论文标题
时间变化的高斯 - 卡奇混合模型用于财务风险管理
Time-Varying Gaussian-Cauchy Mixture Models for Financial Risk Management
论文作者
论文摘要
财务风险有各种指标，例如风险的价值（VAR），预期的短缺，预期/意外损失等。在估计这些指标时，对于资产返还的高斯分配非常普遍，这可能低估了市场的真正风险，尤其是在金融危机期间。在本文中，我们提出了一系列随时间变化的混合模型，以进行风险分析和管理。这些混合模型包含两个组件：一个具有高斯分布的组件，另一个具有脂肪尾分布的组件。我们允许分布参数和组件权重随时间变化，以提高模型的灵活性。蒙特卡洛期望最大化算法用于估计参数。为了验证模型的良好性能，我们进行了一些模拟研究，并将模型实施到真正的股票市场。基于这些研究，我们的模型在不同的经济条件下是适当的，并且组成重量可以捕获市场波动的正确模式。
There are various metrics for financial risk, such as value at risk (VaR), expected shortfall, expected/unexpected loss, etc. When estimating these metrics, it was very common to assume Gaussian distribution for the asset returns, which may underestimate the real risk of the market, especially during the financial crisis. In this paper, we propose a series of time-varying mixture models for risk analysis and management. These mixture models contain two components: one component with Gaussian distribution, and the other one with a fat-tailed Cauchy distribution. We allow the distribution parameters and component weights to change over time to increase the flexibility of the models. Monte Carlo Expectation-Maximization algorithm is utilized to estimate the parameters. To verify the good performance of our models, we conduct some simulation studies, and implement our models to the real stock market. Based on these studies, our models are appropriate under different economic conditions, and the component weights can capture the correct pattern of the market volatility.
