A Bayesian non-parametric approach to asymmetric dynamic conditional correlation model with application to portfolio selection Articles uri icon

publication date

  • August 2016

start page

  • 814

end page

  • 829

volume

  • 100

international standard serial number (ISSN)

  • 0167-9473

electronic international standard serial number (EISSN)

  • 1872-7352

abstract

  • A Bayesian non-parametric approach for modeling the distribution of multiple returns is proposed. More specifically, an asymmetric dynamic conditional correlation (ADCC) model is considered to estimate the time-varying correlations of financial returns where the individual volatilities are driven by GJR-GARCH models. This composite model takes into consideration the asymmetries in individual assets' volatilities, as well as in the correlations. The errors are modeled using a Dirichlet location-scale mixture of multivariate Normals allowing for a flexible return distribution in terms of skewness and kurtosis. This gives rise to a Bayesian non-parametric ADCC (BNP-ADCC) model, as opposed to a symmetric specification, called BNP-DCC. Then these two models are compared using a sample of Apple Inc. and NASDAQ Industrial index daily returns. The obtained results reveal that for this particular data set the BNP-ADCC outperforms the BNP-DCC model. Finally, an illustrative asset allocation exercise is presented. (C) 2014 Elsevier B.V. All rights reserved.

keywords

  • bayesian analysis; dirichlet process mixtures; dcc; markov chain monte carlo; multivariate garch; portfolio allocation; stochastic volatility; mixture; heteroscedasticity; heteroskedasticity; inference; futures; priors; return; hedge