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Tail Dependence in Financial Markets: A Dynamic Copula Approach

Federico Pasquale Cortese ()
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Federico Pasquale Cortese: University of Milano—Bicocca, Piazza dell’Ateneo Nuovo 1, 20126 Milan, Italy

Risks, 2019, vol. 7, issue 4, 1-14

Abstract: This article is concerned with the study of the tail correlation among equity indices by means of dynamic copula functions. The main idea is to consider the impact of the use of copula functions in the accuracy of the model’s parameters and in the computation of Value-at-Risk (VaR). Results show that copulas provide more sophisticated results in terms of the accuracy of the forecasted VaR, in particular, if they are compared with the results obtained from Dynamic Conditional Correlation (DCC) model.

Keywords: copula functions; Monte Carlo simulation techniques; risk measures (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 M2 M4 K2 (search for similar items in EconPapers)
Date: 2019
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Handle: RePEc:gam:jrisks:v:7:y:2019:i:4:p:116-:d:285787