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題名 Age-specific copula-AR-GARCH mortality models
作者 Lin, T. ; Wang, Chouwen ; Tsai, C.C.L.
王昭文
貢獻者 風管系
關鍵詞 Stochastic mortality model; AR-GARCH; Copula; Mortality dependence; Lee–Carter model
日期 2015-03
上傳時間 25-Mar-2015 10:14:17 (UTC+8)
摘要 In this paper, we propose AR-GARCH (autoregression-generalized autoregressive conditional heteroskedasticity) models to fit and forecast mortality rates for a given age by two alternative approaches. Specifically, one approach is to fit a time series of mortality rates for some age to an AR(n)-GARCH(1, 1) model, and project the mortality rate for that age in the next nth year; the other is to fit an AR(1)-GARCH(1, 1) model, and project the mortality rates recursively for the age in the next consecutive years. Further, we employ the copula method to capture the inter-age mortality dependence. Adopting mortality data of Japan, the UK, and the USA, we demonstrate that it is indispensable to consider the conditional heteroskedasticity in our mortality models which provide better performances in out-of-sample projection and prediction intervals with a higher degree of coverage than the Lee–Carter model. Finally, we numerically illustrate with mortality data of Japan that VaR (Value at Risk) values for longevity risk, regarded as additional reserves for annuity or pension providers, will be overestimated if the conditional heteroskedasticity or/and the inter-age mortality dependence structure are ignored.
關聯 Insurance: Mathematics and Economics,61,110-124
資料類型 article
DOI http://dx.doi.org/10.1016/j.insmatheco.2014.12.007
dc.contributor 風管系-
dc.creator (作者) Lin, T. ; Wang, Chouwen ; Tsai, C.C.L.-
dc.creator (作者) 王昭文-
dc.date (日期) 2015-03-
dc.date.accessioned 25-Mar-2015 10:14:17 (UTC+8)-
dc.date.available 25-Mar-2015 10:14:17 (UTC+8)-
dc.date.issued (上傳時間) 25-Mar-2015 10:14:17 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/74023-
dc.description.abstract (摘要) In this paper, we propose AR-GARCH (autoregression-generalized autoregressive conditional heteroskedasticity) models to fit and forecast mortality rates for a given age by two alternative approaches. Specifically, one approach is to fit a time series of mortality rates for some age to an AR(n)-GARCH(1, 1) model, and project the mortality rate for that age in the next nth year; the other is to fit an AR(1)-GARCH(1, 1) model, and project the mortality rates recursively for the age in the next consecutive years. Further, we employ the copula method to capture the inter-age mortality dependence. Adopting mortality data of Japan, the UK, and the USA, we demonstrate that it is indispensable to consider the conditional heteroskedasticity in our mortality models which provide better performances in out-of-sample projection and prediction intervals with a higher degree of coverage than the Lee–Carter model. Finally, we numerically illustrate with mortality data of Japan that VaR (Value at Risk) values for longevity risk, regarded as additional reserves for annuity or pension providers, will be overestimated if the conditional heteroskedasticity or/and the inter-age mortality dependence structure are ignored.-
dc.format.extent 1314893 bytes-
dc.format.mimetype application/pdf-
dc.relation (關聯) Insurance: Mathematics and Economics,61,110-124-
dc.subject (關鍵詞) Stochastic mortality model; AR-GARCH; Copula; Mortality dependence; Lee–Carter model-
dc.title (題名) Age-specific copula-AR-GARCH mortality models-
dc.type (資料類型) articleen
dc.identifier.doi (DOI) 10.1016/j.insmatheco.2014.12.007en_US
dc.doi.uri (DOI) http://dx.doi.org/10.1016/j.insmatheco.2014.12.007en_US