用于金融对数收益率左尾和右尾条件分位数预测的2T-POT Hawkes模型：条件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
AI总结：
提出一种适用于单变量时间序列左右尾条件分位数预测的2T-POT Hawkes模型，在六个大盘指数上验证了时间杠杆效应，并在5%及以下覆盖水平的风险价值与预期缺口预测中优于GARCH-EVT模型，为金融风险建模提供了更优方案。
摘要：
条件极值理论（EVT）方法有望提升对经常主导系统性风险的极端尾部事件的预测能力。我们提出了一种改进的双尾超阈值（2T-POT）Hawkes模型，该模型适用于单变量时间序列左尾和右尾的条件分位数预测。该模型被应用于六个大盘指数的日对数收益率。我们还采取了独特的步骤，在多个超阈值（从1.25%到25.00%的镜像分位数）下拟合模型。在所有六个指数中均发现了Hawkes参数在数量上相似的不对称性，这为金融价格时间序列中的时间杠杆效应提供了进一步的实证支持，即损失的影响不仅更大，而且更直接。样本外回测发现，在预测5%及以下覆盖水平的（镜像）风险价值和预期缺口时，我们的2T-POT Hawkes模型比GARCH-EVT模型更可靠、更准确。这表明，非对称Hawkes型到达动态比GARCH型条件波动率更好地近似了极端日对数收益率的真实数据生成过程；因此，我们的2T-POT Hawkes模型为金融风险建模提供了一个性能更好的替代方案。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
