2021
Outer power transformations of hierarchical Archimedean copulas: Construction, sampling and estimation
GÓRECKI, Jan, Hofert MARIUS a Okhrin OSTAPZákladní 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
Odkazy
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.