英文标题：
《Non-Parametric Robust Model Risk Measurement with Path-Dependent Loss
Functions》
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作者：
Yu Feng
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最新提交年份：
2019
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英文摘要：
Understanding and measuring model risk is important to financial practitioners. However, there lacks a non-parametric approach to model risk quantification in a dynamic setting and with path-dependent losses. We propose a complete theory generalizing the relative-entropic approach by Glasserman and Xu to the dynamic case under any $f$-divergence. It provides an unified treatment for measuring both the worst-case risk and the $f$-divergence budget that originate from the model uncertainty of an underlying state process.
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中文摘要：
理解和衡量模型风险对金融从业者来说很重要。然而，缺乏一种非参数方法来模拟动态环境中的风险量化以及路径相关损失。我们提出了一个完整的理论，将Glasserman和Xu的相对熵方法推广到任何$f$-散度下的动态情况。它提供了一种统一的处理方法，用于衡量最坏情况下的风险和源于基础状态过程的模型不确定性的f美元分歧预算。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Mathematical Finance 数学金融学
分类描述：Mathematical and analytical methods of finance, including stochastic, probabilistic and functional analysis, algebraic, geometric and other methods
金融的数学和分析方法，包括随机、概率和泛函分析、代数、几何和其他方法
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一级分类：Quantitative Finance 数量金融学
二级分类：Portfolio Management 项目组合管理
分类描述：Security selection and optimization, capital allocation, investment strategies and performance measurement
证券选择与优化、资本配置、投资策略与绩效评价
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