论文标题
波动性敏感的贝叶斯估计投资组合和CVAR的估计
Volatility Sensitive Bayesian Estimation of Portfolio VaR and CVaR
论文作者
论文摘要
在本文中，提出了一种整合波动率信息的新方法，以估算投资组合的风险（VAR）和风险的条件价值（CVAR）。新方法是从贝叶斯统计的角度开发的，它基于波动性聚类的概念。通过基于两个不同的滚动窗口大小来指定共轭物中的超参数，可以快速适应波动性的变化，并自动指定先验中的确定性程度。与现有的贝叶斯方法相比，这是一个优势，这些方法对这种波动的变化不太敏感，并且通常缺乏表达信念程度的标准化方法。我们使用模拟和经验数据来说明我们的新方法。与其他一些众所周知的均质和异性模型相比，新方法为风险估计提供了一个很好的选择，尤其是在动荡的时期，它可以迅速适应不断变化的市场状况。
In this paper, a new way to integrate volatility information for estimating value at risk (VaR) and conditional value at risk (CVaR) of a portfolio is suggested. The new method is developed from the perspective of Bayesian statistics and it is based on the idea of volatility clustering. By specifying the hyperparameters in a conjugate prior based on two different rolling window sizes, it is possible to quickly adapt to changes in volatility and automatically specify the degree of certainty in the prior. This constitutes an advantage in comparison to existing Bayesian methods that are less sensitive to such changes in volatilities and also usually lack standardized ways of expressing the degree of belief. We illustrate our new approach using both simulated and empirical data. Compared to some other well known homoscedastic and heteroscedastic models, the new method provides a good alternative for risk estimation, especially during turbulent periods where it can quickly adapt to changing market conditions.
