随机极限正态分布与审慎风险监测

Measuring and Managing Risk with Random Limit Normal Distribution

本文利用我国随机分析与计算领域的国际领先成果,结合有关风险与不确定性的哲学一经济学经典理论,创建了新的概率统计分布模型——随机极限正态分布。进而,在此基础上提出了审慎性风险监管指标R-VaR和R-ES。论文旨在为解决长久困扰金融监管界与实业界的厚尾风险建模测度问题提供开拓性的理论与实证支持。

The paper introduces a new distribution to improve tail risk modelling. We first demonstrate that the fundamental model of risk metrics, like VaR and ES, which leads to their inability to measure risk in a realistic, dynamic economic environment. Then, random limit normal distribution model is proven to be more effective for measuring and managing risk in the real business world. By employing the new distribution, we then propose more prudential risk metrics —— R-VaR and R-ES.