Panel quantile regression for extreme risk
Panel quantile regression for extreme risk
复制标题
极端风险的面板分位数回归
DOI：
10.1016/j.jeconom.2024.105674
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发表时间：
2024-03
影响因子：
6.3
通讯作者：
Yanxi Hou;Xuan Leng;Liang Peng;Yinggang Zhou
中科院分区：
文献类型：
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作者：
Yanxi Hou;Xuan Leng;Liang Peng;Yinggang Zhou
面板分位数回归模型在金融、保险和风险管理应用中发挥着重要作用。然而，由于远尾数据稀疏，对极端条件分位数直接应用面板回归可能会遭受显着的估计不确定性。我们引入了一种两阶段方法来预测横截面的极端条件分位数，该方法在选定的中间水平上使用面板分位数回归，然后利用极值理论将中间水平外推到极端水平。中间水平的面板分位数回归和极值理论的组合依赖于异方差极值的一组二阶条件。提出了一种称为平均绝对相对误差的度量来评估中间和极端条件分位数的预测性能。在面板分位数回归中允许个体固定效应对两阶段方法和预测度量的渐近分析提出了挑战。与直接使用面板分位数回归相比，我们证明了极端条件分位数预测的有限样本性能。最后，将两阶段方法应用于宏观经济和房价数据，发现了房地产泡沫和共同经济因素的有力证据。
Panel quantile regression models play an essential role in finance, insurance, and risk management applications. However, a direct application of panel regression for extreme conditional quantiles may suffer from a significant estimation uncertainty due to data sparsity on the far tail. We introduce a two-stage method to predict extreme conditional quantiles over cross-sections, which uses panel quantile regression at a selected intermediate level and then extrapolates the intermediate level to an extreme level with extreme value theory. This combination of panel quantile regression at an intermediate level and extreme value theory relies on a set of second-order conditions for heteroscedastic extremes. A metric calledAverage AbsoluteRelative Erroris proposed to evaluate the prediction performance of both intermediate and extreme conditional quantiles. Allowing individual fixed effects in panel quantile regressions challenges the asymptotic analysis of the two-stage method and prediction metric. We demonstrate the finite sample performance of the extreme conditional quantile prediction compared to the direct use of panel quantile regression. Finally, an application of the two-stage method to the macroeconomic and housing price data finds strong evidence of housing bubbles and common economic factors.