英文标题：
《Gaussian Process Regression for Derivative Portfolio Modeling and
  Application to CVA Computations》
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作者：
St\\\'ephane Cr\\\'epey and Matthew Dixon
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最新提交年份：
2019
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英文摘要：
  Modeling counterparty risk is computationally challenging because it requires the simultaneous evaluation of all the trades with each counterparty under both market and credit risk. We present a multi-Gaussian process regression approach, which is well suited for OTC derivative portfolio valuation involved in CVA computation. Our approach avoids nested simulation or simulation and regression of cash flows by learning a Gaussian metamodel for the mark-to-market cube of a derivative portfolio. We model the joint posterior of the derivatives as a Gaussian process over function space, with the spatial covariance structure imposed on the risk factors. Monte-Carlo simulation is then used to simulate the dynamics of the risk factors. The uncertainty in portfolio valuation arising from the Gaussian process approximation is quantified numerically. Numerical experiments demonstrate the accuracy and convergence properties of our approach for CVA computations, including a counterparty portfolio of interest rate swaps.
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中文摘要：
交易对手风险建模在计算上具有挑战性，因为它需要同时评估市场和信用风险下与每个交易对手的所有交易。我们提出了一种多元高斯过程回归方法，该方法非常适合于涉及CVA计算的OTC衍生品投资组合估值。我们的方法通过学习衍生品投资组合按市值计价立方体的高斯元模型，避免了嵌套模拟或现金流的模拟和回归。我们将导数的联合后验值建模为函数空间上的高斯过程，并将空间协方差结构施加在风险因素上。然后使用蒙特卡罗模拟来模拟风险因素的动态。对高斯过程近似引起的投资组合估值不确定性进行了数值量化。数值实验证明了我们的CVA计算方法的准确性和收敛性，包括利率掉期交易对手组合。
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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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