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Thresholds, News Impact Surfaces and Dynamic Asymmetric Multivariate GARCH

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  • Massimiliano Caporin

    (Department of Economic Sciences, University of Padova)

  • Michael McAleer

    (Erasmus School of Economics, Erasmus University Rotterdam and Tinbergen Institute)

Abstract

DAMGARCH is a new model that extends the VARMA-GARCH model of Ling and McAleer (2003) by introducing multiple thresholds and time-dependent structure in the asymmetry of the conditional variances. Analytical expressions for the news impact surface implied by the new model are also presented. DAMGARCH models the shocks affecting the conditional variances on the basis of an underlying multivariate distribution. It is possible to model explicitly asset-specific shocks and common innovations by partitioning the multivariate density support. This paper presents the model structure, describes the implementation issues, and provides the conditions for the existence of a unique stationary solution, and for consistency and asymptotic normality of the quasimaximum likelihood estimators. The paper also presents an empirical example to highlight the usefulness of the new model.

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Bibliographic Info

Paper provided by CIRJE, Faculty of Economics, University of Tokyo in its series CIRJE F-Series with number CIRJE-F-740.

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Length: 52pages
Date of creation: May 2010
Date of revision:
Handle: RePEc:tky:fseres:2010cf740

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Cited by:
  1. Jeroen V.K. Rombouts & Lars Stentoft & Francesco Violante, 2012. "The Value of Multivariate Model Sophistication: An Application to pricing Dow Jones Industrial Average options," CREATES Research Papers 2012-04, School of Economics and Management, University of Aarhus.
  2. Harin, Alexander, 2014. "General correcting formulae for forecasts," MPRA Paper 55283, University Library of Munich, Germany.
  3. Manabu Asai & Michael McAleer, 2014. "Forecasting Co-Volatilities via Factor Models with Asymmetry and Long Memory in Realized Covariance," Documentos de Trabajo del ICAE 2014-05, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
  4. Xiuping Mao & Esther Ruiz & Helena Veiga, 2013. "One for all : nesting asymmetric stochastic volatility models," Statistics and Econometrics Working Papers, Universidad Carlos III, Departamento de Estadística y Econometría ws131110, Universidad Carlos III, Departamento de Estadística y Econometría.
  5. Alexander HARIN, 2014. "Partially Unforeseen Events. Corrections and Correcting Formulae for Forecasts," Expert Journal of Economics, Sprint Investify, vol. 2(2), pages 69-79.

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