摘要翻译：
本文描述了一种灵活、易于处理的自下而上动态相关建模框架，该框架具有一致的随机恢复规范。随机恢复规范只对即期恢复率的前两个矩进行建模，因为其较高的矩对损失分布和CDO分期定价几乎没有贡献。鉴于只需要违约指标的联合分布来构建证券组合损失分布，我们提出了一个通用的违约指标copulas类来建模CDO段，它可以很容易地校准到多个期限的指数段价格。这种相关性建模框架具有独特的优点，即违约时间的联合分布和模型的其他动态性质可以与损失分布和分阶段价格分开改变。在对模型进行标定后，可以将现有的自顶向下的方法应用到公共因子过程中，在不改变已标定的分段价格的情况下构建非常灵活的系统动力学。因此，这种建模框架结合了自下而上和自上而下模型的最佳特征：它与所有单一名称的市场信息完全一致，并允许非常丰富和灵活的传播动态。本文还给出了该模型框架的非参数实现的数值结果。即使在极端的市场条件下，非参数实现了对多个期限的指数段的快速和准确的校准。本文还提出了一种条件马尔可夫链方法来构造系统动力学，该方法支持一种有效的动态价差工具的格子定价方法。我们还展示了如何定价分阶段期权作为这个快速格子方法的一个例子。
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英文标题：
《A Dynamic Correlation Modelling Framework with Consistent Stochastic
  Recovery》
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作者：
Yadong Li
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最新提交年份：
2010
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Pricing of Securities        证券定价
分类描述：Valuation and hedging of financial securities, their derivatives, and structured products
金融证券及其衍生产品和结构化产品的估值和套期保值
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英文摘要：
  This paper describes a flexible and tractable bottom-up dynamic correlation modelling framework with a consistent stochastic recovery specification. The stochastic recovery specification only models the first two moments of the spot recovery rate as its higher moments have almost no contribution to the loss distribution and CDO tranche pricing. Observing that only the joint distribution of default indicators is needed to build the portfolio loss distribution, we propose a generic class of default indicator copulas to model CDO tranches, which can be easily calibrated to index tranche prices across multiple maturities. This correlation modelling framework has the unique advantage that the joint distribution of default time and other dynamic properties of the model can be changed separately from the loss distribution and tranche prices. After calibrating the model to index tranche prices, existing top-down methods can be applied to the common factor process to construct very flexible systemic dynamics without changing the already calibrated tranche prices. This modelling framework therefore combines the best features of the bottom-up and top-down models: it is fully consistent with all the single name market information and it admits very rich and flexible spread dynamics. Numerical results from a non-parametric implementation of this modelling framework are also presented. The non-parametric implementation achieved fast and accurate calibration to the index tranches across multiple maturities even under extreme market conditions. A conditional Markov chain method is also proposed to construct the systemic dynamics, which supports an efficient lattice pricing method for dynamic spread instruments. We also showed how to price tranche options as an example of this fast lattice method.
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PDF链接：
https://arxiv.org/pdf/1004.3758