英文标题：
《Sequential Design and Spatial Modeling for Portfolio Tail Risk
  Measurement》
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作者：
Michael Ludkovski and James Risk
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最新提交年份：
2018
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英文摘要：
  We consider calculation of capital requirements when the underlying economic scenarios are determined by simulatable risk factors. In the respective nested simulation framework, the goal is to estimate portfolio tail risk, quantified via VaR or TVaR of a given collection of future economic scenarios representing factor levels at the risk horizon. Traditionally, evaluating portfolio losses of an outer scenario is done by computing a conditional expectation via inner-level Monte Carlo and is computationally expensive. We introduce several inter-related machine learning techniques to speed up this computation, in particular by properly accounting for the simulation noise. Our main workhorse is an advanced Gaussian Process (GP) regression approach which uses nonparametric spatial modeling to efficiently learn the relationship between the stochastic factors defining scenarios and corresponding portfolio value. Leveraging this emulator, we develop sequential algorithms that adaptively allocate inner simulation budgets to target the quantile region. The GP framework also yields better uncertainty quantification for the resulting VaR/TVaR estimators that reduces bias and variance compared to existing methods. We illustrate the proposed strategies with two case-studies in two and six dimensions.
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中文摘要：
当基本经济情景由可模拟的风险因素确定时，我们考虑资本要求的计算。在各自的嵌套模拟框架中，目标是估计投资组合尾部风险，通过VaR或TVaR对代表风险范围内因素水平的给定未来经济情景集合进行量化。传统上，评估外部情景的投资组合损失是通过内部蒙特卡罗计算条件期望来完成的，计算成本很高。我们引入了几种相互关联的机器学习技术来加速这种计算，特别是通过适当考虑模拟噪声。我们的主要工作是一种高级高斯过程（GP）回归方法，该方法使用非参数空间建模来有效地了解定义情景的随机因素与相应投资组合价值之间的关系。利用这个模拟器，我们开发了顺序算法，可以自适应地分配内部模拟预算以定位分位数区域。与现有方法相比，GP框架还为产生的VaR/TVaR估计量提供了更好的不确定性量化，从而减少了偏差和方差。我们在两个维度和六个维度上用两个案例研究来说明所提出的策略。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Risk Management        风险管理
分类描述：Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
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