This paper employs a dynamic peak over threshold (PoT) model to measure and forecast both the lower and upper tail Value at Risks (VaRs) of Bitcoin returns, which offers a new perspective to investigate the tail risk dynamics for Bitcoin. We evaluate the VaR forecasting accuracy of this model compared with that of the GARCH-EVT models based on Student-t, skewed Student-t and Generalized error distribution. The empirical results illustrate that the dynamic PoT model exhibits superior out-of-sample VaR predictive ability, specifically for the lower tail VaR. Thus, this model can be a useful and reliable alternative for forecasting tail risk.

本文采用动态峰值超过阈值（PoT）模型来衡量和预测比特币回报的下尾风险价值（VaR），这为研究比特币的尾部风险动态提供了新的视角。我们与基于 Student-t、偏斜 Student-t 和广义误差分布的 GARCH-EVT 模型相比，评估了该模型的 VaR 预测准确性。实证结果表明，动态 PoT 模型表现出卓越的样本外 VaR 预测能力，特别是对于下尾 VaR。因此，该模型可以成为预测尾部风险的有用且可靠的替代方案。