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研究生:林君怡
研究生(外文):Chun-Yi Lin
論文名稱:不同情境樹模型下利率風險量化方法之研究-應用於現金流量測試法
指導教授:周國端周國端引用關係
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:財務金融學研究所
學門:商業及管理學門
學類:財務金融學類
論文種類:學術論文
論文出版年:2005
畢業學年度:93
語文別:中文
論文頁數:40
中文關鍵詞:利率風險現金流量測試法情境樹模型
外文關鍵詞:interest rate riskcash flow testingevent tree
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本研究探討風險基礎資本額(Risk-Based Capital,簡稱RBC)中利率風險之量化方式,比較台灣與美國RBC利率風險量化方式之異同處,並且仿照美國現金流量測試法(Cash Flow Testing)量化不同保單預定利率之生死合險之利率風險值,資產面架構於不同情境樹模型的模擬值,進而比較不同模型下產生資產報酬率和數值的特性以及利率風險值之比較。

主要結論與建議:

一、 保單預定利率越高,利率風險值越大;初始總保戶人數越多,利率風險值越大;資產面報酬率不佳,且保
The main issue of this thesis is to compare the interest rate risk in the RBC of Taiwan with that of the United States and calculate the interest rate risk factors of different guaranteed interest rate policies by Cash Flow Testing. The asset returns of Cash Flow Testing are generated by different event tree models. We compare the features and interest rate risk factors in different event tree models.
The main conclusions and suggestions are as followings:
First, we have higher interest rate risk factor for higher guaranteed interest rate. Also, the larger the policyholders, the larger the interest rate risk factor. When the asset returns are worse and fewer policyholders want to surrender, the interest rate risk factor will be higher. Moreover, we can find that the interest rate risk factors are totally different in different event tree models.
Second, the simulated numbers of model 1 are more diversified and these of model 2 and 3are more centralized. Model 1 is suitable for estimating the interest rate risk and model 2 and model 3 are suitable for the issues about optimal asset allocations.
Thirdly, the original estimation method of interest rate risk of the United States is based on Duration Matching and the formula of interest rate risk of Taiwan and Cash Flow Testing of the United States are based on Cash Flow Matching. We can find that the Cash Flow Matching can describe the interest rate risk much precisely and different models lead to different interest rate factors. We suggest that if the government wants to change the formula of interest rate risk, they can reserve the formula but let the returns of corporate be stochastic. It can let many companies calculate the interest rate risk under the same standard.
第一章 緒論 1
第一節 研究動機與目的 1
第二節 研究架構 2
第二章 文獻回顧 5
第一節 利率風險量化方式 5
第二節 情境樹的建構方式(SCENARIO GENERATION BY EVENT TREE ) 7
第三章 文獻回顧台灣及美國RBC中利率風險量化方式的歷史沿革以及現行制度 11
第一節 台灣部分 11
第二節 美國部分 12
第三節 台灣與美國利率風險量化方式之比較 16
第四章 利用歷史資料依現金流量測試法計算利率風險值 17
第五章 資產面情境樹模擬方法及利率風險值計算 23
第一節 模型之建構 23
第二節 模型之建構 34
第六章 結論與後續研究建議 37
第一節 結論與建議 37
第二節 後續研究建議 38
參考文獻 39
1.梁正德,謝良瑾譯(1992),「美國壽險業風險資本公式」,保險專刊第27輯,P.185-209。
2.鄭濟世(1998),「我國壽險業資本適足性之研究」,台北:財團法人保險事業發展中心,P.27-55。
3.吳佩如(2001),「保險公司的資產負債管理」,壽險季刊121期,P.59-99。
4.李明黛(2001),「利率風險對公司經營之影響:台灣壽險市場之實證研究」,國立政治大學風險管理與保險學系碩士論文。
5.保險業風險資本額制度專案工作小組清償能力分析組 (2002),「風險基礎資本額之利率風險-現金流量情境測試研究報告」。
6.吳志遠(2004),「國民年金基金之資產負債管理問題--隨機規劃方法的應用」,國立臺灣大學財務金融學研究所博士論文。
7.Bernard L. Webb and Claude C. Lilly (1994), Raising the Safety Net : RBC For Life ,P.40-175
8.C-3 Subgroup of the Life Risk-Based Capital Task Force (1999), Phase I Report of the American Academy of Actuaries'' C-3 Subgroup of the Life Risk Based Capital Task Force to the National Association of Insurance Commissioners'' Risk Based Capital Work Group October 1999-Atlanta, GA , American Academy of Actuaries.
9.David R. Carino, Terry Kent, David H. Myers, Celine Stacy, Mike Sylvanus, Andrew L. Turner, Kanji Watanabe and William T. Ziemba (1994),The Russell-Yasuda Kasai Model : An Asset/Liability Model for Japanese Insurance Company Using Multistage Stochastic Programming , Interfaces 24 (1).P29-49
10.Kjetil Hoyland and Stein W. Wallace (2001), Generating Scenario Trees for Multistage Decision Problems, Management Science Vol.47, No.2, P.295-307.
11.Li-Yong YU, Xiao-Dong JI and Shou-Yang WANG (2003) , Stochastic Programming Models in Financial Optimization : A Survey, Advanced Modeling and Optimization Volume 5.
12.National Association of Insurance Commissioners (2004), 2004 Forecasting.
13.Roy Kouwenberg(2001), Scenario generation and stochastic programming models for asset liability management, European Journal of Operational Research 134 , P.279-292.
14.Roy Kouwenberg and Stavros A. Zwnios (2001), Stochastic Programming Models for Asset Liability Management, Working Paper.
15.Swiss Re (2000), Asset-liability management for insurers, Sigma No.6 ,2000.
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