发表机构
Sao Paulo School of Economics, FGV; Argus Media; Sao Paulo School of Business Administation, FGV（圣保罗经济管理学院，FGV; Argus Media; 圣保罗商业管理学院，FGV）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究在条件尺度模型下建立了条件期望分位数两步估计量的一致性与渐近正态性，阐明了标准化残差替代真实创新项的影响，发现其评估加密市场尾部风险比传统分位数度量更稳健。
AI 中文摘要
我们在条件尺度模型框架下，建立了条件期望分位数的两步估计量的一致性与渐近正态性。首先通过拟极大似然法估计条件方差参数，再利用标准化残差的经验分布计算创新项的无条件期望分位数。我们阐明了用标准化残差替代真实创新项对条件与无条件期望分位数估计量渐近方差的影响。最后，实证分析表明，条件期望分位数在评估加密市场尾部风险时，比传统的基于分位数的风险度量（如风险价值与期望缺口）更具稳健性。
英文摘要
We establish the consistency and asymptotic normality of a two-step estimator of conditional expectiles in the context of conditional scale models. We first estimate the conditional variance parameters by quasi-maximum likelihood and then compute the unconditional expectile of the innovations using the empirical distribution of the standardized residuals. We show how replacing true innovations with standardized residuals affects the asymptotic variances of both conditional and unconditional expectile estimators. Finally, our empirical analysis reveals that conditional expectiles assess tail risk in cryptomarkets in a more robust manner than traditional quantile-based risk measures, such as value at risk and expected shortfall.
