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A Bayesian Non-Parametric Approach to Asymmetric Dynamic Conditional Correlation Model With Application to Portfolio Selection

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  • Audrone Virbickaite
  • M. Concepci'on Aus'in
  • Pedro Galeano

Abstract

We propose a Bayesian non-parametric approach for modeling the distribution of multiple returns. In particular, we use an asymmetric dynamic conditional correlation (ADCC) model to estimate the time-varying correlations of financial returns where the individual volatilities are driven by GJR-GARCH models. The ADCC-GJR-GARCH 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 Gaussian distributions allowing for a great flexibility in the return distribution in terms of skewness and kurtosis. Model estimation and prediction are developed using MCMC methods based on slice sampling techniques. We carry out a simulation study to illustrate the flexibility of the proposed approach. We find that the proposed DPM model is able to adapt to several frequently used distribution models and also accurately estimates the posterior distribution of the volatilities of the returns, without assuming any underlying distribution. Finally, we present a financial application using Apple and NASDAQ Industrial index data to solve a portfolio allocation problem. We find that imposing a restrictive parametric distribution can result into underestimation of the portfolio variance, whereas DPM model is able to overcome this problem.

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  • Audrone Virbickaite & M. Concepci'on Aus'in & Pedro Galeano, 2013. "A Bayesian Non-Parametric Approach to Asymmetric Dynamic Conditional Correlation Model With Application to Portfolio Selection," Papers 1301.5129, arXiv.org, revised Jan 2014.
  • Handle: RePEc:arx:papers:1301.5129
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    References listed on IDEAS

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    Cited by:

    1. Martina Danielova Zaharieva & Mark Trede & Bernd Wilfling, 2017. "Bayesian semiparametric multivariate stochastic volatility with an application to international stock-market co-movements," CQE Working Papers 6217, Center for Quantitative Economics (CQE), University of Muenster.
    2. repec:spr:annopr:v:260:y:2018:i:1:d:10.1007_s10479-017-2659-0 is not listed on IDEAS
    3. Audrone Virbickaite & Hedibert F. Lopes & Maria Concepción Ausín & Pedro Galeano, 2018. "Particle Learning for Bayesian Semi-Parametric Stochastic Volatility Model," DEA Working Papers 88, Universitat de les Illes Balears, Departament d'Economía Aplicada.
    4. repec:spr:stmapp:v:27:y:2018:i:2:d:10.1007_s10260-017-0384-0 is not listed on IDEAS
    5. Audrone Virbickaite & M. Concepción Ausín & Pedro Galeano, 2015. "Bayesian Inference Methods For Univariate And Multivariate Garch Models: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 29(1), pages 76-96, February.
    6. repec:gam:jecnmx:v:5:y:2017:i:2:p:18-:d:97715 is not listed on IDEAS

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