# 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, pages 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)

Working Paper: A Bayesian non-parametric approach to asymmetric dynamic conditional correlation model with application to portfolio selection (2013)

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**Persistent link:** http://EconPapers.repec.org/RePEc:eee:csdana:v:100:y:2016:i:c:p:814-829

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