손해보험 위험조정 MCMC 추정기법
MCMC Method of Risk Adjustment Estimationin Non-life Insurance
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참고문헌
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초록
국제회계기준의 원칙에 부합하는 부채의 시가평가와 관련하여 보험업 분야에서 요구되는 최선추정, 할인율, 위험조정, 서비스 마진의 산출방법이 최근 중요한 이슈로 간주되고 있다. 본 연구는 손해보험회사의 지급준비금 변동성을 의미하는 위험조정을 산출하는 다양한 모형을 비교하고, 진전연도와 발생연도를 함께 고려하는 베이지언 모형이 갖는 장점에 대하여 논하였다. 본 연구의 베이지언 구조는 추가지급보험금이 로그정규분포를 따르는 모형을 이용하였으며, MCMC기법의 대표적인 기법인 깁스샘플링 알고리듬을 이용하였다. 성공적인 IFRS 2단계의 도입을 위하여 보험회사의 지급준비금 변동성을 수리적으로 추정하는 일련의 과정을 제시하고 국내 보험업계의 최근 10년 간의 자동차보험 손해진전자료를 바탕으로 실증분석결과를 제시하였다.
Abstract
IFRS phase II is expected to be effective as of 2021 according to the recent press release by IASB. Insurance companies are to be required to estimate the market value liability based on the principles proposed by IFRS final draft. This paper considers the method of risk adjustment estimation in non-life insurance, beginning with Bayesian structure incorporated with log-linear model of incremental paid loss. The advantages of Bayesian structure which includes the accident year effect as well as development year effect are discussed rigorously, and numerical analyses of the recent data in Korea auto insurance are provided. Technically, Gibbs sampling algorithm is adopted to estimate the parameters of Bayesian structure.
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