Parametric conditional copula models allow the copula parameters to vary with a set of covariates according to an unknown calibration function. In this paper we develop a flexible Bayesian method to estimate the calibration function of a bivariate conditional copula. We construct a prior distribution over the set of smooth calibration functions using a sparse Gaussian process (GP) prior for the...

Copula modelling has become ubiquitous in modern statistics. Here, the problem of nonparametrically estimating a copula density is addressed. Arguably the most popular nonparametric density estimator, the kernel estimator is not suitable for the unit-square-supported copula densities, mainly because it is heavily a↵ected by boundary bias issues. In addition, most common copulas admit unbounded ...

The study of dependence between random variables is a mainstay in statistics. In many cases, the strength of dependence between two or more random variables varies according to the values of a measured covariate. We propose inference for this type of variation using a conditional copula model where the copula function belongs to a parametric copula family and the copula parameter varies with th...

In this paper we propose a copula-based technique to recover the distribution of actively managed funds. The copula is meant to represent the dependence structure between the market return (or in general the benchmark) and the investment strategy of the asset manager. The analysis is carried out in a rational investor economy with managed funds, such as that in Merton (1981) and Berk and Green ...

A continuous random vector (X,Y) uniquely determines a copula C : [0,1]2 → [0,1] such that when the distribution functions of X and Y are properly composed into C , the joint distribution function of (X,Y) results. A copula is said to be D4-invariant if its mass distribution is invariant with respect to the symmetries of the unit square. A D4-invariant copula leads naturally to a family of meas...

In insurance applications yearly claim totals of different coverage fields are often dependent. In many cases there are numerous claim totals which are zero. A marginal claim distribution will have an additional point mass at zero, hence this probability function (pf) will not be continuous at zero and the cumulative distribution functions will not be uniform. Therefore using a copula approach ...

We propose an algorithm for the computation of the volume of a multivariate copula function (and the probability distribution of the counting variable linked to this multidimensional copula function) which is very complex for large dimensions. As it is common practice for large dimensional problem, we restrict to positive orthant dependence and we construct a Hierarchical copula which describes...

In this paper, we provide two one-factor heavy-tailed copula models for pricing a collateralized debt obligation and credit default index swap tranches: (1) a one-factor double t distribution with fractional degrees of freedom copula model and (2) a one-factor double mixture distribution of t and Gaussian distribution copula model. A tail-fatness parameter is introduced in each model, allowing ...

Tail dependence and conditional tail dependence functions describe, respectively, the tail probabilities and conditional tail probabilities of a copula at various relative scales. The properties as well as the interplay of these two functions are established based upon their homogeneous structures. The extremal dependence of a copula, as described by its extreme value copulas, is shown to be co...

Issues of missing data have become increasingly serious with the rapid increase in usage of traffic sensors. Analyses of the Beijing ring expressway have showed that up to 50% of microwave sensors pose missing values. The imputation of missing traffic data must be urgently solved although a precise solution that cannot be easily achieved due to the significant number of missing portions. In thi...