Results 1 to 10 of about 65 (65)
New results on perturbation-based copulas
Dependence Modeling, 2021 A prominent example of a perturbation of the bivariate product copula (which characterizes stochastic independence) is the parametric family of Eyraud-Farlie-Gumbel-Morgenstern copulas which allows small dependencies to be modeled.
Saminger-Platz Susanne +4 more
doaj +1 more source
Detecting and modeling critical dependence structures between random inputs of computer models
Dependence Modeling, 2020 Uncertain information on input parameters of computer models is usually modeled by considering these parameters as random, and described by marginal distributions and a dependence structure of these variables.
Benoumechiara Nazih +3 more
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Maximal asymmetry of bivariate copulas and consequences to measures of dependence
Dependence Modeling, 2022 In this article, we focus on copulas underlying maximal non-exchangeable pairs (X,Y)\left(X,Y) of continuous random variables X,YX,Y either in the sense of the uniform metric d∞{d}_{\infty } or the conditioning-based metrics Dp{D}_{p}, and analyze their ...
Griessenberger Florian +1 more
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On the economic risk capital of portfolio insurance
International Journal of Mathematics and Mathematical Sciences, Volume 2004, Issue 41, Page 2209-2218, 2004., 2004 A formula for the conditional value‐at‐risk of classical portfolio insurance is derived and shown to be constant for sufficiently small loss probabilities. As illustrations, we discuss portfolio insurance for an equity market index using empirical data, and analyze the more general multivariate situation of a portfolio of risky assets.
Werner Hürlimann
wiley +1 more source
On the lower bound of Spearman’s footrule
Dependence Modeling, 2019 Úbeda-Flores showed that the range of multivariate Spearman’s footrule for copulas of dimension d ≥ 2 is contained in the interval [−1/d, 1], that the upper bound is attained exclusively by the upper Fréchet-Hoeffding bound, and that the lower bound is ...
Fuchs Sebastian, McCord Yann
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Multivariate Fréchet copulas and conditional value‐at‐risk
International Journal of Mathematics and Mathematical Sciences, Volume 2004, Issue 7, Page 345-364, 2004., 2004 Based on the method of copulas, we construct a parametric family of multivariate distributions using mixtures of independent conditional distributions. The new family of multivariate copulas is a convex combination of products of independent and comonotone subcopulas.
Werner Hürlimann
wiley +1 more source
Dependent defaults and losses with factor copula models
Dependence Modeling, 2017 We present a class of flexible and tractable static factor models for the term structure of joint default probabilities, the factor copula models. These high-dimensional models remain parsimonious with paircopula constructions, and nest many standard ...
Ackerer Damien, Vatter Thibault
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Measures of concordance determined by D4‐invariant copulas
International Journal of Mathematics and Mathematical Sciences, Volume 2004, Issue 70, Page 3867-3875, 2004., 2004 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.
H. H. Edwards +2 more
wiley +1 more source
Large portfolio risk management and optimal portfolio allocation with dynamic elliptical copulas
Dependence Modeling, 2018 Previous research has focused on the importance of modeling the multivariate distribution for optimal portfolio allocation and active risk management. However, existing dynamic models are not easily applied to high-dimensional problems due to the curse ...
Jin Xisong, Lehnert Thorsten
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Probabilistic derivation of a bilinear summation formula for the Meixner‐Pollaczek polynominals
International Journal of Mathematics and Mathematical Sciences, Volume 3, Issue 4, Page 761-771, 1980., 1980 Using the technique of canonical expansion in probability theory, a bilinear summation formula is derived for the special case of the Meixner‐Pollaczek polynomials which are defined by the generating function These polynomials satisfy the orthogonality condition with respect to the weight function
P. A. Lee
wiley +1 more source