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Inference for some multivariate ARCH and GARCH models

delete2003-11-27
delete15
PRE
AI
I
Ioannis D. Vrontos
D
Dellaportas, R
D
Dimitris N. Politis
DOI：10.1002/for.871delete
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Abstract

Abstract

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Multivariate time-varying volatility models have attracted a lot of attention in modem finance theory. We provide an empirical study of some multivariate ARCH and GARCH models that already exist in the literature and have attracted a lot of practical interest. Bayesian and classical techniques are used for the estimation of the parameters of the models and model comparisons are addressed via predictive distributions. We provide implementation details and illustrations using daily exchange rates of the Athens exchange market. Copyright (C) 2003 John Wiley Sons, Ltd.
Keywords:
autoregressive conditional heteroscedasticity
Markov chain Monte Carlo
maximum likelihood
model comparison
predictive distribution
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Journal

Journal of Forecasting cover
Journal of Forecasting
IF:
2.7
Papers:
2.3K
Citations:
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