J 2021

Outer power transformations of hierarchical Archimedean copulas: Construction, sampling and estimation

GÓRECKI, Jan, Hofert MARIUS a Okhrin OSTAP

Základní údaje

Originální název

Outer power transformations of hierarchical Archimedean copulas: Construction, sampling and estimation

Autoři

GÓRECKI, Jan (203 Česká republika, garant, domácí), Hofert MARIUS (124 Kanada) a Okhrin OSTAP (276 Německo)

Vydání

Computational Statistics & Data Analysis, 2021, 0167-9473

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Nizozemské království

Utajení

není předmětem státního či obchodního tajemství

Kód RIV

RIV/47813059:19520/21:A0000251

Organizační jednotka

Obchodně podnikatelská fakulta v Karviné

UT WoS

000609164800010

Klíčová slova anglicky

Archimedean generator; Outer power transformation; Sampling; Estimation;Tail dependence coefficients; Value at risk

Štítky

Změněno: 12. 4. 2022 13:02, Miroslava Snopková

Anotace

V originále

Outer power (OP) transformations of Archimedean generators are suggested to increase the modeling flexibility and statistical fitting capabilities of classical Archimedean copulas restricted to a single parameter. For OP-transformed Archimedean copulas, a formula for computing tail dependence coefficients is obtained, as well as two feasible OP Archimedean copula estimators are proposed and their properties studied by simulation. For hierarchical extensions of OP-transformed Archimedean copulas under the sufficient nesting condition, a new construction principle, efficient sampling and parameter estimation for models based on a single one-parameter Archimedean family are addressed. Special attention is paid to the case where the sufficient nesting condition simplifies to two types of restrictions on the corresponding parameters. By simulation, the convergence rate and standard errors of the proposed estimator are studied. Excellent tail fitting capabilities of OP-transformed hierarchical Archimedean copula models are demonstrated in a risk management application. The results show that the OP transformation is able to improve the statistical fit of exchangeable Archimedean copulas, particularly of those that cannot capture upper tail dependence or strong concordance, as well as the statistical fit of hierarchical Archimedean copulas, especially in terms of tail dependence and higher dimensions. Given how comparably simple it is to include OP transformations into existing exchangeable and hierarchical Archimedean copula models, OP transformations provide an attractive trade-off between computational effort and statistical improvement.