A new class of copulas involved geometric distribution: Estimation and applications
Kong-Sheng Zhang, Jin-Guan Lin, , Pei-Rong Xu
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doi:10.1016/j.insmatheco.2015.09.008
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Abstract
Copula is becoming a popular tool for modelling the dependence structure among multiple variables. Commonly used copulas are Gaussian, t and Gumbel copulas. To further generalize these copulas, a new class of copulas, referred to as geometric copulas, is introduced by adding geometric distribution into the existing copulas. The interior-point penalty function algorithm is proposed to obtain maximum likelihood estimation of the parameters of geometric copulas. Simulation studies are carried out to evaluate the efficiency of the proposed method. The proposed estimation method is illustrated with workers’ compensation insurance data and exchange rate series data.