What is it about?
The copula functions have been widely used in dependence modeling. In this study, we look at how the copula began to develop from a basic form to a more advanced form through studies that previous researchers have made. Throughout this study, we find various types of the copula, and each exhibits its own characteristics lying under two main families, Elliptical and Archimedean copulas. Our findings suggest that copula is vital in solving problems in statistical dependence measures and joint marginal distribution functions. This comprehensive study served as a review paper on the development of copulas from their initial existence to their latest evolution.
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Why is it important?
This paper intends to deepen understanding of copulas’ role in solving a finance-related problem. Many queries will arise when we explore the theory of copulas; for example, why are copulas significant for researchandanalysisinthisfinancialfield?Toanswer this question, there are two significant reasons for this study. First, it may be used to learn statistical dependence measures with its application in finance, and second, it can be used to create different types of joint distributions. To establish joint distributions, we can use copula to couple marginal distributions of data used in research. The beauty of copula is that it allows us to extract the dependence structure from marginaldistributions jointly. It is useful when studying many variables as it is a multivariate dependence structure
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This page is a summary of: A comprehensive review on the development of copulas in financial field, Journal of Intelligent & Fuzzy Systems, October 2023, IOS Press, DOI: 10.3233/jifs-223481.
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