论文标题
灰色天鹅的不确定形状：不确定阈值的极端价值理论
The Uncertain Shape of Grey Swans: Extreme Value Theory with Uncertain Threshold
论文作者
论文摘要
极值理论（EVT）是衡量投资组合的下行风险，尤其是在金融危机期间的最常用方法之一。在本文中，我们提出了一种基于EVT的新方法，称为不确定EVT，以提高其预测准确性，并捕获超过EVT阈值的风险的统计特征。在我们的框架中，通常假定为常数的极端风险阈值是动态随机变量。更确切地说，我们通过状态依赖性的隐藏变量（称为Break-e-e-e-e-e thim风险阈值（BRT））对EVT阈值进行建模和校准，这是风险和歧义的函数。我们将证明，当EVT方法与不可观察的BRT流程相结合时，不确定的EVT的预测VAR可以预见到巨大的财务损失的风险，优于原始EVT方法在样本之外胜过，并且在经过有效性和可预测性的回顾时，对众所周知的VAR模型具有竞争力。
Extreme Value Theory (EVT) is one of the most commonly used approaches in finance for measuring the downside risk of investment portfolios, especially during financial crises. In this paper, we propose a novel approach based on EVT called Uncertain EVT to improve its forecast accuracy and capture the statistical characteristics of risk beyond the EVT threshold. In our framework, the extreme risk threshold, which is commonly assumed a constant, is a dynamic random variable. More precisely, we model and calibrate the EVT threshold by a state-dependent hidden variable, called Break-Even Risk Threshold (BRT), as a function of both risk and ambiguity. We will show that when EVT approach is combined with the unobservable BRT process, the Uncertain EVT's predicted VaR can foresee the risk of large financial losses, outperforms the original EVT approach out-of-sample, and is competitive to well-known VaR models when back-tested for validity and predictability.
