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Multivariate high‐frequency‐based volatility (HEAVY) models

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  • Diaa Noureldin
  • Neil Shephard
  • Kevin Sheppard

Abstract

This paper introduces a new class of multivariate volatility models that utilizes high-frequency data.� We discuss the models' dynamics and highlight their differences from�multivariate GARCH models.� We also discuss their covariance targeting specification and provide closed-form formulas for multi-step forecasts.� Estimation and inference strategies are outlined.� Empirical results suggest that the HEAVY model outperforms the multivariate GARCH model out-of-sample, with the gains being particularly significant at short forecast horizons.� Forecast gains are obtained for both forecast variances and correlations.

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Bibliographic Info

Article provided by John Wiley & Sons, Ltd. in its journal Journal of Applied Econometrics.

Volume (Year): 27 (2012)
Issue (Month): 6 (09)
Pages: 907-933

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Handle: RePEc:wly:japmet:v:27:y:2012:i:6:p:907-933

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Citations

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Cited by:
  1. Roxana Halbleib & Valeri Voev, 2011. "Forecasting Covariance Matrices: A Mixed Frequency Approach," CREATES Research Papers 2011-03, School of Economics and Management, University of Aarhus.
  2. BAUWENS, Luc & STORTI, Giuseppe, . "Computationally efficient inference procedures for vast dimensional realized covariance models," CORE Discussion Papers RP -2469, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  3. Matthias R. Fengler & Ostap Okhrin, 2012. "Realized Copula," SFB 649 Discussion Papers SFB649DP2012-034, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  4. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2012. "Financial Risk Measurement for Financial Risk Management," NBER Working Papers 18084, National Bureau of Economic Research, Inc.
  5. Kevin Sheppard, 2014. "Factor High-Frequency Based Volatility (HEAVY) Models," Economics Series Working Papers 710, University of Oxford, Department of Economics.
  6. Gribisch, Bastian, 2013. "A latent dynamic factor approach to forecasting multivariate stock market volatility," Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79823, Verein für Socialpolitik / German Economic Association.
  7. Weigand, Roland, 2014. "Matrix Box-Cox Models for Multivariate Realized Volatility," University of Regensburg Working Papers in Business, Economics and Management Information Systems 478, University of Regensburg, Department of Economics.
  8. Huang, Shih-Feng & Tu, Ya-Ting, 2014. "Asymptotic distribution of the EPMS estimator for financial derivatives pricing," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 129-145.
  9. Noureldin, Diaa & Shephard, Neil & Sheppard, Kevin, 2014. "Multivariate rotated ARCH models," Journal of Econometrics, Elsevier, vol. 179(1), pages 16-30.
  10. Diaa Noureldin & Neil Shephard & Kevin Sheppard, 2012. "Multivariate Rotated ARCH Models," Economics Papers 2012-W01, Economics Group, Nuffield College, University of Oxford.
  11. Anne Opschoor & Dick van Dijk & Michel van der Wel, 2013. "Predicting Covariance Matrices with Financial Conditions Indexes," Tinbergen Institute Discussion Papers 13-113/III, Tinbergen Institute.
  12. Pawel Janus & Andr� Lucas & and Anne Opschoor, 2014. "New HEAVY Models for Fat-Tailed Returns and Realized Covariance Kernels," Tinbergen Institute Discussion Papers 14-073/IV, Tinbergen Institute.
  13. Kris Boudt & Sébastien Laurent & Asger Lunde & Rogier Quaedvlieg, 2014. "Positive Semidefinite Integrated Covariance Estimation, Factorizations and Asynchronicity," CREATES Research Papers 2014-05, School of Economics and Management, University of Aarhus.
  14. Matteo Barigozzi & Christian T. Brownlees & Giampiero M. Gallo & David Veredas, 2014. "Disentangling Systematic and Idiosyncratic Dynamics in Panels of Volatility Measures," Econometrics Working Papers Archive 2014_02, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", revised Feb 2014.
  15. Cordis, Adriana S. & Kirby, Chris, 2014. "Discrete stochastic autoregressive volatility," Journal of Banking & Finance, Elsevier, vol. 43(C), pages 160-178.
  16. Makoto Takahashi & Toshiaki Watanabe & Yasuhiro Omori, 2014. "Volatility and Quantile Forecasts by Realized Stochastic Volatility Models with Generalized Hyperbolic Distribution," CIRJE F-Series CIRJE-F-921, CIRJE, Faculty of Economics, University of Tokyo.

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