Abstract: The Multivariate Extreme Value distributions have shown their usefulness in environmental studies, financial and insurance mathematics. The Logistic or Gumbel-Hougaard distribution is one of the oldest multivariate extreme value models and it has been extended to asymmetric models. In this paper we introduce generalized logistic multivariate distributions. Our tools are mixtures of co...

The Multivariate Extreme Value distributions have shown their usefulness in environmental studies, financial and insurance mathematics. The Logistic or Gumbel–Hougaard distribution is one of the oldest multivariate extreme value models and it has been extended to asymmetric models. In this paper we introduce generalized logistic multivariate distributions. Our tools are mixtures of copulas and ...

Hougaard et al.(2008) report a case series on a mixed individual and group treatment for social phobia, suggesting that the treatment is efficacious. As a group who has just embarked on a similar training program, we comment on their endeavor from the teacher/student perspective with an eye towards pedagogy in training students to conduct CBT. Thoughts and questions regarding the treatment, the...

Modeling the joint tails of multiple financial time series has many important implications for risk management. Classical models dependence often encounter a lack fit in tails, calling additional flexibility. This paper introduces new semiparametric time-varying mixture copula model, which both weights and parameters are deterministic unspecified functions time. We propose penalized with group ...

The cumulative distribution network (CDN) [21] is a recently developed class of probabilistic graphical models (PGMs) permitting a copula factorization, in which the CDF, rather than the density, is factored. Despite there being much recent interest within the machine learning community about copula representations, there has been scarce research into the CDN, its amalgamation with copula theor...

The famous Sklar’s theorem (see [54]) allows to build multivariate distributions using a copula and marginal distributions. For the basic theory on copulas see the first chapter ([14]) or the books on copulas by Joe ([32]) and Nelson ([51]). Much emphasis has been put on the bivariate case and in [32] and [51] many examples of bivariate copula families are given. However the class of multivaria...

This paper aims to propose a procedure for modeling the joint probability distribution of bivariate uncertain data with a nonlinear dependence structure. First, the concept of dependence measures is briefly introduced. Then, both the Akaike Information Criterion and the Bayesian Information Criterion are adopted for identifying the best-fit copula. Thereafter, simulation of copulas and bivariat...

In this paper, we address the crucial problems of parameters estimation of Collateralized Debt Obligation (CDO). We present a methodology for fair spread estimation of reconstituted (CDO) from European market data. A fundamental part of the pricing framework is the estimation of default probabilities and the structure of dependency. We present a copula based simulation procedure for pricing CDO...

We propose two new principal component analysis methods in this paper utilizing a semiparametric model. The according methods are named Copula Component Analysis (COCA) and Copula PCA. The semiparametric model assumes that, after unspecified marginally monotone transformations, the distributions are multivariate Gaussian. The COCA and Copula PCA accordingly estimate the leading eigenvectors of ...

This paper provides a new approach to recover relative entropy measures of contemporaneous dependence from limited information by constructing the most entropic copula (MEC) and its canonical form, namely the most entropic canonical copula (MECC). The MECC can effectively be obtained by maximizing Shannon entropy to yield a proper copula such that known dependence structures of data (e.g., meas...