In this paper, we use copula-GARCH models applied to daily data from March 2010 to March 2018 to test the time-varying dependence of the Liv-ex 50, a secondary market fine wine index comprised of the ten most recent vintages of the five Bordeaux First Growths, with a portfolio composed of the six main stock markets (S&P 500, CAC 40, DAX 30, FTSE 100, and Hang Seng). Our results suggest that the Liv-ex 50 underperforms the six stock indexes, but provides diversification benefits in terms of volatility, asymmetry, and extreme events. (JEL Classifications: G110, G120, Q14)
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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