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Copulas are an important tool to formulating models for multivariate data analysis. An `ideal' copula should conform to a wide range of problems at hand, being either symmetric or asymmetric, and exhibiting flexible extent of tail dependence. The copula that I shall discuss is exactly one such candidate.
Specifically, in this talk, I should introduce a class of full-range tail dependence copulas which has been proved quite useful for modeling dependent (insurance/financial) data. I should discuss the key mechanism for constructing such flexible copula models and some future research related to this topic.
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