Abstract
Copula functions are mathematical tools that have been used in finance for approximately ten years. Their main selling point is to separate the dependence function (copula) from the marginal distributions. A little over a decade after the rise of copula modelling in finance, this article provides an initial assessment of their application in financial contexts. More specifically, the main purpose of this paper is to contribute to an ongoing debate in the field: the choice of copulas. Through an empirical study of two composite stock indices (S&P 500 and CAC 40) daily returns over the period 2002-2011, we show that this methodological challenge is still unsolved. With this in view, we suggest a method that enables to capture implicitly the empirical dependence structure without assuming any specific parametric form for it.
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