Portfolio Value-at-Risk Approximation for Geometric Brownian Motion

Pub Date : 2024-04-25 DOI:10.3103/s1068362324700067

H. Kechejian, V. K. Ohanyan, V. G. Bardakhchyan

引用次数: 0

Abstract

Value-at-risk (VaR) serves as a measure for assessing the risk associated with individual securities and portfolios. When calculating VaR for portfolios, the dimension of the covariance matrix increases as more securities are included. In this study, we present a solution to address the issue of dimensionality by directly computing the VaR of a portfolio using a single security, therefore requiring only one variance and one mean. Our results demonstrate that, under the assumption of Gaussian distribution, the deviation between the computed VaR and actual values is relatively small.

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几何布朗运动的投资组合风险价值近似值

摘要风险值（VaR）是评估单个证券和投资组合相关风险的一种方法。在计算投资组合的风险值时，协方差矩阵的维度会随着证券数量的增加而增加。在本研究中，我们提出了一个解决维度问题的方案，即使用单个证券直接计算投资组合的风险价值，因此只需要一个方差和一个均值。我们的结果表明，在高斯分布假设下，计算出的风险值与实际值之间的偏差相对较小。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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