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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/12811

Title: On a hybrid clayton-gumbel and gumbel-frank bivariate copulas with application to stock indices
Authors: Boateng, Maxwell Akwasi
Omari-Sasu, Akoto Yaw
Frempong, Nana Kena
Avuglah, Richard Kodzo
Keywords: Convex convolution
Archimedean copulas
maximum likelihood
random variables
Issue Date: 18-Dec-2018
Publisher: Journal of Advances in Mathematics and Computer Science
Abstract: The study proposes two convex convolution based bivariate Archimedean copulas with their joint distribution functions and conditional distribution functions. Several simulations were performed using sample sizes 100,1000, 10000 and 1000000 for combinations of distributions: Gamma and exponential, Normal and exponential, Gamma and normal, Chi-square and Poisson as well as Skew normal and skew normal for the pairs of random variables to assess the performance of the models under different pairs of distributions. Using the method of maximum likelihood estimation, estimates were obtained for the likelihood function and used in obtaining Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) for comparison of the proposed copula models with existing copula models. The models were applied to two listed stocks on the Ghana Stock Exchange. In all, the proposed models, Clayton- Gumbel and Gumbel-Frank outperformed the existing models.
Description: An article published by Journal of Advances in Mathematics and Computer Science and also available at DOI: 10.9734/JAMCS/2019/45668
URI: http://hdl.handle.net/123456789/12811
Appears in Collections:College of Science

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