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Prévision avec des copules en finance

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  • Arthur Charpentier

    (CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR - Université de Rennes - CNRS - Centre National de la Recherche Scientifique)

Abstract

Cet article présente un survol les techniques usuelles de modélisation de séries nancières multiples. Pus spéciquement, on cherchera a obtenir une extention multivariée des modèles GARCH. Dans un premier temps, nous verrons comment modéliser la dynamique de la matrice de corrélation (conditionnelle), puis nous verrons comment généraliser cette approche à des lois conditionnelles plus générales, construites à l'aide de copules (et s'aranchir ainsi de l'hypothese de lois elliptiques). Les principaux concepts présentés dans cet article seront illustrés sur des séries de rendements de prix du pétrole (Brent, Dubaï et Maya).

Suggested Citation

  • Arthur Charpentier, 2015. "Prévision avec des copules en finance," Working Papers hal-01151233, HAL.
    • Handle: RePEc:hal:wpaper:hal-01151233
    Note: View the original document on HAL open archive server: https://hal.science/hal-01151233
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    References listed on IDEAS

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    Cited by:

    1. Johan Dahlin & Mattias Villani & Thomas B. Schon, 2015. "Bayesian optimisation for fast approximate inference in state-space models with intractable likelihoods," Papers 1506.06975, arXiv.org, revised Jun 2017.
    2. Ho, Anson T.Y. & Huynh, Kim P. & Jacho-Chávez, David T., 2019. "Using nonparametric copulas to measure crude oil price co-movements," Energy Economics, Elsevier, vol. 82(C), pages 211-223.

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