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Bayesian Semiparametric Multivariate GARCH Modeling

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  • Mark J. Jensen

    ()
    (Federal Reserve Bank of Atlanta, USA)

  • John M. Maheu

    ()
    (Department of Economics, University of Toronto, Canada; RCEA, Italy)

Abstract

This paper proposes a Bayesian nonparametric modeling approach for the return distribution in multivariate GARCH models. In contrast to the parametric literature the return distribution can display general forms of asymmetry and thick tails. An infinite mixture of multivariate normals is given a flexible Dirichlet process prior. The GARCH functional form enters into each of the components of this mixture. We discuss conjugate methods that allow for scale mixtures and nonconjugate methods which provide mixing over both the location and scale of the normal components. MCMC methods are introduced for posterior simulation and computation of the predictive density. Bayes factors and density forecasts with comparisons to GARCH models with Student-t innovations demonstrate the gains from our flexible modeling approach.

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Bibliographic Info

Paper provided by The Rimini Centre for Economic Analysis in its series Working Paper Series with number 48_12.

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Date of creation: Jun 2012
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Handle: RePEc:rim:rimwps:48_12

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  1. Concepción Ausín & Pedro Galeano & Pulak Ghosh, 2010. "A semiparametric Bayesian approach to the analysis of financial time series with applications to value at risk estimation," Statistics and Econometrics Working Papers ws103822, Universidad Carlos III, Departamento de Estadística y Econometría.
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Cited by:
  1. Xibin Zhang & Maxwell L. King, 2011. "Bayesian semiparametric GARCH models," Monash Econometrics and Business Statistics Working Papers 24/11, Monash University, Department of Econometrics and Business Statistics.
  2. Audrone Virbickaite & Concepción Ausín & Pedro Galeano, 2013. "A Bayesian non-parametric approach to asymmetric dynamic conditional correlation model with application to portfolio selection," Statistics and Econometrics Working Papers ws131009, Universidad Carlos III, Departamento de Estadística y Econometría.
  3. Jensen, Mark J & Maheu, John M, 2013. "Risk, Return and Volatility Feedback: A Bayesian Nonparametric Analysis," MPRA Paper 52132, University Library of Munich, Germany.

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