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

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  • Virbickaite, Audrone
  • Ausín Olivera, María Concepción
  • Galeano San Miguel, Pedro

Abstract

We use an asymmetric dynamic conditional correlation (ADCC) GJR-GARCH model to estimate the time-varying volatilities of financial returns. 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 flexible location-scale mixture of infinite Gaussian distributions and the inference and estimation is carried out by relying on Bayesian non-parametrics. Finally, we carry out a simulation study to illustrate the flexibility of the new method and present a financial application using Apple and NASDAQ Industrial index data to solve a portfolio allocation problem

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  • Virbickaite, Audrone & Ausín Olivera, María Concepción & Galeano San Miguel, Pedro, 2013. "A Bayesian non-parametric approach to asymmetric dynamic conditional correlation model with application to portfolio selection," DES - Working Papers. Statistics and Econometrics. WS ws131009, Universidad Carlos III de Madrid. Departamento de Estadística.
    • Handle: RePEc:cte:wsrepe:ws131009
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    Cited by:

    1. Sipan Aslan & Ceylan Yozgatligil & Cem Iyigun, 2018. "Temporal clustering of time series via threshold autoregressive models: application to commodity prices," Annals of Operations Research, Springer, vol. 260(1), pages 51-77, January.
    2. 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.
    3. Audronė Virbickaitė & Hedibert F. Lopes & M. Concepción Ausín & Pedro Galeano, 2019. "Particle learning for Bayesian semi-parametric stochastic volatility model," Econometric Reviews, Taylor & Francis Journals, vol. 38(9), pages 1007-1023, October.
    4. Donelli, Nicola & Peluso, Stefano & Mira, Antonietta, 2021. "A Bayesian semiparametric vector Multiplicative Error Model," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).
    5. Marc S. Paolella, 2017. "The Univariate Collapsing Method for Portfolio Optimization," Econometrics, MDPI, vol. 5(2), pages 1-33, May.
    6. 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.
    7. Nianling Wang & Lijie Zhang & Zhuo Huang & Yong Li, 2021. "Asymmetric Correlations in Predicting Portfolio Returns," International Review of Finance, International Review of Finance Ltd., vol. 21(1), pages 97-120, March.
    8. Jim Griffin & Maria Kalli & Mark Steel, 2018. "Discussion of “Nonparametric Bayesian Inference in Applications”: Bayesian nonparametric methods in econometrics," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(2), pages 207-218, June.
    9. Paolella, Marc S. & Polak, Paweł & Walker, Patrick S., 2019. "Regime switching dynamic correlations for asymmetric and fat-tailed conditional returns," Journal of Econometrics, Elsevier, vol. 213(2), pages 493-515.

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