On the asymptotic covariance of the multivariate empirical copula process
Dependence Modeling, 2019 Genest and Segers (2010) gave conditions under which the empirical copula process associated with a random sample from a bivariate continuous distribution has a smaller asymptotic covariance than the standard empirical process based on a random sample ...
Genest Christian +2 more
doaj +1 more source
Explaining predictive models using Shapley values and non-parametric vine copulas
Dependence Modeling, 2021 In this paper the goal is to explain predictions from complex machine learning models. One method that has become very popular during the last few years is Shapley values.
Aas Kjersti +3 more
doaj +1 more source
Detection of arbitrage opportunities in multi-asset derivatives markets
Dependence Modeling, 2021 We are interested in the existence of equivalent martingale measures and the detection of arbitrage opportunities in markets where several multi-asset derivatives are traded simultaneously.
Papapantoleon Antonis +1 more
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New copulas based on general partitions-of-unity (part III) — the continuous case
Dependence Modeling, 2019 In this paper we discuss a natural extension of infinite discrete partition-of-unity copulas which were recently introduced in the literature to continuous partition of copulas with possible applications in risk management and other fields.
Pfeifer Dietmar +3 more
doaj +1 more source
Copula modeling for discrete random vectors
Dependence Modeling, 2020 Copulas have now become ubiquitous statistical tools for describing, analysing and modelling dependence between random variables. Sklar’s theorem, “the fundamental theorem of copulas”, makes a clear distinction between the continuous case and the ...
Geenens Gery
doaj +1 more source
A note on conditional covariance matrices for elliptical distributions
, 2017 In this short note we provide an analytical formula for the conditional covariance matrices of the elliptically distributed random vectors, when the conditioning is based on the values of any linear combination of the marginal random variables.
Jaworski, Piotr, Pitera, Marcin
core +1 more source
An analysis of the R\"uschendorf transform - with a view towards Sklar's Theorem
, 2015 In many applications including financial risk measurement, copulas have shown to be a powerful building block to reflect multivariate dependence between several random variables including the mapping of tail dependencies.
Oertel, Frank
core +4 more sources
The de Finetti structure behind some norm-symmetric multivariate densities with exponential decay
Dependence Modeling, 2020 We derive a sufficient condition on the symmetric norm ||·|| such that the probability distribution associated with the density function f (x) ∝exp(−λ ||x||) is conditionally independent and identically distributed in the sense of de Finetti’s seminal ...
Mai Jan-Frederik
doaj +1 more source
New copulas based on general partitions-of-unity and their applications to risk management (part II)
Dependence Modeling, 2017 We present a constructive and self-contained approach to data driven infinite partition-of-unity copulas that were recently introduced in the literature.
Pfeifer Dietmar +2 more
doaj +1 more source
About the exact simulation of bivariate (reciprocal) Archimax copulas
Dependence Modeling, 2022 We provide an exact simulation algorithm for bivariate Archimax copulas, including instances with negative association. In contrast to existing simulation approaches, the feasibility of our algorithm is directly linked to the availability of an exact ...
Mai Jan-Frederik
doaj +1 more source