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A0646
Title: Full-range tail dependence copulas with insurance applications Authors:  Jianxi Su - Purdue University (United States) [presenting]
Lei Hua - Northern Illinois University (United States)
Abstract: 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 to be discussed is exactly one such candidate. Specifically, a class of full-range tail dependence copulas will be introduced which has been proved quite useful for modeling dependent (insurance/financial) data. The key mechanism for constructing such flexible copula models and some future research related to this topic will be discussed.