Copulae as a new tool in financial modelling

The paper presents an overview of financial applications of copulas. Copulas permit to represent joint distribution functions by splitting the marginal behavior, embedded in the marginal distributions, from the dependence, captured by the copula itself. The splitting proves to be very helpful not only in the modelling phase, but also in the estimation or simulation one. Essentially, it provides a straighforward way to extend financial modelling from the usual joint normality assumption to more general joint distributions, even preserving the normality assumption on the marginals. The paper puts into evidence the advantages of the copula representation with respect to the joint distribution one, with special reference to applications in pricing and risk measurement. Copula functions have a long history in Probability theory, since they date back to [Sklar (1959)]. They have been studied under a number of different names, such as t-norms, dependence functions, doublystochastic measures, ...

• Type: Journal
• Year: 2002
• Where: OR
• Authors: Elisa Luciano, Marina Marena
• Keywords: Joint Distribution, Joint Distribution Functions, Normality Assumption, Operations Research, OR 2002