廖咸興臺灣大學:財務金融學研究所盧嘉梧Lu, Chia-WuChia-WuLu2010-05-112018-07-092010-05-112018-07-092009U0001-0206200911003700http://ntur.lib.ntu.edu.tw//handle/246246/182735本研究建立一個整合存量基礎與流量基礎的結構型企業信用風險評估模型。與傳統結構型模型不同點在於，傳統結構型模型僅考慮存量基礎的違約（資產不足以抵償債務）風險，而本研究模型則同時考慮存量基礎的違約型態，以及流量基礎的違約（流動性償付不能）風險，並可內生化地決定企業未來的違約機率。經由數值分析的結果顯示，相對於傳統Merton形式的存量基礎模型傾向於低估短期違約機率，本研究模型具有較能捕捉短期的違約風險之特點；此外，實際應用本模型於評估樣本銀行之違約風險，亦顯示本模型能增進短期違約機率評估之有效性。This study develops an integrated structural-form credit risk model which combines both stock-based and flow-based corporate credit information. The new model differs from traditional structural-form credit models in that it considers not only stock-based default but also flow-based insolvency. This model can generate endogenously a firm’s probabilities of default, resulting from either asset inadequacy or liquidity crunch. Numerical analyses show that the model can catch short-term default risk which is underestimated by traditional Merton-type stock-based models. An application to a bank sample shows that this model is able to improve the effectiveness for evaluating short-term default probabilities.Contents. Introduction 1I. The Model 5. Stock-based Credit Risk Model 5. Flow-based Credit Risk Model 6. Integrated structural-form credit risk model 8II. Numerical Analysis 10V. Preliminary Application to Sample Banks 12. Sample Bank Selection Criteria 13. Model’s Proxies and Parameters Estimation 14. Results Analysis 16. Conclusions and Further Extension 17eference 19ppendix A. Brief introductions for the selected four structural-form credit models 37ablesable 1. Parameters of the sample banks and the numerical analysis 24able 2. Changes of default probability when the flow-based model is included 25able 3. Integrated default probabilities changes by different setting 26able 4. Sensitive analysis of the correlation coefficient 27able 4. Sensitive analysis of the correlation coefficient (Cont.) 28able 5. Sensitive analysis of the mean-reverting speed parameter 29able 6. Sensitive analysis of the long-term level parameter 30able 7. Sensitive analysis of the standard deviation parameter 31able 8. Characteristics of the sample banks sorted by SIC codes 32able 9. The distribution of the stock-based model parameters of the sample banks 33able 10. The distribution of the flow-based model parameters of the sample banks 34able 11.Comparisons of 1-year default probabilities estimated by structural-form models 35application/pdf429124 bytesapplication/pdfen-US存量基礎信用風險模型流量基礎信用風險模型償付不能Stock-based Credit ModelFlow-based Credit ModelFlow-based Insolvencythesishttp://ntur.lib.ntu.edu.tw/bitstream/246246/182735/1/ntu-98-D93723008-1.pdf