论文标题
2T锅鹰队模型用于财务日志的左尾有条件分位数预测：有条件的EVT模型的样本外比较
2T-POT Hawkes model for left- and right-tail conditional quantile forecasts of financial log-returns: out-of-sample comparison of conditional EVT models
论文作者
论文摘要
有条件的极值理论（EVT）方法有望增强对通常主导全身风险的极端尾巴事件的预测。我们提出了改进的两尾峰值阈值（2t-pot）鹰队模型，该模型适用于单变量时间序列的左右尾部有条件的分位数预测。这适用于六个大帽子指数的每日记录。我们还采取了将模型拟合到多个超过阈值的独特步骤（从1.25％到25.00％的镜像分位数）。在所有六个指数中都发现了鹰派参数的数量相似的不对称性，从而在财务价格时间序列中增加了进一步的经验支持，其中损失的影响不仅更大，而且更直接。样本外回测发现，当预测（镜像）危险中的值（镜像）在5％的覆盖范围及以下时，我们的2T锅鹰队模型比Garch-evt模型更可靠地准确。这表明，与GARCH型的条件波动率相比，不对称的霍克斯型到达动力学是极端每日记录的真实数据生成过程更好的近似。因此，我们的2T-Pot Hawkes模型为财务风险建模提供了更好的替代方案。
Conditional extreme value theory (EVT) methods promise enhanced forecasting of the extreme tail events that often dominate systemic risk. We present an improved two-tailed peaks-over-threshold (2T-POT) Hawkes model that is adapted for conditional quantile forecasting in both the left and right tails of a univariate time series. This is applied to the daily log-returns of six large cap indices. We also take the unique step of fitting the model at multiple exceedance thresholds (from the 1.25% to 25.00% mirrored quantiles). Quantitatively similar asymmetries in Hawkes parameters are found across all six indices, adding further empirical support to a temporal leverage effect in financial price time series in which the impact of losses is not only larger but also more immediate. Out-of-sample backtests find that our 2T-POT Hawkes model is more reliably accurate than the GARCH-EVT model when forecasting (mirrored) value-at-risk and expected shortfall at the 5% coverage level and below. This suggests that asymmetric Hawkes-type arrival dynamics are a better approximation of the true data generating process for extreme daily log-returns than GARCH-type conditional volatility; our 2T-POT Hawkes model therefore presents a better performing alternative for financial risk modelling.
