摘要翻译：
我们展示了伴随算法微分(AAD)如何允许用蒙特卡罗模拟计算期权价格的相关性风险的非常有效的计算。构造中的一个关键点是使用binning来同时实现计算效率和精确的置信区间。我们给出了基于Copula的蒙特卡罗计算一篮子基础资产索赔的方法，并对投资组合违约期权进行了数值检验。对于一个投资组合中任意数量的基础资产或名称，期权价格相对于所有成对相关性的敏感性是以计算成本获得的，计算成本至多是计算期权价值本身成本的4倍。对于典型的应用，这导致相对于标准方法节省几个数量级的计算量。
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英文标题：
《Fast Correlation Greeks by Adjoint Algorithmic Differentiation》
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作者：
Luca Capriotti and Mike Giles
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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：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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英文摘要：
  We show how Adjoint Algorithmic Differentiation (AAD) allows an extremely efficient calculation of correlation Risk of option prices computed with Monte Carlo simulations. A key point in the construction is the use of binning to simultaneously achieve computational efficiency and accurate confidence intervals. We illustrate the method for a copula-based Monte Carlo computation of claims written on a basket of underlying assets, and we test it numerically for Portfolio Default Options. For any number of underlying assets or names in a portfolio, the sensitivities of the option price with respect to all the pairwise correlations is obtained at a computational cost which is at most 4 times the cost of calculating the option value itself. For typical applications, this results in computational savings of several order of magnitudes with respect to standard methods.
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PDF链接：
https://arxiv.org/pdf/1004.1855