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1
Content available remote

Some New Random Effect Models for Correlated Binary Responses

100%
Tounkara F., Rivest L.-P.
Dependence Modeling
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2014
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tom 2
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nr 1
EN
Exchangeable copulas are used to model an extra-binomial variation in Bernoulli experiments with a variable number of trials. Maximum likelihood inference procedures for the intra-cluster correlation are constructed for several copula families. The selection of a particular model is carried out using the Akaike information criterion (AIC). Profile likelihood confidence intervals for the intra-cluster correlation are constructed and their performance are assessed in a simulation experiment. The sensitivity of the inference to the specification of the copula family is also investigated through simulations. Numerical examples are presented.
2
Content available remote

Solution to an open problem about a transformation on the space of copulas

100%
Durante F., Fernández-Sánchez J., Trutschnig W.
Dependence Modeling
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2014
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tom 2
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nr 1
EN
We solve a recent open problem about a new transformation mapping the set of copulas into itself. The obtained mapping is characterized in algebraic terms and some limit results are proved.
3
Content available remote

A note on the Galambos copula and its associated Bernstein function

81%
Mai J.-F.
Dependence Modeling
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2014
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tom 2
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nr 1
EN
There is an infinite exchangeable sequence of random variables {Xk}k∈ℕ such that each finitedimensional distribution follows a min-stable multivariate exponential law with Galambos survival copula, named after [7]. A recent result of [15] implies the existence of a unique Bernstein function Ψ associated with {Xk}k∈ℕ via the relation Ψ(d) = exponential rate of the minimum of d members of {Xk}k∈ℕ. The present note provides the Lévy–Khinchin representation for this Bernstein function and explores some of its properties.
4
Content available remote

A two-component copula with links to insurance

81%
Ismail S., Yu G., Reinert G., Maynard T.
Dependence Modeling
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2017
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tom 5
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nr 1
295-303
EN
This paper presents a new copula to model dependencies between insurance entities, by considering how insurance entities are affected by both macro and micro factors. The model used to build the copula assumes that the insurance losses of two companies or lines of business are related through a random common loss factor which is then multiplied by an individual random company factor to get the total loss amounts. The new two-component copula is not Archimedean and it extends the toolkit of copulas for the insurance industry.
5
Content available remote

Multivariate Extreme Value Theory - A Tutorial with Applications to Hydrology and Meteorology

62%
Dutfoy A., Parey S., Roche N.
Dependence Modeling
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2014
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tom 2
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nr 1
EN
In this paper, we provide a tutorial on multivariate extreme value methods which allows to estimate the risk associated with rare events occurring jointly. We draw particular attention to issues related to extremal dependence and we insist on the asymptotic independence feature. We apply the multivariate extreme value theory on two data sets related to hydrology and meteorology: first, the joint flooding of two rivers, which puts at risk the facilities lying downstream the confluence; then the joint occurrence of high speed wind and low air temperatures, which might affect overhead lines.
6
Content available remote

New copulas based on general partitions-of-unity and their applications to risk management (part II)

52%
Pfeifer D., Mändle A., Ragulina O.
Dependence Modeling
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2017
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tom 5
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nr 1
246-255
EN
We present a constructive and self-contained approach to data driven infinite partition-of-unity copulas that were recently introduced in the literature. In particular, we consider negative binomial and Poisson copulas and present a solution to the problem of fitting such copulas to highly asymmetric data in arbitrary dimensions.
7
Content available remote

An analysis of the Rüschendorf transform - with a view towards Sklar’s Theorem

52%
Oertel F.
Dependence Modeling
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2015
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tom 3
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nr 1
EN
We revisit Sklar’s Theorem and give another proof, primarily based on the use of right quantile functions. To this end we slightly generalise the distributional transform approach of Rüschendorf and facilitate some new results including a rigorous characterisation of an almost surely existing “left-invertibility” of distribution functions.
8
Content available remote

Dependent defaults and losses with factor copula models

42%
Ackerer D., Vatter T.
Dependence Modeling
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2017
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tom 5
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nr 1
375-399
EN
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 models as special cases. The loss distribution of a portfolio of contingent claims can be exactly and efficiently computed when individual losses are discretely supported on a finite grid. Numerical examples study the key features affecting the loss distribution and multi-name credit derivatives prices. An empirical exercise illustrates the flexibility of our approach by fitting credit index tranche prices.
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