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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

    ()

  • Concepción Ausín

    ()

  • Pedro Galeano

    ()

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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Bibliographic Info

Paper provided by Universidad Carlos III, Departamento de Estadística y Econometría in its series Statistics and Econometrics Working Papers with number ws131009.

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Date of creation: May 2013
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Handle: RePEc:cte:wsrepe:ws131009

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Keywords: Asymmetric dynamic condition correlation; Bayesian non-parametrics; Dirichlet process mixtures; Portfolio allocation;

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  1. Hun Y. Park & Anil K. Bera, 1987. "Interest-Rate Volatility, Basis Risk and Heteroscedasticity in Hedging Mortgages," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 15(2), pages 79-97.
  2. Annastiina Silvennoinen & Timo Teräsvirta, 2008. "Multivariate GARCH models," CREATES Research Papers 2008-06, School of Economics and Management, University of Aarhus.
  3. Lorenzo Cappiello & Robert F. Engle & Kevin Sheppard, 2006. "Asymmetric Dynamics in the Correlations of Global Equity and Bond Returns," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 4(4), pages 537-572.
  4. Mark J Jensen & John M Maheu, 2012. "Bayesian semiparametric multivariate GARCH modeling," Working Papers tecipa-458, University of Toronto, Department of Economics.
  5. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, 03.
  6. Giamouridis, Daniel & Vrontos, Ioannis D., 2007. "Hedge fund portfolio construction: A comparison of static and dynamic approaches," Journal of Banking & Finance, Elsevier, vol. 31(1), pages 199-217, January.
  7. Kroner, Kenneth F. & Sultan, Jahangir, 1993. "Time-Varying Distributions and Dynamic Hedging with Foreign Currency Futures," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 28(04), pages 535-551, December.
  8. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. " On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
  9. Ardia, David & Hoogerheide, Lennart F., 2010. "Efficient Bayesian estimation and combination of GARCH-type models," MPRA Paper 22919, University Library of Munich, Germany.
  10. Omiros Papaspiliopoulos & Gareth O. Roberts, 2008. "Retrospective Markov chain Monte Carlo methods for Dirichlet process hierarchical models," Biometrika, Biometrika Trust, vol. 95(1), pages 169-186.
  11. Stephen G. Cecchetti & Robert E. Cumby & Stephen Figlewski, 1986. "Estimation of the optimal futures hedge," Research Working Paper 86-10, Federal Reserve Bank of Kansas City.
  12. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
  13. Eduardo Rossi & Claudio Zucca, 2002. "Hedging interest rate risk with multivariate GARCH," Applied Financial Economics, Taylor & Francis Journals, vol. 12(4), pages 241-251.
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