Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
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Bayesian analysis of insurance risk model with parameter uncertainty

베이지안 접근법과 모수불확실성을 반영한 보험위험 측정 모형

  • 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)
  • 조재린 (보험연구원 금융전략실) ;
  • 지혜수 (KB생명보험 계리부) ;
  • 이항석 (성균관대학교 보험계리학과/수학과)
  • Received : 2015.09.22
  • Accepted : 2016.01.14
  • Published : 2016.01.31
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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.

모수불확실성을 반영하는 손실모형으로는 Heckman과 Meyers가 제안한 모형이 주로 인용되고 있다. 이 모형은 모수 자체가 어떤 확률분포를 따른다는 가정을 하고 있으며 IAA, Swiss Solvency Test, EU Solvency II 등에서 참고하고 있다. 반면 베이지안 기법을 이용한 연구는 모수에 대한 선험적 정보 즉, 사전분포를 이용하여 모수불확실성을 반영한다. 그러나 현실에서는 두 가지 방법을 동시에 고려해야 하는 상황이 빈번히 발생한다. 이에 본 연구는 Heckman-Meyers의 모형과 베이지안 접근법을 동시에 고려한 베이지안 H-M CRM모형을 제안하고 그 특성을 분석하였다.

Keywords

References

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