Modelling nonlinearities and heavy tails via threshold normal mixture GARCH models

Authors:
D. Giannikis;I. D. Vrontos;P. Dellaportas
Affiliations:
Department of Statistics, Athens University of Economics and Business, Patission 76, 10434 Athens, Greece;Department of Statistics, Athens University of Economics and Business, Patission 76, 10434 Athens, Greece;Department of Statistics, Athens University of Economics and Business, Patission 76, 10434 Athens, Greece
Venue:
Computational Statistics & Data Analysis
Year:
2008

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Abstract

A new class of flexible threshold normal mixture GARCH models is proposed for the analysis and modelling of the stylized facts appeared in many financial time series. A Bayesian stochastic method is developed and presented for the analysis of the proposed model allowing for automatic model determination and estimation of the thresholds and their unknown number. A computationally feasible algorithm that explores the posterior distribution of the threshold models is designed using Markov chain Monte Carlo stochastic search methods. A simulation study is conducted to assess the performance of the proposed method, and an empirical application of the proposed model is illustrated using real data.