Abstract Numerical standard error (NSE) is an estimate of the standard deviation of a simulation result if the simulation experiment were to be repeated many times. We review standard methods for computing NSE and perform a Monte Carlo experiments to compare their performance in the case of high/extreme autocorrelation. In particular, we propose an application to risk management where we assess the precision of the value-at-risk measure when the underlying risk model is estimated by simulation-based methods. Overall, heteroscedasticity and autocorrelation estimators with prewhitening perform best in the presence of large/extreme autocorrelation.

中文翻译：

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数值标准误差的计算方法：风险价值估计的回顾和应用

摘要数值标准误差（NSE）是在模拟实验要重复多次的情况下对模拟结果的标准偏差的估计。我们回顾了计算NSE的标准方法，并进行了蒙特卡洛实验，以比较其在高/极高自相关情况下的性能。特别是，我们提出了一种风险管理应用程序，其中当通过基于仿真的方法估算基础风险模型时，我们可以评估风险价值衡量的准确性。总体而言，在存在大/极端自相关的情况下，具有预增白功能的异方差和自相关估计器表现最佳。