Stochastic kriging for conditional value-at-risk and its sensitivities
Stochastic kriging for conditional value-at-risk and its sensitivities
复制标题
DOI：
10.1109/wsc.2012.6465096
复制
发表时间：
2012-12
期刊：
影响因子：
--
通讯作者：
X. Chen;B. Nelson;K. Kim
中科院分区：
文献类型：
--
作者：
X. Chen;B. Nelson;K. Kim
衡量资产组合中的风险一直是金融行业的核心话题之一。自引入连贯的风险度量以来，对风险度量的研究蓬勃发展，学者和从业人员采用了风险价值以外的度量，如预期不足。然而，金融产品的复杂性使得执行计算这些风险度量所需的数值任务非常困难和耗时。本文介绍了一种基于随机克里格元模型的风险及其敏感性的有效估计方法。特别是，该方法使用投资组合中资产的梯度估计量，并以最小的均方误差给出风险敏感性的最佳线性无偏预测器。将该方法与另外两种基于随机克里格的方法进行了数值比较，结果表明该方法在金融风险估计中具有良好的应用前景。
Measuring risks in asset portfolios has been one of the central topics in the financial industry. Since the introduction of coherent risk measures, studies on risk measurement have flourished and measures beyond value-at-risk, such as expected shortfall, have been adopted by academics and practitioners. However, the complexity of financial products makes it very difficult and time consuming to perform the numerical tasks necessary to compute these risk measures. In this paper, we introduce a stochastic kriging metamodel-based method for efficient estimation of risks and their sensitivities. In particular, this method uses gradient estimators of assets in a portfolio and gives the best linear unbiased predictor of the risk sensitivities with minimum mean squared error. Numerical comparisons of the proposed method with two other stochastic kriging based approaches demonstrate the promising role that the proposed method can play in the estimation of financial risk.