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Multivariate ARCH and GARCH models
dc.contributor.advisorPrášková, Zuzana
dc.creatorŠafránková, Jana
dc.date.accessioned2017-04-03T12:01:20Z
dc.date.available2017-04-03T12:01:20Z
dc.date.issued2007
dc.identifier.urihttp://hdl.handle.net/20.500.11956/9927
dc.description.abstractWe study multivariate ARCH and GARCH models and their subsequent application to simulated and real data. In discussed models the conditional variance matrix is considered to be a function of lagged data process which is the subject of study. In case of GARCH models the conditional variance matrix is dependent on own lagged values, too. First of all, we deal with univariate ARCH and GARCH models to get some theoretical basis. The subsequent study extends this basis to multivariate models. A survey of multivariate GARCH models is presented in the next part of this thesis. Further study is devoted to maximum likelihood estimators of these models and we deal with alternatives to multivariate normal distribution which is a standard assumption of this method. We occupy ourselves with tests of these models, too. We mention both preestimation tests and postestimation tests to verify the adequacy of models. In conclusion we give practical examples which show di±culties of applications of these models for real data.en_US
dc.languageČeštinacs_CZ
dc.language.isocs_CZ
dc.publisherUniverzita Karlova, Matematicko-fyzikální fakultacs_CZ
dc.titleMnohorozměrné modely typu ARCH a GARCHcs_CZ
dc.typediplomová prácecs_CZ
dcterms.created2007
dcterms.dateAccepted2007-05-30
dc.description.departmentDepartment of Probability and Mathematical Statisticsen_US
dc.description.departmentKatedra pravděpodobnosti a matematické statistikycs_CZ
dc.description.facultyMatematicko-fyzikální fakultacs_CZ
dc.description.facultyFaculty of Mathematics and Physicsen_US
dc.identifier.repId43860
dc.title.translatedMultivariate ARCH and GARCH modelsen_US
dc.contributor.refereeHurt, Jan
dc.identifier.aleph000847540
thesis.degree.nameMgr.
thesis.degree.levelnavazující magisterskécs_CZ
thesis.degree.disciplineFinanční a pojistná matematikacs_CZ
thesis.degree.disciplineFinancial and insurance mathematicsen_US
thesis.degree.programMathematicsen_US
thesis.degree.programMatematikacs_CZ
uk.faculty-name.csMatematicko-fyzikální fakultacs_CZ
uk.faculty-name.enFaculty of Mathematics and Physicsen_US
uk.faculty-abbr.csMFFcs_CZ
uk.degree-discipline.csFinanční a pojistná matematikacs_CZ
uk.degree-discipline.enFinancial and insurance mathematicsen_US
uk.degree-program.csMatematikacs_CZ
uk.degree-program.enMathematicsen_US
thesis.grade.csVýborněcs_CZ
thesis.grade.enExcellenten_US
uk.abstract.enWe study multivariate ARCH and GARCH models and their subsequent application to simulated and real data. In discussed models the conditional variance matrix is considered to be a function of lagged data process which is the subject of study. In case of GARCH models the conditional variance matrix is dependent on own lagged values, too. First of all, we deal with univariate ARCH and GARCH models to get some theoretical basis. The subsequent study extends this basis to multivariate models. A survey of multivariate GARCH models is presented in the next part of this thesis. Further study is devoted to maximum likelihood estimators of these models and we deal with alternatives to multivariate normal distribution which is a standard assumption of this method. We occupy ourselves with tests of these models, too. We mention both preestimation tests and postestimation tests to verify the adequacy of models. In conclusion we give practical examples which show di±culties of applications of these models for real data.en_US
uk.publication.placePrahacs_CZ
uk.grantorUniverzita Karlova, Matematicko-fyzikální fakulta, Katedra pravděpodobnosti a matematické statistikycs_CZ


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