J 2020

Hierarchical Archimedean Copulas for MATLAB and Octave: The HACopula Toolbox

GÓRECKI, Jan, Marius HOFERT and Martin HOLEŇA

Basic information

Original name

Hierarchical Archimedean Copulas for MATLAB and Octave: The HACopula Toolbox

Authors

GÓRECKI, Jan (203 Czech Republic, guarantor, belonging to the institution), Marius HOFERT (124 Canada) and Martin HOLEŇA (203 Czech Republic)

Edition

Journal of Statistical Software, 2020, 1548-7660

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

RIV identification code

RIV/47813059:19520/20:A0000145

Organization unit

School of Business Administration in Karvina

DOI

UT WoS

000542224700001

Keywords in English

copula; hierarchical Archimedean copula; structure; family; estimation; collapsing; sampling; goodness-of-fit; Kendall’s tau; tail dependence; MATLAB; Octave
Změněno: 7/5/2021 11:47, Ing. Jan Górecki, Ph.D.

Abstract

V originále

To extend the current implementation of copulas in MATLAB to non-elliptical distributions in arbitrary dimensions enabling for asymmetries in the tails, the toolbox HACopula provides functionality for modeling with hierarchical (or nested) Archimedean copulas. This includes their representation as MATLAB objects, evaluation, sampling, estimation and goodness-of-fit testing, as well as tools for their visual representation or computation of corresponding matrices of Kendall's tau and tail dependence coefficients. These are first presented in a quick-and-simple manner and then elaborated in more detail to show the full capability of HACopula. As an example, sampling, estimation and goodness-of-fit of a 100-dimensional hierarchical Archimedean copula is presented, including a speed up of its computationally most demanding part. The toolbox is also compatible with Octave, where no support for copulas in more than two dimensions is currently provided.