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Analytic Score for Multivariate GARCH Models

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  • Riccardo LUCCHETTI

    (Universit… Politecnica delle Marche, Dipartimento di Economia)

Abstract

Multivariate GARCH models constitute the workhorse of empirical applications in several fields, a notable example being financial econometrics. Unfortunately, ML (or quasi-ML) estimation of such models, although relatively straightforward in theory, is often made difficult by the fact that available software relies on numerical methods for computing the first derivatives of the log-likelihood; the fact that these models often include several dozens of parameters makes it impractical to estimate even medium-sized models. In this paper, closed-form expressions for the score of the BEKK model of Engle and Kroner (1995) are obtained, and strategies for efficient computation are discussed.

Suggested Citation

  • Riccardo LUCCHETTI, 1999. "Analytic Score for Multivariate GARCH Models," Working Papers 119, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    • Handle: RePEc:anc:wpaper:119
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    Cited by:

    1. K. Diamantopoulos & I. Vrontos, 2010. "A Student-t Full Factor Multivariate GARCH Model," Computational Economics, Springer;Society for Computational Economics, vol. 35(1), pages 63-83, January.
    2. Riccardo LUCCHETTI & Giulio PALOMBA, 2006. "Forecasting US bond yields at weekly frequency," Working Papers 261, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    3. Ugo FRATESI, 2003. "Innovation Diffusion and the Evolution of Regional Disparities," Working Papers 186, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    4. Nicola MATTEUCCI & Alessandro STERLACCHINI, 2003. "ICT and Employment Growth in Italian Industries," Working Papers 193, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    5. Luc Bauwens & Sébastien Laurent & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109, January.
    6. Gatfaoui, Hayette, 2013. "Translating financial integration into correlation risk: A weekly reporting's viewpoint for the volatility behavior of stock markets," Economic Modelling, Elsevier, vol. 30(C), pages 776-791.
    7. Massimiliano Caporin & Riccardo (Jack) Lucchetti & Giulio Palomba, 2020. "Analytical Gradients of Dynamic Conditional Correlation Models," JRFM, MDPI, vol. 13(3), pages 1-21, March.
    8. Mendoza, Alfonso. & Galvanovskis, Evalds., 2014. "La cópula GED bivariada. Una aplicación en entornos de crisis," El Trimestre Económico, Fondo de Cultura Económica, vol. 0(323), pages .721-746, julio-sep.
    9. Mendoza-Velázquez, Alfonso & Galvanovskis, Evalds, 2009. "Introducing the GED-Copula with an application to Financial Contagion in Latin America," MPRA Paper 46669, University Library of Munich, Germany, revised 01 Feb 2010.
    10. Christian Hafner & Helmut Herwartz, 2008. "Analytical quasi maximum likelihood inference in multivariate volatility models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 67(2), pages 219-239, March.
    11. Lucchetti, Riccardo & Palomba, Giulio, 2008. "Nonlinear Adjustment in US Bond Yields: an Empirical Analysis with Conditional Heteroskedasticity," MPRA Paper 11571, University Library of Munich, Germany.
    12. Lütkepohl,Helmut & Krätzig,Markus (ed.), 2004. "Applied Time Series Econometrics," Cambridge Books, Cambridge University Press, number 9780521547871.
    13. Lucchetti, Riccardo & Palomba, Giulio, 2009. "Nonlinear adjustment in US bond yields: An empirical model with conditional heteroskedasticity," Economic Modelling, Elsevier, vol. 26(3), pages 659-667, May.
    14. Krishnakumar, Jaya & Kabili, Andi & Roko, Ilir, 2012. "Estimation of SEM with GARCH errors," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3153-3181.

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