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http://dx.doi.org/10.7465/jkdi.2016.27.1.9

Bayesian analysis of insurance risk model with parameter uncertainty  

Cho, Jaerin (Department of Financial and Strategy Research, Korea Insurance Research Institute)
Ji, Hyesu (Actuarial Department, KB Life Insurance)
Lee, Hangsuck (Department of Actuarial Science/Mathematics, Sungkyunkwan University)
Publication Information
Journal of the Korean Data and Information Science Society / v.27, no.1, 2016 , pp. 9-18 More about this Journal
Abstract
In the Heckman-Meyers model, which is frequently referred by IAA, Swiss Solvency Test, EU Solvency II, the assumption of parameter distribution is key factor. While in theory Bayesian analysis somewhat reflects parameter uncertainty using prior distribution, it is often the case where both Heckman-Meyers and Bayesian are necessary to better manage the parameter uncertainty. Therefore, this paper proposes the use of Bayesian H-M CRM, a combination of Heckman-Meyers model and Bayesian, and analyzes its efficiency.
Keywords
Bayesian; CRM; insurance risk; parameter uncertainty risk;
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