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研究生:陳柏仁
研究生(外文):CHEN, PAO-JEN
論文名稱:台灣壽險業經驗資料的死亡率模型與死亡風險資本分析
論文名稱(外文):An Empirical Study on the Mortality Modelling and Mortality Risk Capital based on the Data of Taiwan Life Insurance Industry
指導教授:詹芳書詹芳書引用關係
口試委員:田峻吉黃雅文
口試日期:2020-05-13
學位類別:碩士
校院名稱:東吳大學
系所名稱:財務工程與精算數學系
學門:數學及統計學門
學類:其他數學及統計學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:63
中文關鍵詞:死亡率風險資本IFRS 17保險資本標準
外文關鍵詞:Mortality RateCapital RequirementIFRS 17Insurance Capital Standard
相關次數:
  • 被引用被引用:2
  • 點閱點閱:378
  • 評分評分:
  • 下載下載:68
  • 收藏至我的研究室書目清單書目收藏:0
我國保險產業預計將於2025(或2026)年起同時適用國際財務報導準則第17號公報及保險資本標準,前者關乎保險合約負債的評價,後者則將對保險業的清償能力評估方式帶來改變。本研究探討在兩者共同實施的條件下,適用於台灣壽險業經驗資料的死亡率模型。經由死亡率模型的建立,本研究進一步分析死亡率的波動度,並與國際保險資本標準的死亡率波動度進行比較。除了波動度之外,本研究以簡易的終身壽險為例,從給付的現金流量出發,探討死亡率的選用對於最佳估計負債及風險資本的影響,期望能從不同面向,探討模型選用的重要性。研究結果顯示:(1)對於台灣壽險業經驗資料之短期一年的死亡率預估,以LC族群、APC模型及Plat模型的準確度較為優異;(2)比較各模型之下的死亡率波動度,LC模型的波動度最低,而Plat模型的波動度則最高,故死亡風險資本計提若趨向保守即可選擇Plat模型,反之則選擇LC模型;(3)若考慮採用國際保險資本標準建議的死亡率波動度,則較有利於以男性為被保險人的壽險保單。
Insurance industry in Taiwan expects to apply for both IFRS 17 and ICS in 2025 (or 2026). IFRS 17 is related to the evaluation of contractual liability insurance, and ICS will change to the assessment method of the solvency of the insurance industry. This paper analyzes mortality model suitability on the data of Taiwan life insurance industry in both systems. We further use the mortality model to analyze mortality shock level and compare it with mortality shock level addressed by ICS. We use whole life insurance policy as an example, starting from the cash flow of benefits, we explore the impacts of the choices of mortality models for the best estimate of liabilities and capital requirement. We expect to investigate the importance of model selection from various aspects. Our results show that (1) for the one-year mortality forecast of Taiwan life insurance industry, the accuracy estimated by the LC family of models, APC model and the Plat model are acceptable; (2) if the accrual of capital requirement of mortality risk tends to be conservative, the Plat model should be chosen, otherwise, LC model should be chosen; and (3) the mortality shock level addressed by ICS is more lenient for male insured policies in Taiwan’s life insurance.
第一章 前言 1
第二章 文獻回顧 3
第三章 國際財務報導準則第17號公報與保險資本標準 5
第四章 研究資料 7
第五章 研究方法與模型 11
第六章 死亡風險資本評估 28
第七章 結論與建議 39
參考文獻 40
附錄一 各模型之轉換公式 42
附錄二 各模型配適之誤差指標 44
附錄三 各模型預估死亡率之誤差指標 46
附錄四 各模型預估平均餘命之誤差指標 48
附錄五 各模型預估定期年金之誤差指標 52
附錄六 各年度暴露數之加權比重圖 56

中文文獻
1.王信忠、金碩與余清祥,2012,小區域死亡率推估之研究,人口學刊,45:121-145。
2.保險事業發展中心,2018,新一代風險資本額制度研究-保險風險資本的方法論與實證更新研究報告,主持人:詹芳書。

英文文獻
1.Brouhns, N., Denuit, M., and Vermunt, J.K., 2002, “A Poisson Log-bilinear Regression Approach to the Construction of Projected Life Tables.” Insurance: Mathematics and Economics, 31(3), 373-393.
2.Butt, Z., Haberman, S., 2009, “ilc: a collection of R functions for fitting a class of Lee-Carter mortality models using iterative fitting algorithms.”
3.Cairns, A. J. G., Blake, D., Dowd, K., Coughlan, G. D., Epstein, D., Ong, A., Balevich, I., 2009, “A Quantitative Comparison of Stochastic Mortality Models Using Data from England and Wales and the United States.” North American Actuarial Journal, 13(1), 1-35.
4.Cairns, A. J. G., Blake, D., Dowd, K., 2006, “A Two-Factor Model for Stochastic Mortality with Parameter Uncertainty: Theory and Calibration.” Journal of Risk & Insurance, 73(4), 687-718.
5.Cairns, A. J. G., Blake, D., Dowd, K., Coughlan, G.D., Epstein, D., and Khalaf-Allah, M., 2008, “Mortality density forecasts: An analysis of six stochastic mortality models.” Pensions Institute Discussion Paper PI-0801.
6.Currie, I. D., 2006, “Smoothing and Forecasting Mortality Rates with P-Splines.”
7.Gompertz, B., 1825, “On the Nature of the Function Expressive of the Law of Human Mortality, and on a New Mode of Determining the Value of Life Contingencies.” Philosophical Transactions of the Royal Society of London, 115, 513-583.
8.Haberman S., Renshaw A., 2011, “A Comparative Study of Parametric Mortality Projection Models.” Insurance: Mathematics and Economics, 48, 35-55.
9.Lee, R.D., Carter, L., 1992, “Modeling and Forecasting the Time Series of U.S. Mortality.” Journal of the American Statistical Association, 87(419), 659-675.
10.Lewis E. B., 1982, “Control of Body Segment Differentiation in Drosophila by the Bithorax Gene Complex.” Embryonic Development, Part A: Genetics Aspects, Edited by Burger, M. M. and R. Weber. Alan R. Liss, New York, 269-288.
11.Plat. R., 2009, “On Stochastic Mortality Modeling.” Insurance: Mathematics and Economics, 45(3), 393-404.
12.Renshaw, A.E. and Haberman, S., 2006, “A Cohort-Based Extension to the Lee-Carter Model for Mortality Reduction Factors.” Insurance: Mathematics and Economics, 38, 556-570.
13.Smith, S. K. and T. Sincich., 1990, “The Relationship between the Length of the Base Period and Population Forecast Errors.” Journal of the American Statistical Association 85(410), 367-375.
14.Yue, C.J., 2002, “Oldest-Old Mortality Rates and the Gompertz Law: A Theoretical and Empirical Study Based on Four Countries.” Journal of Population Studies, 24, 33-58.
15.Villegas, A. M., P. Millossovich, and V. K. Kaishev., 2015, “StMoMo: An R Package for Stochastic Mortality Modelling.”

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