A Bayesian Non-Parametric Approach to Asymmetric Dynamic Conditional Correlation Model With Application to Portfolio Selection
AbstractWe 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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Bibliographic InfoPaper provided by arXiv.org in its series Papers with number 1301.5129.
Date of creation: Jan 2013
Date of revision: Jan 2014
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Web page: http://arxiv.org/
Other versions of this item:
- Audrone Virbickaite & ConcepciÃ³n AusÃn & Pedro Galeano, 2013. "A Bayesian non-parametric approach to asymmetric dynamic conditional correlation model with application to portfolio selection," Statistics and Econometrics Working Papers, Universidad Carlos III, Departamento de EstadÃstica y EconometrÃa ws131009, Universidad Carlos III, Departamento de EstadÃstica y EconometrÃa.
- NEP-ALL-2013-01-26 (All new papers)
- NEP-ECM-2013-01-26 (Econometrics)
- NEP-ETS-2013-01-26 (Econometric Time Series)
- NEP-RMG-2013-01-26 (Risk Management)
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- 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, Cambridge University Press, vol. 28(04), pages 535-551, December.
- Annastiina Silvennoinen & Timo TerÃ¤svirta, 2008.
"Multivariate GARCH models,"
CREATES Research Papers
2008-06, School of Economics and Management, University of Aarhus.
- 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, Elsevier, vol. 52(1-2), pages 5-59.
- Mark J Jensen & John M Maheu, 2012.
"Bayesian semiparametric multivariate GARCH modeling,"
tecipa-458, University of Toronto, Department of Economics.
- Jensen, Mark J. & Maheu, John M., 2013. "Bayesian semiparametric multivariate GARCH modeling," Journal of Econometrics, Elsevier, Elsevier, vol. 176(1), pages 3-17.
- Mark J. Jensen & John M. Maheu, 2012. "Bayesian semiparametric multivariate GARCH modeling," Working Paper, Federal Reserve Bank of Atlanta 2012-09, Federal Reserve Bank of Atlanta.
- Mark J. Jensen & John M. Maheu, 2012. "Bayesian Semiparametric Multivariate GARCH Modeling," Working Paper Series, The Rimini Centre for Economic Analysis 48_12, The Rimini Centre for Economic Analysis.
- Ardia, David & Hoogerheide, Lennart F., 2010.
"Efficient Bayesian estimation and combination of GARCH-type models,"
22919, University Library of Munich, Germany.
- David Ardia & Lennart F. Hoogerheide, 2010. "Efficient Bayesian Estimation and Combination of GARCH-Type Models," Tinbergen Institute Discussion Papers 10-046/4, Tinbergen Institute.
- 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.
- Sheppard, Kevin & Cappiello, Lorenzo & Engle, Robert F., 2003. "Asymmetric dynamics in the correlations of global equity and bond returns," Working Paper Series, European Central Bank 0204, European Central Bank.
- Stephen G. Cecchetti & Robert E. Cumby & Stephen Figlewski, 1986.
"Estimation of the optimal futures hedge,"
Research Working Paper, Federal Reserve Bank of Kansas City
86-10, Federal Reserve Bank of Kansas City.
- Eduardo Rossi & Claudio Zucca, 2002. "Hedging interest rate risk with multivariate GARCH," Applied Financial Economics, Taylor & Francis Journals, Taylor & Francis Journals, vol. 12(4), pages 241-251.
- Giamouridis, Daniel & Vrontos, Ioannis D., 2007. "Hedge fund portfolio construction: A comparison of static and dynamic approaches," Journal of Banking & Finance, Elsevier, Elsevier, vol. 31(1), pages 199-217, January.
- 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, American Real Estate and Urban Economics Association, vol. 15(2), pages 79-97.
- Omiros Papaspiliopoulos & Gareth O. Roberts, 2008. "Retrospective Markov chain Monte Carlo methods for Dirichlet process hierarchical models," Biometrika, Biometrika Trust, Biometrika Trust, vol. 95(1), pages 169-186.
- Lawrence R. Glosten & Ravi Jagannathan & David E. Runkle, 1993.
"On the relation between the expected value and the volatility of the nominal excess return on stocks,"
Staff Report, Federal Reserve Bank of Minneapolis
157, Federal Reserve Bank of Minneapolis.
- 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, American Finance Association, vol. 48(5), pages 1779-1801, December.
- Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, American Finance Association, vol. 7(1), pages 77-91, 03.
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