报告题目：Optimal Inference for Spot Regressions
报 告 人：李嘉 香港中文大学
报告时间：2023年9月19日(周二)，16:30-18:00
报告地点：厦门大学经济楼N302
腾讯会议ID：626-452-977
内容摘要
Betas from return regressions are commonly used to measure systematic financial market risks. “Good” beta measurements are essential for a range of empirical inquiries in finance and macroeconomics. We introduce a novel econometric framework for the nonparametric estimation of time-varying betas with high-frequency data. The “local Gaussian” property of the generic continuous-time benchmark model enables optimal “finite-sample” inference in a well-defined sense. It also affords more reliable inference in empirically realistic settings compared to conventional large-sample approaches. Two applications pertaining to the tracking performance of leveraged ETFs and an intraday event study illustrate the practical usefulness of the new procedures.
主讲人简介
Professor Li Jia obtained his Ph.D. in economics from Princeton University in 2011. He is currently the Lee Kong Chian Professor of Economics at Singapore Management University, and was a professor of economics at Duke University from 2011 to 2021. Professor Li’s research focuses on semiparametric and nonparametric methods in time series analysis, with a special emphasis on the analysis of high frequency financial data. His work has been published in leading journals across economics, statistics, and probability, including American Economic Review, Econometrica, Review of Economic Studies, Review of Economics and Statistics, Journal of Econometrics, Journal of the American Statistical Association, Annals of Statistics, and Annals of Applied Probability. He is an elected fellow of the Society of Financial Econometrics and the Journal of Econometrics.
