| dc.contributor.advisor | 黃泓智 | zh_TW |
| dc.contributor.author (作者) | 洪德全 | zh_TW |
| dc.contributor.author (作者) | Hong, De Chuan | en_US |
| dc.creator (作者) | 洪德全 | zh_TW |
| dc.creator (作者) | Hong, De Chuan | en_US |
| dc.date (日期) | 2009 | en_US |
| dc.date.accessioned | 4-九月-2013 15:00:37 (UTC+8) | - |
| dc.date.available | 4-九月-2013 15:00:37 (UTC+8) | - |
| dc.date.issued (上傳時間) | 4-九月-2013 15:00:37 (UTC+8) | - |
| dc.identifier (其他 識別碼) | G0097358020 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/60044 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 風險管理與保險研究所 | zh_TW |
| dc.description (描述) | 97358020 | zh_TW |
| dc.description (描述) | 98 | zh_TW |
| dc.description.abstract (摘要) | 隨著醫療技術進步、環境衛生改善與人類追求健康生活的趨勢，全世界人類的死亡率不斷地下降。在死亡率不斷的改善的情形下，保險公司可能在壽險商品上獲利，但在年金部份卻會因長壽風險而有所虧損。 自然避險則是保險公司可行的避險策略之一，即透過公司整體保單的組合，來達到規避死亡率風險和利率風險。此外，不同於之前的相關研究，我們所使用的資料，是由臺灣所有的保險公司提供的經驗死亡率，而不是國民生命表。目前保險公司在定價年金和壽險商品時，使用的死亡率是國民生命表，即假設買年金商品的被保險人和買壽險商品的被保險人的死亡率是相同的。但是從經驗死亡率的資料，我們發現購買年金商品的被保險人，其死亡率會低於買壽險商品的被保險人的死亡率。上述情形，會造成保險商品定價有誤;因此，我們考慮不同性別的年金、壽險的死亡率，並研究這些死亡率之間隨機變動項的相關性，以期在未來死亡率和利率變動下，可以藉由死亡率間的相關性，而抵消總價值變動的變異數和定價差異。 根據經驗資料，我們提出一個模型，可透過調整賣出年金和壽險的比例(年齡、性別)，使得保險公司能夠針對公司整體保單組合，找到並有效地運用的自然避險策略。文中最後進行模型敏感度分析，以及提出可能採用的保險商品配置策略，可作為目前保險公司進行死亡率和利率避險的參考。 | zh_TW |
| dc.description.abstract (摘要) | The mortality rate of human being has decreased year by year due to the improvement of medical and hygienic techniques. With the mortality improvement over time, life insurers may gain a profit and annuity insurers may suffer losses because of longevity risk. However, natural hedging is a feasible strategy to hedge mortality risk and interest risk at the same time. In this paper, we investigate the natural hedging strategy and tryto find an optimal collocation of insurance products to deal with longevity risks for the insurance companies. Different from previous literatures, we use the experiencedmortality rates from life insurance companies rather than population mortality rates.This experienced mortality data set includes more than 50,000,000 policies which are collected from the incidence data of the whole Taiwan life insurance companies. Ingeneral, insurance companies use population mortality rates to price life insurance and annuity products. Nevertheless, the mortality rate of annuity purchasers is averagelylower than that of life insurance purchasers. This situation leads to mispricing problem of both life insurance and annuity products. So in this paper, we canconstruct four mortality tables (gender, product) and investigate the correlation of these stochastic variation terms of four mortality rates. According to the correlationrelation between these four mortality rates, we can offset the variance of portfolio’s change and difference of mispricing. On the basis of the experienced mortality rates, we demonstrate that the proposed model can lead to an optimal collocation of insurance products and effectively applythe natural hedging strategy to a more general portfolio for life insurance companies. | en_US |
| dc.description.tableofcontents | 1. Introduction and Motivation 1 1.1 Agenda 32. Model Setting 4 2.1 Mortality Rate Model 4 2.2 Interest Rate Model 6 2.3 General Portfolio Model 83. Data 11 3.1 Mortality Rate Data 11 3.2 Interest Rate Data 144. Numerical Analysis 15 4.1 Scenario 1 : θ = 0 15 4.2 Scenario 2 : θ = 1 21 4.3 Scenario 3 : 0 < θ < 1 24 4.4 General Portfolio of Insurance Products 295. Conclusion and Suggestion 336. Reference 35 | zh_TW |
| dc.format.extent | 790680 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.language.iso | en_US | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0097358020 | en_US |
| dc.subject (關鍵詞) | 長壽風險 | zh_TW |
| dc.subject (關鍵詞) | 自然避險 | zh_TW |
| dc.subject (關鍵詞) | longevity risk | en_US |
| dc.subject (關鍵詞) | natural hedging | en_US |
| dc.title (題名) | 考慮整體保單組合之最適自然避險策略 | zh_TW |
| dc.title (題名) | An optimal strategy of natural hedging for a general portfolio of insurance companies | en_US |
| dc.type (資料類型) | thesis | en |
| dc.relation.reference (參考文獻) | Carter, L. R. and Lee, R. D. (1992) “Modeling and forecasting U.S. mortality”,Journal of the American Statistical Association, 87(419): 659-675W. Lo(1995) “The Research of Pricing in Taiwan Bills Market”D. Blake and W. Burrows (2001). “Survivor bonds: helping to hedge mortality risk”,Journal of Risk and Insurance 68: 339-348S.C. Chen (2002) “The Evaluation of Value at Risk on Taiwan Bills Portfolio”N. Brouhns, M. Denuit and J.K. Vermunt (2002) “A Poisson log-bilinear regression approach to the construction of projected life-tables”, Mathematicsand Economics, 31: 373-393J. L. Wang, L. Y. Yang, and Y. C. Pan (2003). “Hedging Longevity Risk in Life Insurance Companies”, In Asia-Pacific Risk and Insurance Association, 2003 AnnualMeeting Renshaw, A. E. and Haberman, S. (2003) “ Lee-Carter mortality forecasting with age specific enhancement”, Mathematics and Economics, 33: 255-272Y. Lin, and S. H. Cox (2004). “Natural hedging of life and annuity mortality risks”,Mimeo. Georgia State UniversityY. Lin, and S. H. Cox (2005) “Securitization of Mortality Risks in Life Annuities”,Journal of Risk & Insurance, 72: 227-252MC Koissi, AF Shapiro, G Högnäs (2006) “Evaluating and extending the Lee–Carter model for mortality forecasting: Bootstrap confidence interval”, Mathematics andEconomics, 38: 1-20A. Melnikov and Y. Romaniuk (2006) “Evaluating the performance of Gompertz,Makeham and Lee–Carter mortality models for risk management with unit-linked contracts”, Mathematics and Economics, 39: 310-329K. Dowd, D. Blake, A. J. G. Cairns and P. Dawson (2006) “Survivor Swaps”, Journal of Risk & Insurance, 73: 1-17Cairns, A.J.G., Blake, D., and Dowd, K. (2006b) “A Two-Factor Model for Stochastic Mortality with Parameter Uncertainty: Theory and Calibration”, Journal of Riskand Insurance, 73: 687-718J. L. Wang, H.C. Huang, S. S. Yang, J. T. Tsai (2010) “An Optimal Product Mix For Hedging Longevity Risk in Life Insurance Companies: The Immunization TheoryApproach”, Journal of Risk and Insurance, 77: 473-497 | zh_TW |
