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Dynamic Econometric Models

Modeling the Dependence Structure of the WIG20 Portfolio Using a Pair-copula Construction
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Modeling the Dependence Structure of the WIG20 Portfolio Using a Pair-copula Construction

Authors

  • Ryszard Doman Adam Mickiewicz University in Poznań

DOI:

https://doi.org/10.12775/DEM.2010.003

Keywords

dependence, portfolio, copula, pair-copula construction

Abstract

Elliptical distributions commonly applied to modeling the returns of stocks in highdimensional  portfolio are not capable of adequate describing the dependence between the components  when their statistical properties are very diverse. The MGARCH and standard dynamic  copula models are often of little usefulness in such cases. In this paper, we apply a methodology  called the pair-copula decomposition to model the joint conditional distribution of the returns on  stocks constituting the WIG20 index, and show some advantage of this construction over the  approach using the t Student DCC model.

References

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Bauwens, E. Laurent, S. Rombouts, J.V.K. (2006), Multivariate GARCH Models: A Survey, Journal of Applied Econometrics, 21, 79–109. DOI: http://dx.doi.org/10.1002/jae.842

Bedford, T., Cooke, R.M. (2001), Probability Density Decomposition for Conditionally Dependent Random Variables Modeled by Vines, Annals of Mathematics and Artificial Intelligence, 32, 245–268.

Bedford, T., Cooke, R.M. (2002), Vines – a New Graphical Model for Dependent Random Variables, Annals of Statistics, 30, 1031–1068. DOI: http://dx.doi.org/10.1214/aos/1031689016

Christoffersen, P. F. (1998), Evaluating Interval Forecasts, International Economic Review, 39, 841–862. DOI: http://dx.doi.org/10.2307/2527341

Engle, R. F., (2002), Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models, Journal of Business & Economic Statistics, 20, 339–350. DOI: http://dx.doi.org/10.1198/073500102288618487

Genest, C. Ghoudi K. Rivest, L.-P. (1995), A Semiparametric Estimation Procedure of Dependence Parameters in Multivariate Families of Distributions, Biometrika, 82, 543–552.

Giot, P., Laurent, S. (2003), Value-at-Risk for Long and Short Trading Positions, Journal of Applied Econometrics, 18, 641–664. DOI: http://dx.doi.org/10.1002/jae.710

Joe, H. (1996), Families of m-variate Distributions with Given Margins and m(m - 1)/2 Bivariate Dependence Parameters. In: Rüschendorf, L., Schweizer, B.,Taylor, M.D. (Eds.), Distributions with Fixed Marginals and Related Topics, IMS Lecture Notes Monograph Series 28, Institute of Mathematical Statistics, Hayward, CA, 120–141.

Joe, H. (1997), Multivariate Models and Dependence Concepts, Chapman & Hall, London.

Kurowicka, D., Cooke, R. M., (2006), Uncertainty Analysis with High Dimensional Dependence Modelling, Wiley, New York. DOI: http://dx.doi.org/10.1002/0470863072

Lambert, P., Laurent, S. (2001), Modelling Financial Time Series Using GARCH-type Models with a Skewed Student Distribution for the Innovations, Institut de Statistique, Université Catholique de Louvain, Discussion Paper 0125.

Nelsen, R. B. (2006) An Introduction to Copulas (2nd ed.), Springer, New York. DOI: http://dx.doi.org/10.1007/978-1-4757-3076-0

Sklar, A. (1959), Fonctions de rérpartition à n dimensions et leurs marges, Publications de l’Institut Statistique de l’Université de Paris, 8, 229–231.

Dynamic Econometric Models

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Published

2010-07-16

How to Cite

1.
DOMAN, Ryszard. Modeling the Dependence Structure of the WIG20 Portfolio Using a Pair-copula Construction. Dynamic Econometric Models. Online. 16 July 2010. Vol. 10, pp. 31-42. [Accessed 7 July 2025]. DOI 10.12775/DEM.2010.003.
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