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The Gaussian Mixture Dynamic Conditional Correlation Model

Parameter Estimation, Value at Risk Calculation, and Portfolio Selection

Bibliographic Data

ID19418249
AuthorsPedro Galeano (0000-0003-2577-2747, Universidad Carlos III de Madrid), M Concepción Ausín (0000-0003-0904-6542, Universidad Carlos III de Madrid)
Year2010
Volume28
Issue4
Pages559-571
Publication date2010-10-01
Open AccessNo
TypeARTICLE
VenueJournal of Business and Economic Statistics (JOURNAL)
Journal identifiersISSN: 0735-0015 • E-ISSN: 1537-2707
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1198/jbes.2009.07238
OpenAlexW2011090480
LanguageEN
Citations received1
References cited25

A multivariate generalized autoregressive conditional heteroscedasticity model with dynamic conditional correlations is proposed, in which the individual conditional volatilities follow exponential generalized autoregressive conditional heteroscedasticity models and the standardized innovations follow a mixture of Gaussian distributions. Inference on the model parameters and prediction of future volatilities are addressed by both maximum likelihood and Bayesian estimation methods. Estimation of the Value at Risk of a given portfolio and selection of optimal portfolios under the proposed specification are addressed. The good performance of the proposed methodology is illustrated via Monte Carlo experiments and the analysis of the daily closing prices of the Dow Jones and NASDAQ indexes

Autoregressive conditional heteroskedasticity · Autoregressive model · Conditional variance · Econometrics · Economics · Heteroscedasticity · Model selection · Portfolio · Risk management · Statistics · Value at risk · Financial Risk and Volatility Modeling · Market Dynamics and Volatility · Mathematics · Statistical Distribution Estimation and Applications · Finance

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Citations per year0.13
Citation span2018 - 2018 (1)
Highly citedNo

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