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L'analyse dynamique des dépendances

  • Zhun Peng

    (UP1 UFR02 - Université Paris 1, Panthéon-Sorbonne - UFR d'Économie - Université Paris I - Panthéon Sorbonne)

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    Pour de nombreuses théories financières comme l'allocation d'actifs, la gestion de risque et l'évaluation des produits dérivés, la dépendance entre les facteurs de risque occupe une position cruciale. L'allocation d'actifs et l'évaluation des risques s'appuient sur des corrélations, dans ce cas, un grand nombre de corrélations sont souvent nécessaires. L'étude de notre article suit l'approche du modèle "CAVA" et l'applique dans l'analyse des risques d'un portefeuille international. Dans la section 2, nous commençons par un rappel de la théorie de la copule puis introduisons la décomposition d'une densité multivariée CAVA en produits des densités marginales et des densités de copule variée. La section 3 présente le modèle "CAVA". Dans la section 4, nous décrivons les deux approches de la dépendance dynamique. La section 5 donne une révision sur les domaines GARCH. La section 6 montre comment estimer les paramètres du modèle. La section 7 traite de la spécification du modèle. Dans la section 8, nous appliquons la méthodologie et discutons les résultats. Enfin, la section 9 donne des conclusions et résultats.

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    Paper provided by HAL in its series Post-Print with number dumas-00651795.

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    Date of creation: 30 Jun 2011
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    Handle: RePEc:hal:journl:dumas-00651795
    Note: View the original document on HAL open archive server: http://dumas.ccsd.cnrs.fr/dumas-00651795/en/
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    1. Aas, Kjersti & Czado, Claudia & Frigessi, Arnoldo & Bakken, Henrik, 2009. "Pair-copula constructions of multiple dependence," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 182-198, April.
    2. Andrew Patton, 2004. "Modelling Asymmetric Exchange Rate Dependence," Working Papers wp04-04, Warwick Business School, Finance Group.
    3. Andrew J. Patton, 2004. "On the Out-of-Sample Importance of Skewness and Asymmetric Dependence for Asset Allocation," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 2(1), pages 130-168.
    4. HEINEN, Andréas & VALDESOGO, Alfonso, 2009. "Asymmetric CAPM dependence for large dimensions: the Canonical Vine Autoregressive Model," CORE Discussion Papers 2009069, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    5. Andrew J. Patton, 2008. "Copula-Based Models for Financial Time Series," OFRC Working Papers Series 2008fe21, Oxford Financial Research Centre.
    6. Hurlimann, Werner, 2004. "Fitting bivariate cumulative returns with copulas," Computational Statistics & Data Analysis, Elsevier, vol. 45(2), pages 355-372, March.
    7. Hansen, Bruce E, 1994. "Autoregressive Conditional Density Estimation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 35(3), pages 705-30, August.
    8. Jondeau, Eric & Rockinger, Michael, 2006. "The Copula-GARCH model of conditional dependencies: An international stock market application," Journal of International Money and Finance, Elsevier, vol. 25(5), pages 827-853, August.
    9. Huang, Jen-Jsung & Lee, Kuo-Jung & Liang, Hueimei & Lin, Wei-Fu, 2009. "Estimating value at risk of portfolio by conditional copula-GARCH method," Insurance: Mathematics and Economics, Elsevier, vol. 45(3), pages 315-324, December.
    10. Andrew J. Patton, 2006. "Estimation of multivariate models for time series of possibly different lengths," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(2), pages 147-173.
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