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Realized Beta GARCH: Multivariate GARCH Model with Realized Measures of Volatility and CoVolatility

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  • Peter Reinhard Hansen
  • Asger Lunde
  • Valeri Voev

Abstract

We introduce a multivariate GARCH model that incorporates realized measures of volatility and covolatility. The realized measures extract information about the current level of volatility and covolatility from high-frequency data, which is particularly useful for the modeling of return volatility during periods with rapid changes in volatility and covolatility. When applied to market returns in conjunction with returns on an individual asset, the model yields a dynamic model of the conditional regression coefficient that is known as the beta. We apply the model to a large set of assets and find the conditional betas to be far more variable than is usually found with rolling-window regressions based exclusively on daily returns. In the empirical part of the paper we examine the cross-sectional as well as the time variation of the conditional beta series during the financial crises.

Suggested Citation

  • Peter Reinhard Hansen & Asger Lunde & Valeri Voev, 2012. "Realized Beta GARCH: Multivariate GARCH Model with Realized Measures of Volatility and CoVolatility," Economics Working Papers ECO2012/28, European University Institute.
  • Handle: RePEc:eui:euiwps:eco2012/28
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Roxana Halbleib & Valerie Voev, 2011. "Forecasting Covariance Matrices: A Mixed Frequency Approach," Working Papers ECARES ECARES 2011-002, ULB -- Universite Libre de Bruxelles.
    2. Nikolaus Hautsch & Lada M. Kyj & Peter Malec, 2015. "Do High‐Frequency Data Improve High‐Dimensional Portfolio Allocations?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(2), pages 263-290, March.
    3. Matthias R. Fengler & Ostap Okhrin, 2012. "Realized Copula," SFB 649 Discussion Papers SFB649DP2012-034, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    4. Diaa Noureldin & Neil Shephard & Kevin Sheppard, 2012. "Multivariate high‐frequency‐based volatility (HEAVY) models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(6), pages 907-933, September.
    5. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2013. "Financial Risk Measurement for Financial Risk Management," Handbook of the Economics of Finance, Elsevier.
    6. Kevin Sheppard, 2014. "Factor High-Frequency Based Volatility (HEAVY) Models," Economics Series Working Papers 710, University of Oxford, Department of Economics.
    7. Bannouh, K. & Martens, M.P.E. & Oomen, R.C.A. & van Dijk, D.J.C., 2012. "Realized mixed-frequency factor models for vast dimensional covariance estimation," ERIM Report Series Research in Management ERS-2012-017-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    8. KALNINA, Ilze, 2015. "Inference for nonparametric high-frequency estimators with an application to time variation in betas," Cahiers de recherche 2015-08, Universite de Montreal, Departement de sciences economiques.
    9. Hautsch, Nikolaus & Kyj, Lada M. & Malec, Peter, 2011. "The merit of high-frequency data in portfolio allocation," CFS Working Paper Series 2011/24, Center for Financial Studies (CFS).
    10. Manabu Asai, 2013. "Heterogeneous Asymmetric Dynamic Conditional Correlation Model with Stock Return and Range," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(5), pages 469-480, August.
    11. Peter Christoffersen & Mathieu Fournier & Kris Jacobs, 2013. "The Factor Structure in Equity Options," CREATES Research Papers 2013-47, Department of Economics and Business Economics, Aarhus University.
    12. Asger Lunde & Kasper V. Olesen, 2014. "Modeling and Forecasting the Distribution of Energy Forward Returns - Evidence from the Nordic Power Exchange," CREATES Research Papers 2013-19, Department of Economics and Business Economics, Aarhus University.

    More about this item

    Keywords

    Financial Volatility; Beta; Realized GARCH; High Frequency Data;

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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