信用風險因涉及範圍廣，只要金融單位雙方有合同而必須履行義務都有可能發生信用風險。本文以J.P. Morgan 所提出的CreditMetrics模型為基礎，建構一個包含公司債的投資組合並分析其信用風險。切入此問題之觀點在於評價公司債組合類似資產證券化過程，使用copula來描述各資產之間的相依結構 (dependence structure) 而非傳統的常態分配設定方法，再透過factor copula其假設資產值可表示成單因子或多因子的關係式；在單因子的情形下考慮市場因子 (systematic factor) 為主要因子與公司本身的因子。而我們使用主成分分析法來計算每個因素的權重，並加以估算未來一年可能面臨的信用風險損失。其優點為放寬CreditMetrics對資產之間是多元常態分配的假設，改由以股票市場價格代替資產價格來描述資產之間的相關。此外，提出的模型計算上速度也較多元常態為快，並可藉由不同的參數解釋能力來進一步減少計算時間。為了驗證此模型對信用風險評估的效用，另外提出一個易於使用的軟體工具，其可以根據市場上真實放款資料計算風險分配，並提供一些對銀行決策者有幫助的分析。最後並與傳統常態分配比較發現，使用所提出的模型對相關性比較高的產業(例如：同為電子業)所計算出的風險值較使用多元常態分配的為高，符合直觀的想法，也發現提出的方法的確可以顯示出厚尾 ( fat tail) 現象的存在，更接近真實市場上的風險水準。

Almost every finance institution pays lots of attention and energy to deal with credit risk. The default correlations of credit assets have a fatal influence on credit risk. How to model default correlation correctly has become a prerequisite of effective management of credit risk. In this thesis, we provide a new approach to estimate future credit risk on target portfolio based on the framework of CreditMetricsTM by J.P. Morgan. However, we adopt the perspective of factor copula and then bring the principal component analysis concept into factor structure to construct a more appropriate dependence structure among credits. In order to examine the proposed method, we use real market data instead of virtual one. We also develop a tool for risk analysis which is convenient to use, especially for banking loan businesses. The results show the fact that people assume dependence structures are normally distributed will indeed lead to risks underestimate. On the other hand, our proposed method captures better features of risks and shows the fat-tail effects conspicuously even though assuming the factors are normally distributed.

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