We consider the problem of a sum of two dependent and heavy tailed distributions through the C-convolution. The C-convolution provides the distribution of the sum of two random variables whose dependence structure is described by a copula function. Moreover, to investigate the role of heavy tails we use three different marginal distributions characterized by this property: Cauchy, Levy and Pareto. We show that the tail behavior of the C-convolution measured by level-q quantiles for q = 0.01, 0.05 (left tail) and q = 0.95, 0.99 (right tail) is strongly affected by the copula function which links the marginals and by the tail heaviness of marginals themselves
In the thesis the sums of dependent nonidentically distributed heavy-tailed random variables are inv...
Abstract. We propose nonparametric asymptotic condence intervals for the upper and lower tail depend...
This thesis focuses on the analysis of heavy-tailed distributions, which are widely applied to model...
We consider the problem of a sum of two dependent and heavy tailed distributions through the C-convo...
Abstract The tail dependence of multivariate distributions is frequently studied via the tool of cop...
The tail dependence of multivariate distributions is frequently studied via the tool of copulas. Thi...
AbstractThe multivariate regular variation (MRV) is one of the most important tools in modeling mult...
Much empirical work has shown that asset returns, exchange rates, operational risks, large insuranc...
Abstract: In this paper, we study the asymptotic behavior of the tail of X1 +X2 in a dependent frame...
Stochastic dependence arises in many fields including electrical grid reliability, network/internet ...
In many areas of interest, modern risk assessment requires estimation of the extremal behaviour of s...
This thesis presents the concept of tail dependence in a financial context as one tool to measure de...
We show that diversification does not reduce Value-at-Risk for a large class of dependent heavy tail...
International audienceThe tail copula is widely used to describe the dependence in the tail of multi...
Starting with a notion of positive dependence View the MathML source and with the family of the lowe...
In the thesis the sums of dependent nonidentically distributed heavy-tailed random variables are inv...
Abstract. We propose nonparametric asymptotic condence intervals for the upper and lower tail depend...
This thesis focuses on the analysis of heavy-tailed distributions, which are widely applied to model...
We consider the problem of a sum of two dependent and heavy tailed distributions through the C-convo...
Abstract The tail dependence of multivariate distributions is frequently studied via the tool of cop...
The tail dependence of multivariate distributions is frequently studied via the tool of copulas. Thi...
AbstractThe multivariate regular variation (MRV) is one of the most important tools in modeling mult...
Much empirical work has shown that asset returns, exchange rates, operational risks, large insuranc...
Abstract: In this paper, we study the asymptotic behavior of the tail of X1 +X2 in a dependent frame...
Stochastic dependence arises in many fields including electrical grid reliability, network/internet ...
In many areas of interest, modern risk assessment requires estimation of the extremal behaviour of s...
This thesis presents the concept of tail dependence in a financial context as one tool to measure de...
We show that diversification does not reduce Value-at-Risk for a large class of dependent heavy tail...
International audienceThe tail copula is widely used to describe the dependence in the tail of multi...
Starting with a notion of positive dependence View the MathML source and with the family of the lowe...
In the thesis the sums of dependent nonidentically distributed heavy-tailed random variables are inv...
Abstract. We propose nonparametric asymptotic condence intervals for the upper and lower tail depend...
This thesis focuses on the analysis of heavy-tailed distributions, which are widely applied to model...