Copula functions have been introduced recently in finance. They are a general tool to construct multivariate distributions and to investigate dependence structure between random variables. In this paper, we show that copula functions may be extensively used to solve many financial problems

copula. We describe an alternative approach that uses “probability–proportional–to–size ” (PPS) random sampling with weights formed from the copula likelihood function. The method is flexible and can be applied to parametric and nonparametric marginal density estimates. The precision of the simulation

is invalid under Boolean algebra. We introduce models of special functions such that, OML-Copula, and OML-Co-Copula.

This Thesis examines Copula functions, and their use in pricing baskets of credit derivatives, and pricing Collateralized Debt Obligations (CDO) in particular. The goal is to collect from the literature and bring into one body of work, all the steps involved when pricing a CDO tranche. The most

copula model non-trivial and is where a nonparametric estimator can play a significant role. In this paper, we investigate a kernel estimator which is mean square consistent everywhere in the support of the copula function. The kernel estimator is then used to formulate a goodness-of-fit test

chi ve of S

A useful method for representing Bayesian classifiers is through discriminant functions. Here, using copula functions, we propose a new model for discriminants. This model provides a rich and generalized class of decision boundaries. These decision boundaries significantly boost the classification

. This paper aims to improve the current setting of academic warning through Monte Carlo simulation of multivariate Copula functions, and to calculate more reasonable academic warning credit line. The result demonstrates that the accuracy is significantly improved, therefore, this approach can provide a new

Correlation structure of a multidimensional probability distribution captures only a small fraction of information on components dependence. To overcome the lack of information copula functions were intensively used during recent decades. The present paper gives an overview of copula functions

with the densities fi(t) and a copula function. The proposed model includes the traditional mixture model, the polyhazard and the fraction of cure models. Real applications are used to illustrate the model.