题目：Average quantile regression: a new non-mean regression model and coherent risk measure

汇报人： 姜荣

讲座时间：2025年6月23日(周一)  15：30-16：30

地点： 综合楼615会议室

报告人简介：姜荣，博士，上海对外经贸大学统计与数据科学学院教授，全国工业统计学教学研究会金融科技与大数据技术分会理事。在《Journal of Business & Economic Statistics》、《Journal of Financial Econometrics》、《Test》、《Neurocomputing》和《Journal of Multivariate Analysis》等国际期刊上发表SCI和SSCI论文30余篇。主持国家自然科学基金2项以及教育部人文社科基金和上海市扬帆计划等项目。

报告摘要：In this talk, I will introduce the innovative concept of Average Quantile Regression (AQR), which is smooth at the quantile-like level, comonotonically additive, and explicitly accounts for the severity of tail losses relative to quantile regression. AQR serves as a versatile regression model capable of describing distributional information across all positions, akin to quantile regression, yet offering enhanced interpretability compared to expectiles. Numerous traditional regression models and coherent risk measures can be regarded as special cases of AQR. As a flexible non-parametric regression model, AQR demonstrates outstanding performance in analyzing high-dimensional and large datasets, particularly those generated by distributed systems, and provides a convenient framework for their statistical analysis. The corresponding estimators are rigorously derived, and their asymptotic properties are thoroughly developed. In a risk management context, the case study confirms AQR’s effectiveness in risk assessment and portfolio optimization.