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研究生: 柯志龍
Chih-lung Ke
論文名稱: 利用Kernel Regression模型預測台灣貸款違約率之研究
Estimating Default Rates on Mortgage Loans with The Kernel Regression Model: A case of Taiwan
指導教授: 張光第
Guang-di Chang
口試委員: 黃彥聖
Yen-Sheng Huang
莊文議
Wen-I Chuang
學位類別: 碩士
Master
系所名稱: 管理學院 - 財務金融研究所
Graduate Institute of Finance
論文出版年: 2008
畢業學年度: 96
語文別: 英文
論文頁數: 29
中文關鍵詞: 違約率無母數Kernel 模型邏輯斯迴歸Probit 模型抵押貸款
外文關鍵詞: mortgage, default probabilities, nonparametric, kernel regression, Logistic regression, Probit model
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  • 本篇論文的主要目的為利用Kernel迴歸模型來預測台灣抵押貸款違約率.過去大部分的文獻皆以有母數的統計方法做為實證的模型. LaCour-Little and Maxam (2001) 則以KR模型預測抵押貸款證券(MBS)的提前清償率,發現無母數的統計方法能有效的應用在違約模型.所以本篇參考LaCour-Little and Maxam (2001)的KR模型,資料是台灣北部某商業銀行提供,期間為2003年的12月到2006年6月的抵押貸款資料.我們發現(1)每月所得,LTV ratio,貸款額度,存款餘額,婚姻狀況以及教育程度對貸款者違約率有顯著性的影響.(2)在比較整體預測能力上,KR模型的預測能力皆比兩各有母數的統計模型(邏輯斯迴歸以及Probit 模型)來的好.(3)另外不管在Type I or Type II 誤差,KR模型的誤差率皆比輯斯迴歸以及Probit 模型來的低.


    This paper is to estimate mortgage loan default probabilities in Taiwan’s mortgage market using a nonparametric kernel regression (KR) model. Many past studies utilize primarily the proportional hazard model or logistic regression (LR) as a technique for modeling and predicting default rates. LaCour-Little and Maxam (2001) use KR to estimate prepayment rates and find that the technique contributes to default modeling. Hence, we employ the kernel model to predict default rates using residential mortgage data between December 2003 and June 2006 from a financial holdings company in Taiwan. Our findings are as follows: (1) Income, Loan To Property Value ratio ( LTV ratio), Amount of Loan, Savings Deposit, Marital Status, and Education have significant impacts on the default of debtors, and LTV ratio, Marital Status, and Education are positively correlated with default rates, while Amount of Loan and Savings Deposit are negatively correlated with default rates; (2) The overall prediction accuracy of the KR default forecasting model, LR and Probit model are 81.96%, 66.99% and 66.94, respectively; (3) For either Type I or Type II errors, the nonparametric KR model provides a lower forecast error rates than the parametric LR and Probit model. We conclude that the nonparametric KR is shown to exhibit superior predictive ability compared with the LR model. Lenders can use all available data as the basis for early credit evaluation and employ the proposed nonparametric KR model to forecast default risks.

    1. Introduction-------------------------------------------1 2. Literature Review--------------------------------------3 2.1 Parametric Models----------------------------------3 2.2 KR and Other Nonparametric Models------------------4 2.3 Variables for Default Modeling---------------------5 3. The Model---------------------------------------------13 3.1 Nonparametric Kernel Regression Models------------13 3.2 Logistic Regression-------------------------------15 3.3 Probit Model--------------------------------------16 4. Relevant Variables and Data---------------------------16 5. Empirical Results-------------------------------------18 5.1 Descriptive Statistics----------------------------18 5.2 Logistic Regression and Probit Model--------------19 5.3 Kernel Regression Model---------------------------22 6. Conclusions and Future Extensions---------------------24 References-----------------------------------------------25

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