信用連結債券的價值主要取決於所連結資產池內的資產違約情況，因此過去有許多文獻在評價時會利用Copula模擬各資產的違約時點，或是用Factor Copula估算他們在各時點下的違約機率。而本研究以Gaussian Factor Copula模型為主軸，對資產池違約機率做估計，以得到連結該資產池的信用連結債券價值。但過去文獻較常以給定參數的方式進行評價，本研究進一步利用市場實際資料估出模型參數並加入產業因子，以期達到符合市場的效果。
本研究利用已知的違約資訊對照模型結果，發現在給定原油價格成長率、產業GDP成長率及CAPM殘差之後，使用Factor Copula模型在資產池小且違約比例過高時容易低估損失，主要原因在於各資產的違約機率並非逼近1。且模型算出的預期損失會隨著距今時間變長而增加，但若資產池實際上沒有更多違約公司，模型的結果就可能會高估損失。而所有的變數又以參考價差對該商品價值的影響最大，因參考價差的數值取決於該公司的信用評等，因此可知信用連結債券價值主要還是與各公司信評有最大相關。

The value of credit linked notes depends on whether the reference entities in the linked asset pool default or not, so some previous studies used Copula model to simulate the times to default or Factor Copula model to get the default probability. In this paper, with the Gaussian Factor Copula model adopted and industry factors taken into account, the default probability is estimated in order to obtain the value of the credit linked notes. Then, unlike other previous studies using the given parameters, this paper evaluated the parameters by using the model as well as market data, hoping to achieve the goal that results can reflect the real market situation.
With real default information compared with the modeling results, three findings can be drawn given the growth rate of oil price, the growth rate of industrial GDP and the residuals of CAPM. First, the loss will be underestimated if the asset pool is small and the default proportion is too high mainly because not all the default probability approximates one. Second, expected default probability will be directly proportional to the time period between the present and the expected moment. So if there are not so many defaulting companies, then the loss might be overestimated. Last, the reference spread has the most impact on the product value among all the variables, and as we know, the reference spread of a company depends on its credit rating. Therefore, compared with other factors, credit rating remains the most essential to credit linked notes.

英文部分
[1] Davis, M. and V. Lo (2001). "Infectious defaults." Quantitative Finance 1: p. 382-397.
[2] Duan, J.-C. (2010). Clustered Defaults. Risk magazine: p. 87-91.
[3] Duan, J.-C. and A. Fulop (2012). "Multiperiod Corporate Default Prediction with the Partially-Conditioned Forward Intensity." Working Paper.
[4] Gregory, J. and J.-P. Laurent (2004). "In the Core of Correlation." Risk 17: p. 87-91.
[5] Hull, J. and A. White (2004). "Valuation of a CDO and an nth to Default CDS Without Monte Carlo Simulation." Journal of Derivatives 12(2): p. 8-23.
[6] Hwang, R.-C. (2012). "A varying-coefficient default model." International Journal of Forecasting 28: p. 675-688.
[7] Jarrow, R. A. and F. YU (2001). "Counterparty Risk and the Pricing of Defaultable Securities." Journal of Finance 56: p. 1765-1799.
[8] Li, D. X. (2000). "On Default Correlation: A Copula Function Approach." Journal of Fixed Income: p. 41-50.
[9] Longstaff, F., et al. (2004). "Corporate Yield Spreads: Default Risk or Liquidity? New Evidence from the Credit-Default Swap Market." Working Paper.
[10] Rosch, D. and B. Winterfeldt (2008). Estimating Credit Contagion in a Standard Factor Model. Risk: p. 78-82.

中文部分
[1] 李美儀 (2005). "信用衍生性商品之擔保債權憑證之評價與分析." 國立政治大學金融研究所碩士論文.
[2] 張耀洲 (2004). "擔保債權憑證之評價－BET、Copula與Factor Copula方法之比較與分析." 國立政治大學金融研究所碩士論文.
[3] 郭銚倫 (2005). "信用評等分組下之合成型CDO評價." 國立政治大學金融研究所碩士論文.