Dependence Modeling Volume 7 Issue 1, Page 2

Journal Article

Probability of ruin in discrete insurance risk model with dependent Pareto claims

Constantinescu, Corina D.; Kozubowski, Tomasz J.; Qian, Haoyu H.

2019 Dependence Modeling

doi:10.1515/demo-2019-0011

AbstractWe present basic properties and discuss potential insurance applications of a new class of probability distributions on positive integers with power law tails. The distributions in this class are zero-inflated discrete counterparts of the Pareto distribution. In particular, we obtain the...

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Journal Article

A latent class analysis towards stability and changes in breadwinning patterns among coupled households

Pennoni, Fulvia; Nakai, Miki

2019 Dependence Modeling

doi:10.1515/demo-2019-0012

AbstractA latent class model is proposed to examine couples’ breadwinning typologies and explain the wage differentials according to the socio-demographic characteristics of the society with data collected through surveys. We derive an ordinal variable indicating the couple’s income...

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Journal Article

Copulas, stable tail dependence functions, and multivariate monotonicity

Ressel, Paul

2019 Dependence Modeling

doi:10.1515/demo-2019-0013

AbstractFor functions of several variables there exist many notions of monotonicity, three of them being characteristic for resp. distribution, survival and co-survival functions. In each case the “degree” of monotonicity is just the basic one of a whole scale.Copulas are special distribution...

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Journal Article

On a class of norms generated by nonnegative integrable distributions

Falk, Michael; Stupfler, Gilles

2019 Dependence Modeling

doi:10.1515/demo-2019-0014

AbstractWe show that any distribution function on ℝd with nonnegative, nonzero and integrable marginal distributions can be characterized by a norm on ℝd+1, called F-norm. We characterize the set of F-norms and prove that pointwise convergence of a sequence of F-norms to an F-norm is equivalent...

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Journal Article

On the asymptotic covariance of the multivariate empirical copula process

Genest, Christian; Mesfioui, Mhamed; Nešlehová, Johanna G.

2019 Dependence Modeling

doi:10.1515/demo-2019-0015

AbstractGenest 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 from the underlying copula. An...

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Journal Article

On kernel-based estimation of conditional Kendall’s tau: finite-distance bounds and asymptotic behavior

Derumigny, Alexis; Fermanian, Jean-David

2019 Dependence Modeling

doi:10.1515/demo-2019-0016

AbstractWe study nonparametric estimators of conditional Kendall’s tau, a measure of concordance between two random variables given some covariates. We prove non-asymptotic pointwise and uniform bounds, that hold with high probabilities. We provide “direct proofs” of the consistency and the...

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Journal Article

On Copula-Itô processes

Jaworski, Piotr

2019 Dependence Modeling

doi:10.1515/demo-2019-0017

AbstractWe study the dynamics of the family of copulas {Ct}t≥0 of a pair of stochastic processes given by stochastic differential equations (SDE). We associate to it a parabolic partial differential equation (PDE). Having embedded the set of bivariate copulas in a dual of a Sobolev Hilbert space...

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Journal Article

Dependence measure for length-biased survival data using copulas

Bentoumi, Rachid; Mesfioui, Mhamed; Alvo, Mayer

2019 Dependence Modeling

doi:10.1515/demo-2019-0018

AbstractThe linear correlation coefficient of Bravais-Pearson is considered a powerful indicator when the dependency relationship is linear and the error variate is normally distributed. Unfortunately in finance and in survival analysis the dependency relationship may not be linear. In such case,...

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Journal Article

Fitting heavy-tailed mixture models with CVaR constraints

Pertaia, Giorgi; Uryasev, Stan

2019 Dependence Modeling

doi:10.1515/demo-2019-0019

AbstractStandard methods of fitting finite mixture models take into account the majority of observations in the center of the distribution. This paper considers the case where the decision maker wants to make sure that the tail of the fitted distribution is at least as heavy as the tail of the...

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Journal Article

Optimal bandwidth selection for recursive Gumbel kernel density estimators

Slaoui, Yousri

2019 Dependence Modeling

doi:10.1515/demo-2019-0020

AbstractIn this paper, we propose a data driven bandwidth selection of the recursive Gumbel kernel estimators of a probability density function based on a stochastic approximation algorithm. The choice of the bandwidth selection approaches is investigated by a second generation plug-in method....

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