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題名：房貸灰色信用風險管理模式之建立與應用
作者：楊適予
作者(外文)：Yang, Shih-Yu
校院名稱：銘傳大學
系所名稱：管理科學研究所
指導教授：林進財
學位類別：博士
出版日期：2002
主題關鍵詞：房貸；風險；信用風險；灰色理論；mortgage；risk；credit risk；grey theory
原始連結：連回原系統網址new window
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本文運用灰色理論，建構一個房貸灰色信用風險管理模式，找出影響逾期還款、違約及提前還款之因素，並以此來篩選房貸客戶，此外還針對正常還款、逾期還款、違約及提前還款機率等進行預測。本文主要之研究結果如下：
(一) 本文構建一個新的房貸還款模式，以邏輯迴歸(logistic regression model)及多項邏輯迴歸(multinomial logit regression model)方法，對影響房貸異常還款及還款狀態轉移之因素進行分析。本文探討台灣房貸市場中，影響房貸異常還款之因素，研究發現借款人之收入、教育程度、性別、職業、婚姻狀況及服務年資等對異常還款都有影響。
(二) 本文運用所找出之影響房貸異常還款因素及灰色關聯分析，來替金融機構篩選房貸客戶。本文結果證實，灰色關聯分析篩選房貸客戶之效果優於邏輯迴歸模式。
(三) 本文使用非穩定馬可夫鏈（nonstationary Markov chain）及灰色預測法( Grey forecasting)發展出能預測房貸正常還款、逾期還款、違約及提前還款機率之模式。比較這預測模式和時間數列模式(ARIMA)之效果，結果顯示這預測模式之誤差比時間數列模式之誤差小。
本文研究結果可提供金融機構放款部門經理人，作為提列適當準備金及制定房貸政策之參考。
This paper applies Grey theory to construct a new home mortgage model that fits the Taiwanese situation of slightly delinquent payment and prepayment of home mortgage. The main results as follows:
1. This paper applies the logistic regression model and the multinomial logit regression model to analyze the influence of factors on abnormal payment and loan state transfer. Analytical results indicate that the following factors are significant in explaining abnormal payment: educational level, marital status, loan amount, loan-to-value ratio, loan source, and loan status.
2. This paper applies a novel approach, grey relational analysis, to assess home mortgage applications. Home mortgage application data from 4,153 home mortgage applications, from July 1, 1992 to June 30, 1998, obtained by a major Taiwan financial institution are adopted to judge the discriminating effect of the grey relational analysis. Analysis results validate that the grey relational analysis is as effective as the logistic regression model. The grey relational analysis is, therefore, a promising alternative to the logistic regression model in assessing home mortgage applications.
3. This paper develops a novel forecasting model with nonstationary Markov chain and Grey forecasting, capable of predicting the likelihood of delinquency, default and prepayment. Empirical results indicate that the novel forecasting model with a low error is better than ARIMA.
Taiwanese financial institutions can use the results from this paper when drafting administrative policies for home mortgages.
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