Bayesian estimation of student-t garch model using lindley's approximation

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dc.contributor.authorArı, Yakup
dc.contributor.authorPapadopoulos, Alex
dc.date.accessioned2021-02-19T21:16:33Z
dc.date.available2021-02-19T21:16:33Z
dc.date.issued2019
dc.departmentALKÜ
dc.description.abstractThe dependency of conditional second moments of financial time series is modelled by Generalized Autoregressive conditionally heteroscedastic (GARCH) processes. The maximum likelihood estimation (MLE) procedure is most commonly used for estimating the unknown parameters of a GARCH model. In this study, the parameters of the GARCH models with student-t innovations are discussed for estimations using the Bayesian approach. It is assumed that the parameters of the GARCH model are random variables having known prior probability density functions. Lindley's approximation will be used to estimate the Bayesian estimators since they are not in a closed form. The Bayesian estimators are derived under squared error loss function. Finally, a simulation study is performed in order to compare the ML estimates to the Bayesian ones and in addition to simulations an example is given in order to illustrate the findings. MLE's and Bayesian estimates are compared according to the expected risks in the simulation study which shows that as the sample size increases the expected risks decrease and also it is observed that Bayesian estimates have performed better than MLE 's.
dc.identifier.doi10.24818/18423264/53.1.19.05
dc.identifier.endpage88en_US
dc.identifier.issn0424-267X
dc.identifier.issn1842-3264
dc.identifier.issue1en_US
dc.identifier.scopusqualityQ3
dc.identifier.startpage75en_US
dc.identifier.urihttps://doi.org/10.24818/18423264/53.1.19.05
dc.identifier.urihttps://hdl.handle.net/20.500.12868/474
dc.identifier.volume53en_US
dc.identifier.wosWOS:000461762700005
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthor0-belirlenecek
dc.language.isoen
dc.publisherAcad Economic Studies
dc.relation.ispartofEconomic Computation And Economic Cybernetics Studies And Research
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectGARCH
dc.subjectMLE
dc.subjectLindley's Approximation
dc.subjectBayesian Methods
dc.subjectSquared Error
dc.titleBayesian estimation of student-t garch model using lindley's approximation
dc.typeArticle

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