论文标题
使用混合模拟方法的投资组合风险测量
Portfolio Risk Measurement Using a Mixture Simulation Approach
论文作者
论文摘要
蒙特卡洛（Monte Carlo）用于计算价值风险（VAR）的方法是全球金融风险经理广泛使用的强大工具。但是，它们耗时，有时不准确。在本文中，引入了基于高斯混合模型的快速准确的蒙特卡洛算法，用于计算Var和ES。高斯混合模型能够将输入数据集中在市场条件下，因此不需要相关矩阵才能进行风险计算。从每个集群的权重进行抽样，然后计算出波动率调整的股票收益会导致资产价格的可能方案。我们对美国股票样本的结果表明，基于GMM的VAR模型在计算上是有效且准确的。从管理的角度来看，我们的模型可以有效地模仿市场的动荡行为。结果，我们的VAR措施在危机期间，期间和之后实际反映了市场高度非正态的行为和非线性相关结构。
Monte Carlo Approaches for calculating Value-at-Risk (VaR) are powerful tools widely used by financial risk managers across the globe. However, they are time consuming and sometimes inaccurate. In this paper, a fast and accurate Monte Carlo algorithm for calculating VaR and ES based on Gaussian Mixture Models is introduced. Gaussian Mixture Models are able to cluster input data with respect to market's conditions and therefore no correlation matrices are needed for risk computation. Sampling from each cluster with respect to their weights and then calculating the volatility-adjusted stock returns leads to possible scenarios for prices of assets. Our results on a sample of US stocks show that the Gmm-based VaR model is computationally efficient and accurate. From a managerial perspective, our model can efficiently mimic the turbulent behavior of the market. As a result, our VaR measures before, during and after crisis periods realistically reflect the highly non-normal behavior and non-linear correlation structure of the market.
