Journal ArticleDOI
Everything You Always Wanted to Know about Copula Modeling but Were Afraid to Ask
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This paper presents an introduction to inference for copula models, based on rank methods, by working out in detail a small, fictitious numerical example, the various steps involved in investigating the dependence between two random variables and in modeling it using copulas.Abstract:
This paper presents an introduction to inference for copula models, based on rank methods. By working out in detail a small, fictitious numerical example, the writers exhibit the various steps involved in investigating the dependence between two random variables and in modeling it using copulas. Simple graphical tools and numerical techniques are presented for selecting an appropriate model, estimating its parameters, and checking its goodness-of-fit. A larger, realistic application of the methodology to hydrological data is then presented.read more
Citations
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Journal ArticleDOI
Discussion of “Copulas: Tales and facts”, by Thomas Mikosch
Christian Genest,Bruno Rémillard +1 more
TL;DR: A measured response is provided to Dr. Mikosch’s vitriolic attack on the merits of studying, characterizing and modeling stochastic dependence through copulas.
Journal ArticleDOI
A copula-based approach to accommodate the dependence among microscopic traffic variables
Yajie Zou,Yunlong Zhang +1 more
TL;DR: The modeling and simulation results suggest that copula models can adequately accommodate and accurately reproduce the dependence structure revealed by the traffic observations and provide a framework for generating multiple microscopic traffic variables simultaneously by considering their dependence.
Journal ArticleDOI
Operational risk quantification using extreme value theory and copulas: from theory to practice
TL;DR: In this paper, the authors point out several pitfalls of the standard methodologies for quantifying operational losses and introduce dependence between the business lines using Copula Theory, showing that standard economic thinking about diversification may be inappropriate when infinite-mean distributions are involved.
Journal ArticleDOI
Note(s): A note on the asymptotic behavior of the Bernstein estimator of the copula density
TL;DR: The purpose of this note is to study the asymptotic distributional behavior of the Bernstein estimator of a copula density, and the general theorem does not assume known marginals.
Posted Content
D-vine copula based quantile regression
Daniel Kraus,Claudia Czado +1 more
TL;DR: A new semiparametric quantile regression method based on sequentially fitting a likelihood optimal D-vine copula to given data resulting in highly flexible models with easily extractable conditional quantiles is introduced.
References
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Book
An Introduction to Copulas
TL;DR: This book discusses the fundamental properties of copulas and some of their primary applications, which include the study of dependence and measures of association, and the construction of families of bivariate distributions.
Journal ArticleDOI
Non-Uniform Random Variate Generation.
B. J. T. Morgan,Luc Devroye +1 more
TL;DR: This chapter reviews the main methods for generating random variables, vectors and processes in non-uniform random variate generation, and provides information on the expected time complexity of various algorithms before addressing modern topics such as indirectly specified distributions, random processes, and Markov chain methods.
Book ChapterDOI
A Class of Statistics with Asymptotically Normal Distribution
TL;DR: In this article, the authors considered the problem of estimating a U-statistic of the population characteristic of a regular functional function, where the sum ∑″ is extended over all permutations (α 1, α m ) of different integers, 1 α≤ (αi≤ n, n).