Open Access Published since October 21, 2013

Dependence Modeling

ISSN: 2300-2298

Edited by: Giovanni Puccetti

About this journal

Special Issues:

Special Issue on Econometrics and Business Analytics

The journal Dependence Modeling aims at providing a medium for exchanging results and ideas in the area of multivariate dependence modeling.
It is an open access fully peer-reviewed journal providing the readers with free, instant, and permanent access to all content worldwide.
Dependence Modeling is listed by Web of Science (Emerging Sources Citation Index), Scopus, MathSciNet and Zentralblatt Math.

To submit a paper please go to the section ‘’Submission of manuscripts’’ below.
Flyer and Editorial Board

The journal presents different types of articles:

"Research Articles" on fundamental theoretical aspects, as well as on significant applications in science, engineering, economics, finance, insurance and other fields.
"Review Articles" which present the existing literature on the specific topic from new perspectives.
"Interview articles" limited to two papers per year, covering interviews with milestone personalities in the field of Dependence Modeling.

The journal topics include (but are not limited to):　

Copula methods
Multivariate distributions
Estimation and goodness-of-fit tests
Measures of association
Quantitative risk management
Risk measures and stochastic orders
Time series
Environmental sciences
Computational methods and software
Extreme-value theory
Limit laws
Mass Transportations

Your Benefits

Why subscribe and read?

premier source of high quality research in a growing topic
international forum for the dissemination of research, designed to facilitate the exchange of ideas between theoretical and applied researchers from different countries

Why submit?

promotion of each published article in the scientific community
first journal focusing on the modeling of dependence
fast, fair and constructive peer review
secure archiving by De Gruyter and the independent archiving service Portico
liberal policies on copyrights - the copyrights are governed by the Creative-Commons Attribution Only license　(CC-BY) compliant with Plan-S.

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Volume 9 Issue 1

January 2021

Regular articles

Open Access March 1, 2021

Generalized Bernoulli process with long-range dependence and fractional binomial distribution

Jeonghwa Lee

Page range: 1-12

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Abstract

Bernoulli process is a finite or infinite sequence of independent binary variables, X i , i = 1, 2, · · ·, whose outcome is either 1 or 0 with probability P ( X i = 1) = p , P ( X i = 0) = 1 – p , for a fixed constant p ∈ (0, 1). We will relax the independence condition of Bernoulli variables, and develop a generalized Bernoulli process that is stationary and has auto-covariance function that obeys power law with exponent 2 H – 2, H ∈ (0, 1). Generalized Bernoulli process encompasses various forms of binary sequence from an independent binary sequence to a binary sequence that has long-range dependence. Fractional binomial random variable is defined as the sum of n consecutive variables in a generalized Bernoulli process, of particular interest is when its variance is proportional to n 2 H , if H ∈ (1/2, 1).

Open Access March 30, 2021

Polynomial bivariate copulas of degree five: characterization and some particular inequalities

Adam Šeliga, Manuel Kauers, Susanne Saminger-Platz, Radko Mesiar, Anna Kolesárová, Erich Peter Klement

Page range: 13-42

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Abstract

Bivariate polynomial copulas of degree 5 (containing the family of Eyraud-Farlie-Gumbel-Morgenstern copulas) are in a one-to-one correspondence to certain real parameter triplets ( a , b , c ), i.e., to some set of polynomials in two variables of degree 1: p ( x , y ) = ax + by + c . The set of the parameters yielding a copula is characterized and visualized in detail. Polynomial copulas of degree 5 satisfying particular (in)equalities (symmetry, Schur concavity, positive and negative quadrant dependence, ultramodularity) are discussed and characterized. Then it is shown that for polynomial copulas of degree 5 the values of several dependence parameters (including Spearman’s rho, Kendall’s tau, Blomqvist’s beta, and Gini’s gamma) lie in exactly the same intervals as for the Eyraud-Farlie-Gumbel-Morgenstern copulas. Finally we prove that these dependence parameters attain all possible values in ]−1, 1[ if polynomial copulas of arbitrary degree are considered.

Open Access May 24, 2021

Multivariate radial symmetry of copula functions: finite sample comparison in the i.i.d case

Monica Billio, Lorenzo Frattarolo, Dominique Guégan

Page range: 43-61

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Abstract

Given a d -dimensional random vector X = ( X 1 , . . ., X d ), if the standard uniform vector U obtained by the component-wise probability integral transform (PIT) of X has the same distribution of its point reflection through the center of the unit hypercube, then X is said to have copula radial symmetry. We generalize to higher dimensions the bivariate test introduced in [11], using three different possibilities for estimating copula derivatives under the null. In a comprehensive simulation study, we assess the finite-sample properties of the resulting tests, comparing them with the finite-sample performance of the multivariate competitors introduced in [17] and [1].

Open Access June 4, 2021

Explaining predictive models using Shapley values and non-parametric vine copulas

Kjersti Aas, Thomas Nagler, Martin Jullum, Anders Løland

Page range: 62-81

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Abstract

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. The original development of Shapley values for prediction explanation relied on the assumption that the features being described were independent. If the features in reality are dependent this may lead to incorrect explanations. Hence, there have recently been attempts of appropriately modelling/estimating the dependence between the features. Although the previously proposed methods clearly outperform the traditional approach assuming independence, they have their weaknesses. In this paper we propose two new approaches for modelling the dependence between the features. Both approaches are based on vine copulas, which are flexible tools for modelling multivariate non-Gaussian distributions able to characterise a wide range of complex dependencies. The performance of the proposed methods is evaluated on simulated data sets and a real data set. The experiments demonstrate that the vine copula approaches give more accurate approximations to the true Shapley values than their competitors.

Open Access July 5, 2021

Study of partial and average conditional Kendall’s tau

Irène Gijbels, Margot Matterne

Page range: 82-120

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Abstract

When the interest is in studying conditional dependencies, and more precisely the strength of conditional dependencies, some kind of averaging over the conditioning random vector may be needed. Examples of average measures that can serve in this context are the average conditional Kendall’s tau and partial Kendall’s tau. It is known that these measures differ in general. Some statistical tests are based on these average measures, and a better knowledge of them is of importance. The aim of this paper is to provide a quantitative study of the possible differences of these two average measures, and to establish su˚cient conditions under which they coincide. Both measures are studied in two fairly general settings. In each setting theoretical results are established as well as several illustrative examples given.

Open Access July 23, 2021

Detecting departures from meta-ellipticity for multivariate stationary time series

Axel Bücher, Miriam Jaser, Aleksey Min

Page range: 121-140

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Abstract

A test for detecting departures from meta-ellipticity for multivariate stationary time series is proposed. The large sample behavior of the test statistic is shown to depend in a complicated way on the underlying copula as well as on the serial dependence. Valid asymptotic critical values are obtained by a bootstrap device based on subsampling. The finite-sample performance of the test is investigated in a large-scale simulation study, and the theoretical results are illustrated by a case study involving financial log returns.

Cite Score	1.7
Journal Citation Indicator	0.39
Mathematical Citation Quotient	0.24
SCImago Journal Rank	0.837
Source Normalized Impact per Paper	1.171

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PEER-REVIEW PROCESS
The journal uses a closed single-blind peer review system (the names of the reviewers are hidden from the authors). Submitted manuscripts are reviewed by two or more experts. Reviewers are asked to recommend whether a manuscript should be accepted, revised or rejected. Although the journal uses the plagiarism detection system CrossCheck, reviewers should alert the editors if they suspect any issues relating to author misconduct such as plagiarism.

Reviewers are asked to provide detailed, constructive comments that will help both the editors make a decision on the publication and the author(s) to improve their manuscript. They should point out whether the work has serious flaws that preclude its publication, or whether additional experiments should be carried out or additional data should be collected to support the conclusions drawn.

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check and update the references where possible (preprint and working paper which have been accepted during the revision process) and make sure the bibliography is contained in the TeX/LaTeX file (i.e. without reference to any external .bib file). References should be listed alphabetically and Journal names should be written in abbreviated from according to Mathematical Reviews Serials Abbreviations, see http://www.ams.org/msnhtml/serials.pdf. For in-press references or preprint we suggest to indicate a web address where to download the paper or a DOI number if available. Provide the graphical elements accompanying the manuscript (figures, pictures, etc.) as separate files in .eps or .pdf format.
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When a major revision of a manuscript is suggested, authors are expected to deliver the revised version of the manuscript within 3 months (1 month in the case a minor revision is suggested). Along with the major/minor revision the authors should also provide a point by point explanation of how the issued raised by the referees have been handled and a brief evaluation of each report received on the paper. The manuscript should be uploaded directly to the Editorial Manager as an answer to the Journal Editor's decision (and not as a new manuscript). If the above mentioned deadlines are not met, the manuscript will be treated as a new submission.

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Editorial

Editor-in-Chief
Giovanni Puccetti, Università degli Studi di Milano, Italy　

Associate Editors
Fabrizio Durante, Università del Salento, Italy
Matthias Scherer, Technische Universität München, Germany
Steven Vanduffel, Vrije Universiteit Brussel, Belgium

Honorary Board Members:
Roger Cooke, Resources for the Future, USA
Claudia Czado, Technische Universität München, Germany
Paul Embrechts, ETH Zürich, Switzerland
Edward (Jed) Frees, University of Wisconsin-Madison, USA & Australian National University,
Australia
Christian Genest, McGill University, Canada
Harry Joe, University of British Columbia, Canada
Roger Nelsen, Lewis & Clark College, USA
Ludger Rüschendorf, Albert-Ludwigs-Universität Freiburg, Germany
Carlo Sempi, Università del Salento, Italy

Editorial Advisory Board Members:
Beatrice Acciaio, ETH Zürich, Switzerland
Concepcion Ausin, Universidad Carlos III de Madrid, Spain
Carole Bernard, Vrije Universiteit Brussel, Belgium & Grenoble EM, France
Corina Constantinescu, University of Liverpool, UK
Elena di Bernardino, Université Côte d'Azur, France
Sebastian Engelke, Université de Genève, Switzerland
Jean-David Fermanian, ENSAE Paris, France
Juan Fernández-Sánchez, Universidad de Almería, Spain
Sebastian Fuchs, Universität Salzburg, Austria
Stéphane Girard, Inria Grenoble Rhône-Alpes, France
Ingrid Hobæk Haff, University of Oslo, Norway
Enkelejd Hashorva, Université de Lausanne, Switzerland
Taizhong Hu, University of Science and Technology of China
Lei Hua, Northern Illinois University, USA
Piotr Jaworski, Uniwersytet Warszawski, Poland
Shogo Kato, The Institute of Statistical Mathematics, Japan
Nadja Klein, Humboldt-Universität zu Berlin, Germany
Pavel Krupskiy, University of Melbourne, Australia
Dorota Kurowicka, TU Delft, Netherlands
Woojoo Lee, Seoul National University, Republic of Korea
Jan-Frederik Mai, XAIA Investment, Germany
Mélina Mailhot, Concordia University, Canada
Giampiero Marra, University College London, UK
Ostap Okhrin, Technische Universität Dresden, Germany
Artem Prokhorov, The University of Sydney Business School, Australia
Jean-Francois Quessy, Université du Québec à Trois-Rivières, Canada
Peng Shi, University of Wisconsin-Madison, USA
Wolfgang Trutschnig, Universität Salzburg, Austria
Emiliano Valdez, University of Connecticut, USA
Ruodu Wang, University of Waterloo, Canada
Johannes Wiesel, Columbia University, USA

Assistant Editors
Andrew Chernih, University of New South Wales, Australia
Marta Nai Ruscone, Università degli Studi di Genova, Italy

Publisher
DE GRUYTER Poland
Bogumiła Zuga 32A Str.
01-811 Warsaw, Poland
T: +48 22 701 50 15

Editorial Contact
Giovanni Puccetti
demo.editorial@degruyter.com

(Deutsch)

Type: Journal
Language: English
Publisher: De Gruyter Open Access
First published: October 21, 2013
Publication Frequency: 1 Issue per Year
Audience: researchers in the field of multivariate dependence modeling