学术报告：Modeling volatility for high-frequency data with rounding error: a nonparametric Bayesian approach

报告时间：2023年11月8日（星期三）14:30-16:30

报告地点：沙河校区，学院1号楼102会议室

报告人：吴奔，中国人民大学统计学院，副教授

报告摘要：Rounding is a pivotal source of market microstructure noise which should be carefully addressed in high-frequency data analysis. In this talk, I will introduce a novel Bayesian rounding error model, for which we incorporate available market information in modeling rounding mechanism and assign a thresholded Gaussian process prior to the instantaneous volatility of the log-price process. We rely on a fully Bayesian approach with an efficient Markov chain Monte Carlo algorithm for model inference and propose a novel Trading Information-based estimator for the integrated volatility. Simulations show the good performance of the proposed method with multiple volatility shapes and rounding mechanisms, including model misspecification. Empirical studies suggest the rounding mechanism in the Shanghai A-share market is more likely to be random and trading directions have a great impact on the rounding probability of the asset prices.

报告人简介：吴奔，中国人民大学统计学院副教授，曾经在Emory大学生物统计与生物信息系、Michigan大学生物统计系从事博士后研究工作。主要研究方向为贝叶斯统计、独立成分分析、神经影像数据分析、金融高频数据分析等。主持国家自然科学基金青年项目，研究成果在JASA、Biometrics、Statistics and Computing、《中国科学（数学）》等国内外知名期刊发表。