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

Audronė Virbickaitė, M. Concepción Ausín and Pedro Galeano

Computational Statistics & Data Analysis, 2016, vol. 100, issue C, 814-829

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.

Keywords: Bayesian analysis; Dirichlet process mixtures; DCC; Markov chain Monte Carlo; Multivariate GARCH; Portfolio allocation (search for similar items in EconPapers)
Date: 2016
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Related works:
Working Paper: A Bayesian Non-Parametric Approach to Asymmetric Dynamic Conditional Correlation Model With Application to Portfolio Selection (2014) Downloads
Working Paper: A Bayesian non-parametric approach to asymmetric dynamic conditional correlation model with application to portfolio selection (2013) Downloads
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