A Generalized Extreme Value Approach to Financial Risk Measurement
提出基于极值理论的无条件和条件方法计算风险价值（VaR），利用极端收益分布而非全部收益分布，更准确估计金融机构的最大可能损失，优于正态和偏t分布。
This paper develops an unconditional and conditional extreme value approach to calculating value at risk (VaR), and shows that the maximum likely loss of financial institutions can be more accurately estimated using the statistical theory of extremes. The new approach is based on the distribution of extreme returns instead of the distribution of all returns and provides good predictions of catastrophic market risks. Both the in‐sample and out‐of‐sample performance results indicate that the Box–Cox generalized extreme value distribution introduced in the paper performs surprisingly well in capturing both the rate of occurrence and the extent of extreme events in financial markets. The new approach yields more precise VaR estimates than the normal and skewed t distributions.