报告题目：  Regime-specific Return Predictability in Quantiles

报 告 人： 涂云东教授

时    间：  11月13日（周一）下午3:00-4:00

地    点：  西安交通大学创新港涵英楼经济金融研究院8003报告厅

报告人简介：

涂云东,北京大学光华管理学院和北京大学统计科学中心联席教授。入选“日出东方”北大光华青年人才，北京大学优秀博士学位论文指导教师，国家级青年人才入选者。2004年和2006年先后获武汉大学理学学士学位和经济学硕士学位，2012年获美国加州大学河滨分校经济学博士学位。亚太青年计量经济学者会议发起人和主要组织者。40余篇学术论文发表在多个国际国内知名专业杂志。著作教材《时间序列分析》由人民邮电出版社于2022年9月出版。主持多个国家自然科学基金项目，并担任自然科学基金匿名评审。曾获世界计量经济学会、加州计量经济学会议等学术组织提供的青年学者研究资助。研究领域涵盖时间序列分析、非参数计量方法、大数据分析、金融计量和预测等。

摘要：

This paper proposes a predictive quantile regression with multiple thresholds to capture the underlying regime switching mechanism in the prediction of stock returns. The predictability of each predictor is allowed to switch from one regime to another according to the value of a threshold variable and could vary across quantiles, and the predictors could possess different degrees of persistence. A sequential estimation procedure, joint with an adaptive group Lasso refinement, is proposed to efficiently and consistently estimate the unknown multiple thresholds. To remove the impact of model misspecification from the sequential estimation on subsequent inference for the thresholds, a partitioned estimation of the thresholds is further considered. The resulting threshold estimators do not depend on parameters from other regimes asymptotically and have symmetric limiting distributions. The adaptive Lasso is finally adopted to identify the important predictors in each regime to improve prediction accuracy at each quantile, and is shown to achieve the oracle property. Monte Carlo simulations demonstrate the nice performance of our procedure in finite samples. The empirical analysis for the U.S. stock returns shows that the return predictability changes with the economy policy uncertainty across the quantiles.

西安交通大学经济与金融学院

2023年11月7日