| dc.contributor.advisor | 楊曉文 | zh_TW |
| dc.contributor.author (Authors) | 吳怡潁 | zh_TW |
| dc.contributor.author (Authors) | Wu, Yi-Ying | en_US |
| dc.creator (作者) | 吳怡潁 | zh_TW |
| dc.creator (作者) | Wu, Yi-Ying | en_US |
| dc.date (日期) | 2020 | en_US |
| dc.date.accessioned | 2-Sep-2020 11:51:16 (UTC+8) | - |
| dc.date.available | 2-Sep-2020 11:51:16 (UTC+8) | - |
| dc.date.issued (上傳時間) | 2-Sep-2020 11:51:16 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0107358020 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/131515 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 風險管理與保險學系 | zh_TW |
| dc.description (描述) | 107358020 | zh_TW |
| dc.description.abstract (摘要) | 隨著科技進步，各年齡的死亡率皆隨著時間降低，可預期保險公司年金 商品的保險給付會增加;而死亡率改善的幅度會如何影響 IFRS 17 之下終身 型年金商品的保險負債、並影響公司的盈利虧損，為本文預探探討的議題。為了觀察死亡率改善的影響，本文以 Lee-Carter、CBD 兩個死亡率隨機 模型推估 100 年以後的死亡率，並以 Gompertz 模型針對高齡部分死亡率進 行外插。根據模型預測結果，第二回年金生表高估了高齡人口的死亡率改 善、低估了低齡人口的死亡率改善。接著我們將模型估計之死亡率用於保險負債的計算。研究結果發現，高齡死亡率對最佳估計負債之結果影響較低齡死亡率大。且假設模型預測之死亡率為真實的死亡率、並以第二回年金生命表對終身年金商品進行定價，可能會出現低投保年齡人口保費收取不足、而導致公司虧損的現象。 | zh_TW |
| dc.description.abstract (摘要) | With the advancement of technology, the mortality rates of all ages decrease with time. It can be expected that the benefits of annuity products will increase; therefore, how the improvement in mortality rates will affect the insurance liabilities of whole life annuity products under IFRS 17 and affect the company’s profit and losses are what we will discuss in this paper.In order to observe the impact of mortality improvement, we use the Lee-Carter and CBD stochastic mortality models to estimate the death rates for 100 years, and then use the Gompertz model to extrapolate the senior death rates. According to the predicted mortality rates, the second annuity life table had overestimated the mortality improvement of the senior population and underestimated that of the younger population.Finally, we use the mortality rates we estimated to calculate insurance liabilities. We found that the impact of mortality rate at higher ages on the best estimated liabilities has a greater impact on that of younger ages. In addition, under the actuarial assumptions of this study, the premiums calculated by the second annuity life table may not be sufficient to pay for future annuity benefits when the insured age is low, and cause losses to insurance companies. | en_US |
| dc.description.tableofcontents | 第一章 緒論 7第一節 研究動機 7第二節 研究架構 8第二章 負債公允價值之衡量 9第一節 IFRS 17 負債衡量模型 9第二節 死亡率模型 12第三章 年金商品負債分析 15第一節 保單假設 15第二節 死亡率推估 16第三節 IFRS 4 年金準備金分析 19第四節 IFRS 17 保單未來現金流量現值之估計 20第四章 結論與建議 30第一節 研究結論 30第二節 未來研究方向 31參考文獻 32附錄一 死亡率原始資料 34附錄二 死亡率模型參數估計38附錄三 死亡率模型推估結果 42 | zh_TW |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0107358020 | en_US |
| dc.subject (關鍵詞) | 死亡率改善 | zh_TW |
| dc.subject (關鍵詞) | IFRS 17 | en_US |
| dc.subject (關鍵詞) | Mortality improvement | en_US |
| dc.subject (關鍵詞) | Lee-Carter | en_US |
| dc.subject (關鍵詞) | CBD | en_US |
| dc.subject (關鍵詞) | Gompertz | en_US |
| dc.title (題名) | 死亡率改善對 IFRS 17 負債之影響― 以年金保險商品為例 | zh_TW |
| dc.title (題名) | THE IMPACT OF MORTALITY IMPROVEMENT ON THE LIABILITY UNDER IFRS 17-A CASE OF ANNUITY PRODUCTS | en_US |
| dc.type (資料類型) | thesis | en_US |
| dc.relation.reference (參考文獻) | 吳佩軒。2019。台灣實施 IFRS 17 之資產負債管理研究―以傳統型保險商品為例。 國立政治大學風險管理與保險學(系)研究所碩士學位論文。保險合約負債公允價值評價精算實務處理準則。 2018。中華民國精算學會。 Butt, Z. and Haberman, S. 2010. A Comparative Study of Parametric Mortality Projection Models. Actuarial Research Paper, No. 196, ISBN 978-1-905752-29-4.Cairns, A. J. G., Blake, D., and Dowd, k. 2006. A two-factor model for stochastic mortality with parameter uncertainty: theory and calibration. The Journal of Riskand Insurance, 2006, Vol. 73, No. 4, 687-718European Insurance and Occupational Pensions Authority. 2018. EIOPA’s analysis of IFRS 17 Insurance Contracts. EIOPA-18-717.Haberman, S., and Russolillo, M. 2005. Lee Carter Mortality Forecasting: Application to the Italian Population. Actuarial Research Paper, No. 167, ISBN 1 901615-93-6.Levantesi, S and Pizzorusso, V. 2019. Application of Machine Learning to Mortality Modeling and Forecasting.Ronald D. Lee and Lawrence R. Carter. 1992. Modeling and Forecasting U. S. Mortality. Journal of the American Statistical Association, Vol. 87, No. 419, pp.659-671Swiss Re. 2018. Who Pays for Ageing?William Hines, W., and Verheugen, H.. 2017. IFRS 17-Introduction, Challenges & Opportunities. Milliman.Yue, J. C. Oldest-Old Mortality Rates and the Gompertz Law: A Theoretical and Empirical Study Based on Four Countries. Department of Statistics, National Chengchi University. | zh_TW |
| dc.identifier.doi (DOI) | 10.6814/NCCU202001458 | en_US |
