Change analysis of dynamic copula for measuring dependence in multivariate financial data

Dominique Guegan () and Jing Zhang ()
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Dominique Guegan: CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique
Jing Zhang: CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, ECNU - East China Normal University [Shangaï]

Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) from HAL

Abstract: This paper proposes a new approach to measure the dependence in multivariate financial data. Data in finance and insurance often cover a long time period. Therefore, the economic factors may induce some changes inside the dependence structure. Recently, two methods using copulas have been proposed to analyze such changes. The first approach investigates the changes of copula's parameters. The second one tests the changes of copulas by determining the best copulas using moving windows. In this paper we take into account the non stationarity of the data and analyze : (1) the changes of parameters while the copula family keeps static ; (2) the changes of copula family. We propose a series of tests based on conditional copulas and goodness-of-fit (GOF) tests to decide the type of change, and further give the corresponding change analysis. We illustrate our approach with Standard & Poor 500 and Nasdaq indices, and provide dynamic risk measures.

Keywords: change-point; VaR; ES; Dynamic copula; goodness-of-fit test; time-varying parameter (search for similar items in EconPapers)
Date: 2006-07
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-00189141v2
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Citations: View citations in EconPapers (5)

Published in 2006

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Related works:
Working Paper: Change analysis of a dynamic copula for measuring dependence in multivariate financial data (2010)
Working Paper: Change analysis of dynamic copula for measuring dependence in multivariate financial data (2006)
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