• Title/Summary/Keyword: 메트로폴리스 알고리듬

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Longevity Bond Pricing by a Cohort-based Stochastic Mortality (코호트 사망률을 이용한 장수채권 가격산출)

  • Jho, Jae Hoon;Lee, Kangsoo
    • The Korean Journal of Applied Statistics
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    • v.28 no.4
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    • pp.703-719
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    • 2015
  • We propose an extension of the Lee and Jho (2015) mean reverting the two factor mortality model by incorporating a period-specific cohort effect. We found that the consideration of cohort effect improves the mortality fit of Korea male data above age 65. Parameters are estimated by the weighted least squares method and Metropolis algorithm. We also emphasize that the cohort effect is necessary to choose the base survival index to calculate longevity bond issue price. A key contribution of the article is the proposal and development of a method to calculate the longevity bond price to hedge the longevity risk exposed to Korea National Pension Services.

A Two Factor Model with Mean Reverting Process for Stochastic Mortality (평균회귀확률과정을 이용한 2요인 사망률 모형)

  • Lee, Kangsoo;Jho, Jae Hoon
    • The Korean Journal of Applied Statistics
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    • v.28 no.3
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    • pp.393-406
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    • 2015
  • We examine how to model mortality risk using the adaptation of the mean-reverting processes for the two factor model proposed by Cairns et al. (2006b). Mortality improvements have been recently observed in some countries such as United Kingdom; therefore, we assume long-run mortality converges towards a trend at some unknown time and the mean-reverting processes could therefore be an appropriate stochastic model. We estimate the parameters of the two-factor model incorporated with mean-reverting processes by a Metropolis-Hastings algorithm to fit United Kingdom mortality data from 1991 to 2015. We forecast the evolution of the mortality from 2014 to 2040 based on the estimation results in order to evaluate the issue price of a longevity bond of 25 years maturity. As an application, we propose a method to quantify the speed of mortality improvement by the average mean reverting times of the processes.