题目：Learning Quantile Factors for Large-dimensional Time Series with Statistical Guarantee

主讲人：南京审计大学 孔新兵教授

主持人：统计学院 常晋源教授

时间：2021年5月25日（周二）下午13:30-14:30

地点：腾讯会议，564 507 506

报告摘要：

Quantile is an important measure in risk control in finance and quality assessment in service industry. In this paper, we model the temporal and cross-sectional interactive effect of the quantiles of a large-dimensional time series by a latent quantile factor model. The factor loadings and scores are learnt with statistical guarantee via an iterative check-loss-minimization procedure. Without any moment constraint on the idiosyncratic errors, we correctly identify the common and idiosyncratic components for each variable. We obtained the statistical convergence rates of the minimization estimators. Bahardur representations for the estimated factor loadings and scores are provided under some mild conditions. Moreover, a robust method is proposed to select the number of factors consistently. Simulation experiments checked the validity of the theory. Our analysis on a financial data set shows the superiority of the proposed method over other state-of-the-art methods.

主讲人简介：

孔新兵，南京审计大学教授、国际统计协会(ISI)当选会员；获2012年度香港数学会“最佳博士论文奖”；主要研究兴趣为高频数据分析、髙维因子分析和经济金融计量分析；担任两个学术期刊编委；中国现场统计研究会多个分会常务理事；在JASA、 AoS、 Biometrika，JoE等SCI期刊发表论文30余篇，独立发表AoS、Biometrika成果三篇；主持基金项目四项。