Copula hurdle GARCH models for multivariate non-negative time series

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A10506298" target="_blank" >RIV/00216208:11320/25:10506298 - isvavai.cz</a>

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=OVC7YpZkY_" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=OVC7YpZkY_</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00362-025-01713-x" target="_blank" >10.1007/s00362-025-01713-x</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Copula hurdle GARCH models for multivariate non-negative time series

Popis výsledku v původním jazyce

This work addresses the modeling of multiple related time series with non-negative observations, many of which contain non-negligible portions of zeros. Each series is modeled univariately as a GARCH process, constrained to non-negative values. A parametric copula is used to introduce dependence among the time series, with the occurrence of zeros assumed to follow a multivariate Markov chain. The goal is to estimate the omnibus model parameters. The multivariate hurdle distribution and the dependence of zeros cause classical estimation techniques to fail. Therefore, a partial quasi-maximum likelihood approach is employed, using a generalized density supported on the closed orthant. Under simple and easily verifiable assumptions, the estimated parameters of the joint model are shown to be consistent. The empirical properties are demonstrated in a simulation study.

Název v anglickém jazyce

Copula hurdle GARCH models for multivariate non-negative time series

Popis výsledku anglicky

This work addresses the modeling of multiple related time series with non-negative observations, many of which contain non-negligible portions of zeros. Each series is modeled univariately as a GARCH process, constrained to non-negative values. A parametric copula is used to introduce dependence among the time series, with the occurrence of zeros assumed to follow a multivariate Markov chain. The goal is to estimate the omnibus model parameters. The multivariate hurdle distribution and the dependence of zeros cause classical estimation techniques to fail. Therefore, a partial quasi-maximum likelihood approach is employed, using a generalized density supported on the closed orthant. Under simple and easily verifiable assumptions, the estimated parameters of the joint model are shown to be consistent. The empirical properties are demonstrated in a simulation study.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/GF22-01639K" target="_blank" >GF22-01639K: Postupné funkcionální změny - GraFuCha</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Statistical Papers

ISSN

0932-5026

e-ISSN

1613-9798

Svazek periodika

66

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

19

Strana od-do

87

Kód UT WoS článku

001482788000001

EID výsledku v databázi Scopus

2-s2.0-105004351620