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Multivariate Rotated ARCH models

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  • Diaa Noureldin
  • Neil Shephard
  • Kevin Sheppard

Abstract

This paper introduces a new class of multivariate volatility models which is easy to estimate using covariance targeting, even with rich dynamics. We call them rotated ARCH (RARCH) models. The basic structure is to rotate the returns and then to fit them using a BEKK-type parameterization of the time-varying covariance whose long-run covariance is the identity matrix. The extension to DCC-type parameterizations is given, introducing the rotated conditional correlation (RCC) model. Inference for these mdoels is computationally attractive, and the asymptotics are standard. The techniques are illustrated using data on some SJIA stocks.�

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Bibliographic Info

Paper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number 594.

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Date of creation: 16 Feb 2012
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Handle: RePEc:oxf:wpaper:594

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Keywords: RARCH; RCC; Multivariate volatility; Covariance targeting; Common persistence; Empirical Bayes; Predictive likelihood;

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  6. Cavit Pakel & Neil Shephard & Kevin Sheppard, 2009. "Nuisance parameters, composite likelihoods and a panel of GARCH models," Economics Papers 2009-W12, Economics Group, Nuffield College, University of Oxford.
  7. Diaa Noureldin & Neil Shephard & Kevin Sheppard, 2011. "Multivariate High-Frequency-Based Volatility (HEAVY) Models," Economics Papers 2011-W01, Economics Group, Nuffield College, University of Oxford.
  8. bauwens, Luc & hafner, Christian & pierret, Diane, 2011. "Multivariate volatility modeling of electricity futures," CORE Discussion Papers, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) 2011011, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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Cited by:
  1. 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, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti" 2014_02, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", revised Feb 2014.

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