This paper details the correspondence between various dependence concepts and stochastic orderings for an Archimedean copula Cφ(x, y) = φ −1{φ(x) + φ(y)} and the aging properties of the corresponding life distribution Fφ(t) = 1− φ−1(t). Various applications of the results are given. AMS (2000) subject classification. Primary 62H05, 62H20; secondary 62N05.

The characteristic of copula is non strict on certain distribution assumptions, can explain nonlinier relationship, and easily construct through the marginals that do not need to come from same family. Copula will be useful for stock data has price charts fluctuate rapidly risk always follow in investing. relation between this study calculate value portfolio using VaR with generation Monte Carl...

This paper presents the role of copula functions in the theory of aggregation operators and an axiomatic characterization of Archimedean aggregation functions. In this context we are focusing our attention about several properties of aggregation functions, like supermodularity and Schur-concavity.

Companies in the same industry sector are usually stronger correlated than firms in different sectors, as they are similarly affected by macroeconomic effects, political decisions, and consumer trends. In spite of many stock return models taking account of this fact there are only a few credit default models taking it into consideration. In this paper we present a default model based on nested ...

Abstract In this article, a new reciprocal Rayleigh extension called the Xgamma model is defined and studied. The relevant statistical properties are derived, useful results related to convexity concavity addressed. We discussed estimation of parameters using different methods such as maximum likelihood method, ordinary least squares weighted Cramer-Von-Mises bootstrapping method. A simulation ...

The copula–entropy theory combines the entropy theory and the copula theory. The entropy theory has been extensively applied to derive the most probable univariate distribution subject to specified constraints by applying the principle of maximum entropy. With the flexibility to model nonlinear dependence structure, parametric copulas (e.g., Archimedean, extreme value, meta-elliptical, etc.) ha...

Parametric conditional copulamodels allow the copula parameters to vary with a set of covariates according to an unknown calibration function. Flexible Bayesian inference for the calibration function of a bivariate conditional copula is introduced. The prior distribution over the set of smooth calibration functions is built using a sparse Gaussian process (GP) prior for the single index model (...

In this paper, we address risk aggregation and capital allocation problems in the presence of dependence between risks. The structure is defined by a mixed Bernstein copula which represents generalization well-known Archimedean copulas. Using new copula, probability density function cumulative distribution aggregate are obtained. Then, closed-form expressions for basic measures, such as tail va...

In the classical multivariate time series models the residuals are assumed to be normally distributed. However the assumption of normality is rarely consistent with the empirical evidence and leads to possibly incorrect inferences from financial models. The copula theory allows us to extend the classical time series models to nonelliptically distributed residuals. In this paper we analyze the t...