摘要翻译：
本文提出了一种新的非齐次随机游动近似量化算法，该算法是潜在因子模型中CDO序列估值的关键项。该方法基于一个具有内在平稳性的对偶量化算子，从而自动导致弱近似的二阶误差界。我们说明了我们的方法在合成CDO乘积的条件分块函数逼近情况下的数值性能，并与鞍点法和Stein法的逼近结果进行了比较。
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英文标题：
《Dual Quantization for random walks with application to credit
  derivatives》
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作者：
Gilles Pag\`es (PMA), Benedikt Wilbertz (PMA)
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最新提交年份：
2009
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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一级分类：Mathematics        数学
二级分类：Probability        概率
分类描述：Theory and applications of probability and stochastic processes: e.g. central limit theorems, large deviations, stochastic differential equations, models from statistical mechanics, queuing theory
概率论与随机过程的理论与应用：例如中心极限定理，大偏差，随机微分方程，统计力学模型，排队论
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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：
  We propose a new Quantization algorithm for the approximation of inhomogeneous random walks, which are the key terms for the valuation of CDO-tranches in latent factor models. This approach is based on a dual quantization operator which posses an intrinsic stationarity and therefore automatically leads to a second order error bound for the weak approximation. We illustrate the numerical performance of our methods in case of the approximation of the conditional tranche function of synthetic CDO products and draw comparisons to the approximations achieved by the saddlepoint method and Stein's method.
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PDF链接：
https://arxiv.org/pdf/0910.5655