Dependence modeling for multi‐type recurrent events via copulas

Dependence Modeling of Joint Extremes via Copulas:

Recent empirical evidence highlights the importance of asymmetries in the distribution of asset returns in both their marginal behavior in terms of skewness and their dependence structure in that assets tend to be more highly correlated during bear markets than during market upturns. In this paper we develop a model that is able to address both features of the data based on the construction of ...

Multivariate dependence modeling using copulas

In this contribution we review models for construction of higher dimensional dependence that have arisen recent years. In particular we focus on specific generalized Farlie Gumbel (or Sarmanov) copulas which are generated by a single function (so-called generator or generator function) defined on the unit interval. An alternative approach to generalize the FGM family of copulas is to consider t...

Modeling dependence between loss triangles using copulas

A critical problem in property and casualty insurance is forecasting payments for incurred but not yet finalized claims. Forecasts and associated risk margins are often based on loss triangles with each triangle corresponding to a different line of business. Each triangle displays the development over time of all payments associated with claims originating in each year and for a given line of b...

Modeling tail dependence using copulas — literature review

Modeling dependence between random variables using copulas

The concept of copulas is normaly used to model the dependence structure between two or more random variables. Random variables are transformed to the unit interval I = [0, 1] by using quasi-inverse transformation. As a result we get a normalised multivariate distribution function called the copula. Copulas uniquely determine the dependence structure of multiple random variables. The aim of the...