【专家简介】：刘志, 澳门大学教授。主要研究方向为统计学及其交叉方向。研究兴趣包括随机过程统计，金融统计，机器学习在生物信息和医学数据方面的应用。论文发表于统计学及其交叉学科国际期刊，如统计学期刊AoS, JASA, JBES, 计量经济学期刊JoE, QE, ET, EJ, 金融学期刊JBF, FS, 生物信息学期刊Bioinformatics, 以及机器学习会议AAAI, 等。主持完成国家自然科学基金和澳门政府基金10余项。分别于2022年和2025年获澳门大学科技学院卓越教学奖和杰出研究奖。
【报告摘要】：The association between log-price increments of exchange-traded equities, as measured by their spot correlation estimated from high-frequency data, exhibits a pronounced upward-sloping and almost piecewise linear relationship at the intraday horizon. There is notably lower---on average less positive---correlation in the morning than in the afternoon. We develop a nonparametric testing procedure to detect such variation in a correlation process. The test statistic has a known distribution under the null hypothesis, whereas it diverges under the alternative. We run a Monte Carlo simulation to discover the finite sample properties of the test statistic, which are close to the large sample predictions, even for small sample sizes and realistic levels of diurnal variation. In an application, we implement the test on a high-frequency dataset covering the stock market over an extended period. The test leads to rejection of the null most of the time. This suggests diurnal variation in the correlation process is a nontrivial effect in practice. We show how conditioning information about macroeconomic news and corporate earnings announcements affect the intraday correlation curve.
【报告时间】：2026年04月29日15:00-16:00
【报告地点】：位育楼417
