Hierarchical Archimedean Copulas for MATLAB and Octave: The HACopula Toolbox

被引:6
|
作者
Gorecki, Jan [1 ]
Hofert, Marius [2 ]
Holena, Martin [3 ]
机构
[1] Silesian Univ Opava, Dept Informat, Sch Business Adm Karvina, Univ Namesti 1934-3, Karvina, Czech Republic
[2] Univ Waterloo, Dept Stat & Actuarial Sci, Fac Math, 200 Univ Ave West, Waterloo, ON, Canada
[3] Acad Sci Czech Republ, Inst Comp Sci, Vodarenskou Vezi 271-2, Prague 18207, Czech Republic
来源
JOURNAL OF STATISTICAL SOFTWARE | 2020年 / 93卷 / 10期
关键词
copula; hierarchical Archimedean copula; structure; family; estimation; collapsing; sampling; goodness-of-fit; Kendall's tau; tail dependence; MATLAB; Octave; KENDALLS TAU;
D O I
10.18637/jss.v093.i10
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
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.
引用
收藏
页码:1 / 36
页数:36
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