摘要翻译：
重要性抽样已成为计算尾部风险测度的重要工具。由于这些量通常主要由稀有事件确定，标准蒙特卡罗方法可能效率低下，而重要性抽样提供了一种加快计算速度的方法。本文考虑了在重要抽样中产生的加权经验过程（加权经验度量的过程模拟）的适度偏差。建立了适度偏差原理，作为已有结果的推广。利用Gao和Zhao（Ann.Statist.，2011)建立的大偏差delta方法和经典的大偏差技术，将加权经验过程的中等偏差原理推广到与风险度量相对应的加权经验过程的泛函。主要结果是一个分布的分位数函数的重要性抽样估计量的中等偏差原理和期望缺口。
---
英文标题：
《Moderate deviations for importance sampling estimators of risk measures》
---
作者：
Pierre Nyquist
---
最新提交年份：
2013
---
分类信息：

一级分类：Mathematics 数学
二级分类：Probability 概率
分类描述：Theory and applications of probability and stochastic processes: e.g. central limit theorems, large deviations, stochastic differential equations, models from statistical mechanics, queuing theory
概率论与随机过程的理论与应用：例如中心极限定理，大偏差，随机微分方程，统计力学模型，排队论
--
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
--

---
英文摘要：
Importance sampling has become an important tool for the computation of tail-based risk measures. Since such quantities are often determined mainly by rare events standard Monte Carlo can be inefficient and importance sampling provides a way to speed up computations. This paper considers moderate deviations for the weighted empirical process, the process analogue of the weighted empirical measure, arising in importance sampling. The moderate deviation principle is established as an extension of existing results. Using a delta method for large deviations established by Gao and Zhao (Ann. Statist., 2011) together with classical large deviation techniques, the moderate deviation principle for the weighted empirical process is extended to functionals of the weighted empirical process which correspond to risk measures. The main results are moderate deviation principles for importance sampling estimators of the quantile function of a distribution and Expected Shortfall.
---
PDF下载：
-->