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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/12820

Title: An efficient copula under data perturbations across stock markets
Authors: Barley, Ivy
Okyere, Gabriel Asare
Kpamma, Henry Man’tieebe
Achamfour, James Baah
Kweku, David
et. al
Keywords: Copula
Dependence Structure
Data Perturbation
Joint Distribution
Issue Date: 2019
Publisher: Mathematics and Statistics
Abstract: Economic trade amongst the variousWest African economies can either lead to mutual gains or losses. It is therefore important to assess the extent to which dependence amongst these countries can have on their economies. The linear correlation coefficient is normally used as a measure of dependence between random variables. However, there are some limitations when used for economic variables like the stock market; as they do not follow the elliptical distribution. Copulas, however are scale-free methods of constructing dependence structures amongst the stock markets, even in cases of data perturbations. The aim of this study is to assess the impact of data perturbations on the copula models. The maximum likelihood estimation method was the parameter estimation method used for the Archimedean copulas. The Clayton, Joe, Frank and Gumbel copulas were estimated. The Gumbel copula was the most robust copula in all the cases of data perturbations.
Description: An article published by Mathematics and Statistics and also available at DOI: 10.13189/ms.2019.070202
URI: http://hdl.handle.net/123456789/12820
Appears in Collections:College of Science

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