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Modelling volatility asymmetries: a Bayesian analysis of a class of tree structured multivariate GARCH models

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  • P. Dellaportas
  • I. D. Vrontos

Abstract

A new class of multivariate threshold GARCH models is proposed for the analysis and modelling of volatility asymmetries in financial time series. The approach is based on the idea of a binary tree where every terminal node parametrizes a (local) multivariate GARCH model for a specific partition of the data. A Bayesian stochastic method is developed and presented for the analysis of the proposed model consisting of parameter estimation, model selection and volatility prediction. A computationally feasible algorithm that explores the posterior distribution of the tree structure is designed using Markov chain Monte Carlo stochastic search methods. Simulation experiments are conducted to assess the performance of the proposed method, and an empirical application of the proposed model is illustrated using real financial time series. Copyright Royal Economic Society 2007

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Bibliographic Info

Article provided by Royal Economic Society in its journal Econometrics Journal.

Volume (Year): 10 (2007)
Issue (Month): 3 (November)
Pages: 503-520

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Handle: RePEc:ect:emjrnl:v:10:y:2007:i:3:p:503-520

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Cited by:
  1. Burda Martin & Maheu John M., 2013. "Bayesian adaptively updated Hamiltonian Monte Carlo with an application to high-dimensional BEKK GARCH models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(4), pages 345-372, September.
  2. Mark J. Jensen & John M. Maheu, 2012. "Bayesian semiparametric multivariate GARCH modeling," Working Paper, Federal Reserve Bank of Atlanta 2012-09, Federal Reserve Bank of Atlanta.
  3. Vrontos, Spyridon D. & Vrontos, Ioannis D. & Giamouridis, Daniel, 2008. "Hedge fund pricing and model uncertainty," Journal of Banking & Finance, Elsevier, vol. 32(5), pages 741-753, May.
  4. Meligkotsidou, Loukia & Vrontos, Ioannis D. & Vrontos, Spyridon D., 2009. "Quantile regression analysis of hedge fund strategies," Journal of Empirical Finance, Elsevier, Elsevier, vol. 16(2), pages 264-279, March.
  5. Giannikis, D. & Vrontos, I.D. & Dellaportas, P., 2008. "Modelling nonlinearities and heavy tails via threshold normal mixture GARCH models," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 52(3), pages 1549-1571, January.
  6. Martin Burda & John Maheu, 2011. "Bayesian Adaptive Hamiltonian Monte Carlo with an Application to High-Dimensional BEKK GARCH Models," Working Papers tecipa-438, University of Toronto, Department of Economics.

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