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題名 死亡率模型、保單貼現與最適保險之研究
Three Essays on Life Insurance
作者 宮可倫
貢獻者 蔡政憲
宮可倫
關鍵詞 死亡率模型
保單貼現
最適保險
日期 2016
上傳時間 4-一月-2017 11:55:57 (UTC+8)
摘要 本篇論文共包含三章。第一章討論一個近似因子模型(approximate factor model)在死亡率模型上的應用，死亡率模型應用需要考慮大量的死亡率變數，但現有的資料年數卻比死亡率變數還少。在多族群死亡率的應用上需考慮的變數更多，且成長的速度較資料更新的速度更快。在這種情形下，若我們使用因素分析去估計死亡率的因子，則估計方法無法保證因子有統計上的一致性。此時，我們應採用近似因子模型的架構進行統計推論。在應用上，我利用近似因子模型中的三個模型(APCA、HFA與POET)估計單國與多國死亡率，結果發現在小樣本的情形下，近似因子模型在配適與預測皆較傳統模型準確。最後我以Diebold-Mariano Test來檢定近似因子模型的預測準確度，結果顯示近似因子模型在綜合考慮期望值與變異數的結果下較傳統模型準確。第二章討論保單貼現(Life settlement)的利差決定因子(yield determinant)。我利用美國一間公司的實際交易資料估計決定保單貼現預期利差的因素，結果發現除了年齡、性別以外，保險公司的評等與被保險人之間健康程度的不同也會影響價格。此外，我也發現在資料期間內(2009-2011)股票市場的溢酬並不影響利差，顯示保單貼現有可能成為zero-beta的投資標的。第三章討論一個最適保險的問題。我在Munk and Sorensen (2010)的金融市場與Huang, Milevsky, and Wang (2008)的目標函數架構下，推導出家戶最適的消費、投資與保險需求。另外，我估計保險的附加費用對消費、投資與保額的影響。結果發現附加費用會透過人力資本影響最適投資與消費。
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描述 博士
國立政治大學
風險管理與保險研究所
99358501
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0993585011
資料類型 thesis
dc.contributor.advisor 蔡政憲zh_TW
dc.contributor.author (作者) 宮可倫zh_TW
dc.creator (作者) 宮可倫zh_TW
dc.date (日期) 2016en_US
dc.date.accessioned 4-一月-2017 11:55:57 (UTC+8)-
dc.date.available 4-一月-2017 11:55:57 (UTC+8)-
dc.date.issued (上傳時間) 4-一月-2017 11:55:57 (UTC+8)-
dc.identifier (其他 識別碼) G0993585011en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/105591-
dc.description (描述) 博士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) 99358501zh_TW
dc.description.abstract (摘要) 本篇論文共包含三章。第一章討論一個近似因子模型(approximate factor model)在死亡率模型上的應用，死亡率模型應用需要考慮大量的死亡率變數，但現有的資料年數卻比死亡率變數還少。在多族群死亡率的應用上需考慮的變數更多，且成長的速度較資料更新的速度更快。在這種情形下，若我們使用因素分析去估計死亡率的因子，則估計方法無法保證因子有統計上的一致性。此時，我們應採用近似因子模型的架構進行統計推論。在應用上，我利用近似因子模型中的三個模型(APCA、HFA與POET)估計單國與多國死亡率，結果發現在小樣本的情形下，近似因子模型在配適與預測皆較傳統模型準確。最後我以Diebold-Mariano Test來檢定近似因子模型的預測準確度，結果顯示近似因子模型在綜合考慮期望值與變異數的結果下較傳統模型準確。第二章討論保單貼現(Life settlement)的利差決定因子(yield determinant)。我利用美國一間公司的實際交易資料估計決定保單貼現預期利差的因素，結果發現除了年齡、性別以外，保險公司的評等與被保險人之間健康程度的不同也會影響價格。此外，我也發現在資料期間內(2009-2011)股票市場的溢酬並不影響利差，顯示保單貼現有可能成為zero-beta的投資標的。第三章討論一個最適保險的問題。我在Munk and Sorensen (2010)的金融市場與Huang, Milevsky, and Wang (2008)的目標函數架構下，推導出家戶最適的消費、投資與保險需求。另外，我估計保險的附加費用對消費、投資與保額的影響。結果發現附加費用會透過人力資本影響最適投資與消費。zh_TW
dc.description.tableofcontents 1 Introduction 4
2 Multi-population Mortality Modeling: When the Data is Too Much and Not Enough 8
1 Introduction.................... 8
2 The approximate factor model ......... 12
2.1 The basic setup.............. 12
2.2 Motivation for approximate factor model .................. 14
2.3 The benchmark case for approximate factor model .... 15
2.4 Asymptotic PCA............. 17
2.5 Heteroskedasticity Factor Analysis ...................... 18
2.6 Principal Orthogonal complEment Thresholding estimator .. 20
2.7 Benchmark Example:USA ....... 21
3 Fitting performance for single population ...................... 23
4 Multiplepopulations............... 25
5 Multi-population performance.......... 28
5.1 Other models............... 29
5.2 The difference to augmented common factor model ... 30
5.3 Goodness-of-fit .............. 31
5.4 Forecast comparison ........... 33
6 Conclusion .................... 35
3 Explaining the Yield Spread of Life Settlements 51
1 Introduction.................... 51
2 Life settlement samples ............. 55
3 Calculating expected returns and risk premiums . . . . . . . . . . . . . . . . . . 55
4 Possible sources of the spreads and variable specifications . . . . . . . . 58
5 Empirical results ................. 61
6 Conclusion .................... 62
4 Optimal Consumption and Investment Problem Incorporating the Life Insurance Decision: Continuous Time Case 73
1 Introduction.................... 73
2 Marketframework ................ 76
2.1 Financial assets.............. 76
2.2 Life insurance............... 77
2.3 Labor income............... 78
2.4 Household’s decision........... 79
3 Solution of optimal strategies .......... 81
4 Numerical example................ 84
4.1 Wealth and consumption ........ 84
4.2 Optimal investment strategies...... 85
4.3 The effect of loading and optimal insurance demand .. 86
5 Conclusion .................... 89		zh_TW
dc.format.extent 1133343 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0993585011en_US
dc.subject (關鍵詞) 死亡率模型zh_TW
dc.subject (關鍵詞) 保單貼現zh_TW
dc.subject (關鍵詞) 最適保險zh_TW
dc.title (題名) 死亡率模型、保單貼現與最適保險之研究zh_TW
dc.title (題名) Three Essays on Life Insuranceen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Bai, J., 2003, Inferential theory for factor models of large dimensions. Econometrica, 71, 1, 135–171.
Bai, J. and S. Ng, 2002, Determining the number of factors in approximate factor models. Econometrica, 70, 1, 191–221.
Bell, W. and B. Monsell, 1991, Using principal components in time series modelling and forecast- ing of age specific mortality rates. Paper presented at the American Statistical Association 1991 Proceedings of the Social Statistics Section.
Booth, H. and L. Tickle, 2008, Mortality modelling and forecasting: a review of methods. Annals of Actuarial Science, 3, 3–43.
Brouhns, N., M. Denuit, and J. K. Vermunt, 2002, A poisson log-bilinear regression approach to the construction of projected lifetables. Insurance: Mathematics and Economics, 31, 3, 373–393.
Cairns, A. J., D. Blake, K. Dowd, G. D. Coughlan, and M. Khalaf-Allah, 2011, Bayesian stochastic mortality modelling for two populations. ASTIN Bulletin, 41, 29–59.
Cairns, A. J. G., D. Blake, and K. Dowd, 2008, Modelling and management of mortality risk: a review. Scandinavian Actuarial Journal, 2008, 2-3, 79–113.
Chamberlain, G. and M. Rothschild, 1983, Arbitrage, factor structure, and mean-variance anal- ysis on large asset markets. Econometrica, 51, 5, 1281–304.
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