Conditional Tail Dependence for Bivariate Copulas with Applications in Finance

dc.contributor.advisorYe, Keying
dc.contributor.advisorLien, Da-Hsiang Donald
dc.contributor.authorLiu, Zhiruo
dc.contributor.committeeMemberKeating, Jerome P.
dc.contributor.committeeMemberTripathi, Ram C.
dc.date.accessioned2024-02-12T14:54:39Z
dc.date.available2024-02-12T14:54:39Z
dc.date.issued2018
dc.descriptionThis item is available only to currently enrolled UTSA students, faculty or staff. To download, navigate to Log In in the top right-hand corner of this screen, then select Log in with my UTSA ID.
dc.description.abstractAssociations between variables can be measured in many ways such as correlation, concordance, dependence, and so on. Researchers and practitioners recently are interested in the associations between variables at extreme values in which large amount of profits or losses in financial industry are considered. Sibuya (1960) proposed a conditional dependence structure, called tail dependence coefficient, to measure the asymptotic dependency between variables. This coefficient has become a standard measurement of associations between variables at extreme values. A conditional tail dependence is a structure that involves the joint and marginal distributions of random variables at their extreme values. Different conditions of identifying extreme events are used to capture various behaviors of random variables approaching to their limiting situations. Existing tail dependence coefficients consider the co-movements of two variables at the same rate towards extremes. Such measures may well describe the dependence of two similar variables at the extremes. However, it may be more reasonable to consider the asymptotic dependence behavior for two not so similar variables at different rates. In this research, we propose a functional tail dependence structure between two variables at their extreme values in the sense that the rates approaching to extremes are functionally different. Furthermore, we propose a generalized tail dependence structure in which each tail may approach extreme values by itself. We obtain definite solutions of such proposed tail dependence coefficients for six commonly used copulas under mild assumptions. In addition, empirical studies are carried out for financial data.
dc.description.departmentManagement Science and Statistics
dc.format.extent124 pages
dc.format.mimetypeapplication/pdf
dc.identifier.isbn9780438739895
dc.identifier.urihttps://hdl.handle.net/20.500.12588/4436
dc.languageen
dc.subject.classificationStatistics
dc.subject.classificationFinance
dc.titleConditional Tail Dependence for Bivariate Copulas with Applications in Finance
dc.typeThesis
dc.type.dcmiText
dcterms.accessRightspq_closed
thesis.degree.departmentManagement Science and Statistics
thesis.degree.grantorUniversity of Texas at San Antonio
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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