COGARCH models: An explicit solution to the stochastic differential equation for variance

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dc.contributor.authorAn, Yakup
dc.date.accessioned2026-01-24T12:20:56Z
dc.date.available2026-01-24T12:20:56Z
dc.date.issued2019
dc.departmentAlanya Alaaddin Keykubat Üniversitesi
dc.description.abstractIn this chapter, the features of a continuous time GARCH (COGARCH) process is discussed since the process can be applied as an explicit solution for the stochastic differential equation which is defined for the volatility of unequally spaced time series. COGARCH process driven by a Lévy process is an analogue of discrete time GARCH process and is further generalized to solutions of Lévy driven stochastic differential equations. The Compound Poisson and Variance Gamma processes are defined and used to derive the increments for the COGARCH process. Although there are various parameter estimation methods introduced for COGARCH, this study is focused on two methods which are Pseudo Maximum Likelihood Method and General Methods of Moments. Furthermore, an example is given to illustrate the findings. © 2020, IGI Global.
dc.identifier.doi10.4018/978-1-7998-0134-4.ch005
dc.identifier.endpage97
dc.identifier.isbn9781799801368
dc.identifier.isbn9781799801344
dc.identifier.scopus2-s2.0-85125156612
dc.identifier.scopusqualityN/A
dc.identifier.startpage79
dc.identifier.urihttps://doi.org/10.4018/978-1-7998-0134-4.ch005
dc.identifier.urihttps://hdl.handle.net/20.500.12868/4712
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIGI Global
dc.relation.publicationcategoryKitap Bölümü - Uluslararası
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20260121
dc.titleCOGARCH models: An explicit solution to the stochastic differential equation for variance
dc.typeBook Chapter

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