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New composite distributions for insurance claim sizes

보험 청구액에 대한 새로운 복합분포

  • Jung, Daehyeon (Department of Statistics, Yeungnam University) ;
  • Lee, Jiyeon (Department of Statistics, Yeungnam University)
  • Received : 2017.02.16
  • Accepted : 2017.03.31
  • Published : 2017.06.30

Abstract

The insurance market is saturated and its growth engine is exhausted; consequently, the insurance industry is now in a low growth period with insurance companies that face a fierce competitive environment. In such a situation, it will be an important issue to find the probability distributions that can explain the flow of insurance claims, which are the basis of the actuarial calculation of the insurance product. Insurance claims are generally known to be well fitted by lognormal distributions or Pareto distributions biased to the left with a thick tail. In recent years, skew normal distributions or skew t distributions have been considered reasonable distributions for describing insurance claims. Cooray and Ananda (2005) proposed a composite lognormal-Pareto distribution that has the advantages of both lognormal and Pareto distributions and they also showed the composite distribution has a higher fitness than single distributions. In this paper, we introduce new composite distributions based on skew normal distributions or skew t distributions and apply them to Danish fire insurance claim data and US indemnity loss data to compare their performance with the other composite distributions and single distributions.

보험 시장은 포화되고 그 성장 동력은 소진되어 보험 산업이 저성장에 머물러 있는 가운데 보험사들은 치열한 경쟁 환경에 놓여있다. 이러한 상황에서 보험 상품에 대한 보험수리적 계산의 기초가 되는 보험 청구액의 흐름을 잘 설명할 수 있는 확률분포를 찾아내는 것은 중요한 쟁점이 될 것이다. 보험 청구액의 분포는 일반적으로 두꺼운 꼬리를 가지면서 왼쪽으로 치우친 로그정규분포나 파레토 분포로 잘 설명된다고 알려져 있으나 최근에는 기운 정규분포나 기운 t 분포가 보험 청구액 분포로 적절한 것으로 고찰되었다. Cooray와 Ananda (2005)는 로그정규분포와 파레토 분포의 장점을 모두 가진 로그정규-파레토 복합분포를 제시하고 단일분포보다 더 높은 적합도를 가짐을 확인하였다. 본 논문에서는 기운 정규분포와 기운 t 분포를 머리 부분으로 결합한 새로운 복합분포를 소개하고 덴마크의 화재보험 청구액 데이터와 미국의 배상 지불금 데이터에 적용하여 기존의 다른 복합분포들을 포함하여 여러 단일분포들과 그 성능을 비교한다.

Keywords

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