Variance Reduction Techniques for Estimating Value-at-Risk
Variance Reduction Techniques for Estimating Value-at-Risk
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DOI：
10.1287/mnsc.46.10.1349.12274
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发表时间：
2000-10
影响因子：
5.4
通讯作者：
P. Glasserman;P. Heidelberger;P. Shahabuddin
中科院分区：
文献类型：
--
作者：
P. Glasserman;P. Heidelberger;P. Shahabuddin
本文描述、分析和评价了一种利用蒙特卡罗模拟估计投资组合损失概率的算法，得到这种损失概率的精确估计对于计算损失分布的分位数--风险价值是至关重要的。该方法采用二次型（“delta--gamma”）对投资组合价值变化的逼近，以指导有效方差缩减技术的选择;特别是重要性抽样和分层抽样。如果近似是精确的，数值结果表明，当重要性抽样和分层抽样相结合时，估计大的投资组合损失的可能性。
This paper describes, analyzes and evaluates an algorithm for estimating portfolio loss probabilities using Monte Carlo simulation.Obtaining accurate estimates of such loss probabilities is essential to calculating value-at-risk, which is a quantile of the loss distribution. The method employs a quadratic ("delta--gamma") approximation to the change in portfolio value to guide the selection of effective variance reduction techniques;specifically importance sampling and stratified sampling.If the approximation is exact, then the importance sampling is shown to be asymptotically optimal.Numerical results indicate that an appropriate combination of importance sampling and stratified sampling can result in large variance reductions when estimating the probability of large portfolio losses.