根據Das and Sundaram (2004) 發展的模型，Bandreddi et al. (2007) 在CDO評價模型中使用了Das and Sundaram (2004) 模擬違約相關的特殊觀念，並在模型中使用了權益二元樹及大量CDS市場資料來校正模型參數發展了D-CRR模型。相較於其它評價信用商品的模型其輸入資料更貼近並反應現實世界。由於目前尚未有研究指出Bandreddi et al. (2007) 其模型使用的績效如何，故本研究將採用國外指數iTraxx 為合成式CDO合約，使用Bandreddi et al. (2007) 與一因子Gaussian copula二種模型分別模擬並產生分券價差，再將其結果與iTraxx Europe指數市場分券價差分別計算誤差，分析其二種模型的評價優劣程度。實證結果顯示，使用大量市場資料校正模型參數的D-CRR模型較一因子Gaussian copula方法準確。

Based on the models proposed by Das and Sundaram (2004), Bandreddi, Das, and Fan (2007) extend Das and Sundaram (2004)’s model to simulate the concept of related default on the binomial tree. More specifically, Bandreddi et al. (2007) use the equity binomial tree and plenty market data from CDS market to calibrate the model parameters. Therefore, this model is more practical theoretically, comparing to other models valuing the credit products. Because not yet has the research to indicate the achievements of Bandreddi et al. (2007)’s model at present, this research will use iTraxx Europe index CDO contracts to compare the performance of Bandreddi et al. (2007)’s D-CRR model and the one factor Gaussian copula model for pricing the tranche spreads. We figure out the sum of squares of all tranche spreads between each model and the market price for the CDO contracts of iTraxx Europe index as the estimation for comparing these two models. The results show that the D-CRR model is more accurate than the one factor Gaussian copula model.

Bandreddi, Santhosh, Sanjiv Das, and Rong Fan (2007). “Correlated Default Modeling with a Forest of Binomial Trees,” Journal of Fixed Income, Vol. 17, No. 3, pp.38–56.

Black, Fischer and John C. Cox (1976), “Valuing Corporate Securities: Some Effects of Bond Indenture Provisions,” Journal of Finance, Vol. 31, No. 2 , pp. 351-367.

Das, Sanjiv., and Rangarajan Sundaram (2004). “A Simple Model for Pricing Derivative Securities with Equity, Interest-rate and Default Risk,” Working paper, Santa Clara University.

Das, Sanjiv., and Gary Geng (2004). “Correlated Default Processes: A Criterion-Based Copula Approach,” Journal of Investment Management 2(2), 44-70.

Duffie, Darrel, and Kenneth J. Singleton (1999). “Modeling Term Structures of Defaultable Bonds,” Review of Financial Studies, Vol. 12, No.4, pp. 687–720.

Hull, John, Izzy Nelken, and Alan White (2003). “Merton’s Model, Credit Risk, and Volatility Skews,” working paper, University of Toronto

Jarrow, Robert A. and Stuart M. Turnbull (1995). “Pricing Derivatives on Financial Securities Subject to Credit Risk,” Journal of Finance, Vol. 50, No. 1, pp. 53-85.

Jarrow, Robert A. and Philip Protter (2004). “Structural Versus Reduced Form Models: A New Information Based Perspective,” Journal of Investment Management, Vol.2, No. 2, pp. 1–10.

Li, David, X. (2000). “On Default Correlation: A Copula Function Approach,” Journal of Fixed Income 9(4), 43-54.

Meneguzzo, Davide. and Walter Vecchiato (2004), “Copula Sensitivity in Collateralized Debt Obligations and Basket Default Swaps,” The Journal of Futures Markets, Vol. 24(1), pages 37-70.

Merton, Robert. (1974). “On the Pricing of Corporate Debt: The Risk Structure of Interest Rates,” Journal of Finance 29, 449-470.

Schönbucher, Philipp. (2002). “A Tree Implementation of a Credit Spread Model for Credit Derivatives,” Journal of Computational Finance 6(2), 1-38.

Schönbucher, Philipp. (2005). “Portfolio Losses and the Term Structure of Loss Transition Rates: A New Methodology for the Pricing of Portfolio Credit Derivatives,” Working paper, ETH, Zurich.

Schönbucher, Philipp. (2003). Credit Derivatives Pricing Models: Models, Pricing
and Implementation. Hoboken, New Jersey: Wiley Finance.

Sidenius, Jakob., Vladimir Piterbarg, and Leif Andersen (2004). “A New Framework for Dynamic Credit Portfolio Loss Modeling,” Working paper, Royal Bank of Scotland.

Zhou, Chunsheng (2001) “An Analysis of Default Correlation and Multiple Defaults,” Review of Financial Studies 14, pp. 555-576