifies that the models have momentum and mean reversion effects, and that they adjust quickly to structural breaks in the level of the volatility process. We study how to estimate the models and how they perform through the credit crunch, comparing their fit to more traditional GARCH models. We analysis a model based bootstrap which allow us to estimate the entire predictive distribution of returns. We also provide an analysis of missing data in the context of these models.

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Paper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number 438.

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Date of creation: 2009
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Handle: RePEc:oxf:wpaper:438

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Keywords: ARCH models; Bootstrap; Missing data; Multiplicative error model; Multistep ahead prediction; Non-nested likelihood ratio test; Realised kernal; Realised volatility;

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  2. Peter Christoffersen & Kris Jacobs & Chayawat Ornthanalai & Yintian Wang, 2008. "Option Valuation with Long-run and Short-run Volatility Components," CREATES Research Papers 2008-11, School of Economics and Management, University of Aarhus. [Downloadable!]
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  3. Giot, Pierre & Laurent, Sebastien, 2004. "Modelling daily Value-at-Risk using realized volatility and ARCH type models," Journal of Empirical Finance, Elsevier, vol. 11(3), pages 379-398, June. [Downloadable!] (restricted)
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  4. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-58, May. [Downloadable!] (restricted)
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  6. Gabriele Fiorentini & Enrique Sentana & Neil Shephard, 2004. "Likelihood-based estimation of latent generalised ARCH structures," OFRC Working Papers Series 2004fe02, Oxford Financial Research Centre. [Downloadable!]
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  7. Christian T. Brownlees & Giampiero Gallo, 2006. "Financial Econometric Analysis at Ultra–High Frequency: Data Handling Concerns," Econometrics Working Papers Archive wp2006_03, Universita' degli Studi di Firenze, Dipartimento di Statistica "G. Parenti". [Downloadable!]
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  8. Bandi & Russell, 2008. "Microstructure Noise, Realized Variance, and Optimal Sampling," Review of Economic Studies, Blackwell Publishing, vol. 75(2), pages 339-369, 04. [Downloadable!] (restricted)
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  11. Raffaella Giacomini & Halbert White, 2006. "Tests of Conditional Predictive Ability," Econometrica, Econometric Society, vol. 74(6), pages 1545-1578, November. [Downloadable!] (restricted)
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  12. Fan, Jianqing & Wang, Yazhen, 2007. "Multi-Scale Jump and Volatility Analysis for High-Frequency Financial Data," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1349-1362, December. [Downloadable!] (restricted)
  13. Tim Bollerslev & Jeffrey Wooldridge, 1992. "Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances," Econometric Reviews, Taylor and Francis Journals, vol. 11(2), pages 143-172. [Downloadable!] (restricted)
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  15. Patrick Burns & Robert Engle & Joseph Mezrich, 1998. "Correlations and Volatilities of Asynchronous Data," University of California at San Diego, Economics Working Paper Series 97-30r, Department of Economics, UC San Diego. [Downloadable!]
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  17. Douglas Rivers & Quang Vuong, 2002. "Model selection tests for nonlinear dynamic models," Econometrics Journal, Royal Economic Society, vol. 5(1), pages 1-39, June. [Downloadable!] (restricted)
  18. Fabrizio Cipollini & Robert F. Engle & Giampiero M. Gallo, 2007. "A Model for Multivariate Non-negative Valued Processes in Financial Econometrics," Econometrics Working Papers Archive wp2007_16, Universita' degli Studi di Firenze, Dipartimento di Statistica "G. Parenti". [Downloadable!]
  19. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2007. "Roughing It Up: Including Jump Components in the Measurement, Modeling and Forecasting of Return Volatility," CREATES Research Papers 2007-18, School of Economics and Management, University of Aarhus. [Downloadable!]
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  20. Lo, Andrew W. & Craig MacKinlay, A., 1990. "An econometric analysis of nonsynchronous trading," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 181-211. [Downloadable!] (restricted)
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  21. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
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