一种集成重要性采样与机器学习的方法，用于黑盒模型中扭曲风险度量的高效蒙特卡洛估计
An Integrated Approach to Importance Sampling and Machine Learning for Efficient Monte Carlo Estimation of Distortion Risk Measures in Black Box Models
AI总结：
本文提出一种结合重要性采样与机器学习的高效蒙特卡洛方法，用于黑盒仿真模型中扭曲风险度量的估计，并通过数值实验验证其适用性与效率。
摘要：
扭曲风险度量在量化与不确定结果相关的风险中起着关键作用。在缺乏解析可处理性且计算代价高昂的仿真模型背景下，准确估计这些风险度量是有效风险管理与决策的基础。本文提出一种针对此类模型中扭曲风险度量的高效重要性采样方法，该方法通过机器学习降低计算成本。我们在多种扭曲风险度量与模型的数值实验中，展示了该蒙特卡洛方法的适用性与效率。
英文摘要：
Distortion risk measures play a critical role in quantifying risks associated with uncertain outcomes. Accurately estimating these risk measures in the context of computationally expensive simulation models that lack analytical tractability is fundamental to effective risk management and decision making. In this paper, we propose an efficient important sampling method for distortion risk measures in such models that reduces the computational cost through machine learning. We demonstrate the applicability and efficiency of the Monte Carlo method in numerical experiments on various distortion risk measures and models.
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