Solvency II 量化分析──以反向抵押貸款為例;Quantitative Analysis of Solvency II - The Example of Home Reverse Mortgage

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题名:	Solvency II 量化分析──以反向抵押貸款為例;Quantitative Analysis of Solvency II - The Example of Home Reverse Mortgage
作者:	邱煜修;Chiu, Yu-Hsiu
贡献者:	財務金融學系
关键词:	反向抵押貸款;非負權益保證;Solvency II;home reverse mortgage;no negative equity guarantees
日期:	2017-06-23
上传时间:	2017-10-27 13:49:03 (UTC+8)
出版者:	國立中央大學
摘要:	本文探討保險清償監理制度Solvency II的量化分析，並以反向抵押貸款做為例子。非負權益保證是反向抵押貸款契約到期時，累積負債低於房價產生的差額。此差額通常由金融公司或政府承擔，是此商品的主要風險。本文聚焦在房價與死亡率對此風險的影響，並以英國的資料進行量化分析。本文首先以ARMA-GARCH及VAR模型來模擬房價報酬走勢、預測房價走勢，並比較配適度狀況。接著以Lee-Carter (1992)當作死亡率模型，此模型可以衡量壽命延長的現象。最後結合房價模型與死亡率模型進行量化分析，探討不同性別、不同年紀、不同貸款乘數對於最適估計、邊際風險與清償資本要求的影響或趨勢。本研究主要有四個結論：第一，未考慮壽命延長現象會低估量化數字；第二，清償資本要求主要來自房價衝擊的影響；第三，女性的量化數字會略大於男性；第四，不同房價模型對於量化數字有顯著的差異。;The thesis study on quantitative analysis of solvency II in insurance liquidation supervision system - taking home reverse mortgage for example. No negative equity guarantees (NNEG) is the difference which outstanding liability is lower than housing price when the home reverse mortgage contract expires. This difference is usually borne by a financial company or government and is a major risk for this contract. The thesis focus on the impact of housing prices and mortality on this risk, and use UK data for quantitative analysis. In the thesis, the ARMA-GARCH and VAR models are first used to simulate the trend of housing price return, forecast the trend of housing price, and compare the fitness of these two models with each other. Second, Lee-Carter (1992) model is used as a mortality model. This model can measure the phenomenon of prolonged life. Finally, the thesis combines the housing price model with the mortality model for quantitative analysis, analyzing the impact or trend of different gender, age, and loan to reverse on best estimate, risk margin and solvency capital requirement. There are four main conclusions of this study. First, not considering the phenomenon of prolonged life will underestimate the quantitative number. Second, solvency capital requirement is mainly from the impact of housing price shock. Third, quantitative numbers on female are slightly higher than numbers on male. Fourth and last, different housing price models have significant differences in quantitative number.
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