Publisher : Korean Data and Information Science Society
DOI : 10.7465/jkdi.2016.27.1.9
Title & Authors
Bayesian analysis of insurance risk model with parameter uncertainty Cho, Jaerin; Ji, Hyesu; Lee, Hangsuck;
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.
Cho, Y. and Cho, J. (2013). Measuring insurance risk of health insurance using a collective risk model. Journal of Insurance and Finance, 24, 3-35
Heckman, P. E. and Meyers, G. G. (1983). The calculation of aggregate loss distributions form claim severity and claim count distributions. Proceedings of the Casualty Actuarial Society, LXX, 22-61.
International Actuarial Association. (2004). A global framework for insurer solvency assessment, IAA Insurer Solvency Assessment Working Party Research Report, Availabe from http://www.actuaries.org/LIBRARY/Papers/Global_Framework_Insurer_Solvency_Assessment-public.pdf.
Luder T. (2005). Swiss solvency test in non-life insurance, Federal Office of Private Insurance, 36th ASTIN Colloquium.
Migon, H. S., Edison M. O. and Penna (2006). Bayesian analysis of a health insurance Model. Journal of Actuarial Practice, 13, 61-80.
Migon, H. S. and Moura, F. A. S. (2005). Hierarchical Bayesian collective risk model: An application to health insurance. Insurance: Mathematics and Economics, 36, 119-135.
Pai, J. S. (1997). Bayesian analysis of compound loss distributions. Journal of Econometrics, 79, 129-146.