IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Log in (now much improved!) to save this article

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

Listed author(s):
  • Virbickaitė, Audronė
  • Ausín, M. Concepción
  • Galeano, Pedro

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.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://www.sciencedirect.com/science/article/pii/S0167947314003430
Download Restriction: Full text for ScienceDirect subscribers only.

As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

Volume (Year): 100 (2016)
Issue (Month): C ()
Pages: 814-829

as
in new window

Handle: RePEc:eee:csdana:v:100:y:2016:i:c:p:814-829
DOI: 10.1016/j.csda.2014.12.005
Contact details of provider: Web page: http://www.elsevier.com/locate/csda

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

as
in new window


  1. 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.
  2. Galeano, Pedro & Ausín, M. Concepción, 2010. "The Gaussian Mixture Dynamic Conditional Correlation Model: Parameter Estimation, Value at Risk Calculation, and Portfolio Selection," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(4), pages 559-571.
  3. Mencía, Javier & Sentana, Enrique, 2009. "Multivariate location-scale mixtures of normals and mean-variance-skewness portfolio allocation," Journal of Econometrics, Elsevier, vol. 153(2), pages 105-121, December.
  4. Silvennoinen, Annastiina & Teräsvirta, Timo, 2007. "Multivariate GARCH models," SSE/EFI Working Paper Series in Economics and Finance 669, Stockholm School of Economics, revised 18 Jan 2008.
  5. Wenling Yang & David E. Allen, 2005. "Multivariate GARCH hedge ratios and hedging effectiveness in Australian futures markets," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 45(2), pages 301-321.
  6. Cecchetti, Stephen G & Cumby, Robert E & Figlewski, Stephen, 1988. "Estimation of the Optimal Futures Hedge," The Review of Economics and Statistics, MIT Press, vol. 70(4), pages 623-630, November.
  7. Jensen, Mark J. & Maheu, John M., 2013. "Bayesian semiparametric multivariate GARCH modeling," Journal of Econometrics, Elsevier, vol. 176(1), pages 3-17.
  8. Robert Engle, 2002. "New frontiers for arch models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 425-446.
  9. Rossi, E. & Spazzini, F., 2010. "Model and distribution uncertainty in multivariate GARCH estimation: A Monte Carlo analysis," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2786-2800, November.
  10. Bauwens, Luc & Laurent, Sebastien, 2005. "A New Class of Multivariate Skew Densities, With Application to Generalized Autoregressive Conditional Heteroscedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 346-354, July.
  11. Jensen, Mark J. & Maheu, John M., 2010. "Bayesian semiparametric stochastic volatility modeling," Journal of Econometrics, Elsevier, vol. 157(2), pages 306-316, August.
  12. Sébastien Laurent & Luc Bauwens & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109.
  13. 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.
  14. Maria Kalli & Stephen G. Walker & Paul Damien, 2013. "Modeling the Conditional Distribution of Daily Stock Index Returns: An Alternative Bayesian Semiparametric Model," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(4), pages 371-383, October.
  15. Ausin, Maria Concepcion & Galeano, Pedro, 2007. "Bayesian estimation of the Gaussian mixture GARCH model," Computational Statistics & Data Analysis, Elsevier, vol. 51(5), pages 2636-2652, February.
  16. David Ardia & Lennart F. Hoogerheide, 2010. "Efficient Bayesian Estimation and Combination of GARCH-Type Models," Tinbergen Institute Discussion Papers 10-046/4, Tinbergen Institute.
  17. 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.
  18. Eduardo Rossi & Claudio Zucca, 2002. "Hedging interest rate risk with multivariate GARCH," Applied Financial Economics, Taylor & Francis Journals, vol. 12(4), pages 241-251.
  19. Ausín, M. Concepción & Galeano, Pedro & Ghosh, Pulak, 2014. "A semiparametric Bayesian approach to the analysis of financial time series with applications to value at risk estimation," European Journal of Operational Research, Elsevier, vol. 232(2), pages 350-358.
  20. 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.
  21. 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.
  22. Christodoulakis, George A. & Satchell, Stephen E., 2002. "Correlated ARCH (CorrARCH): Modelling the time-varying conditional correlation between financial asset returns," European Journal of Operational Research, Elsevier, vol. 139(2), pages 351-370, June.
  23. Daniel B. Nelson, 1994. "Asymptotically Optimal Smoothing with ARCH Models," NBER Technical Working Papers 0161, National Bureau of Economic Research, Inc.
  24. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
  25. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
  26. Jorion, Philippe, 1986. "Bayes-Stein Estimation for Portfolio Analysis," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 21(03), pages 279-292, September.
  27. Greyserman, Alex & Jones, Douglas H. & Strawderman, William E., 2006. "Portfolio selection using hierarchical Bayesian analysis and MCMC methods," Journal of Banking & Finance, Elsevier, vol. 30(2), pages 669-678, February.
  28. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
  29. Jensen, Mark J. & Maheu, John M., 2014. "Estimating a semiparametric asymmetric stochastic volatility model with a Dirichlet process mixture," Journal of Econometrics, Elsevier, vol. 178(P3), pages 523-538.
  30. Doron Avramov & Guofu Zhou, 2010. "Bayesian Portfolio Analysis," Annual Review of Financial Economics, Annual Reviews, vol. 2(1), pages 25-47, December.
  31. 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.
  32. Fiorentini, Gabriele & Sentana, Enrique & Calzolari, Giorgio, 2003. "Maximum Likelihood Estimation and Inference in Multivariate Conditionally Heteroscedastic Dynamic Regression Models with Student t Innovations," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(4), pages 532-546, October.
  33. Delatola, E.-I. & Griffin, J.E., 2013. "A Bayesian semiparametric model for volatility with a leverage effect," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 97-110.
  34. 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.
  35. Merton, Robert C., 1972. "An Analytic Derivation of the Efficient Portfolio Frontier," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 7(04), pages 1851-1872, September.
  36. Tse, Y K & Tsui, Albert K C, 2002. "A Multivariate Generalized Autoregressive Conditional Heteroscedasticity Model with Time-Varying Correlations," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 351-362, July.
  37. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:100:y:2016:i:c:p:814-829. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu)

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.