报告题目：Transfer Learning through Enhanced Sufficient Representation: Enriching Source Domain Knowledge with Target Data

报告人：黄坚（香港理工大学讲座教授）

报告时间：2025年3月7日16:00

报告地点：文波楼智慧教室205

摘要：Modeling multivariate stochastic volatility (MSV) can pose significant challenges, particularly when both variances and covariances are time-varying. In this study, we tackle these complexities by introducing novel MSV models based on the generalized Fisher transformation (GFT) proposed by Achakov and Hansen (2021). Our model exhibits remarkable flexibility, ensuring the positive definiteness of the variance-covariance matrix, and disentangling the driving forces of volatilities and correlations. To conduct Bayesian analysis of the models, we employ a Particle Gibbs Ancestor Sampling (PGAS) method, facilitating efficient Bayesian model comparisons. Furthermore, we extend our MSV model to cover leverage effects and incorporate realized measures. Our simulation studies demonstrate that the proposed method performs well for our GFT-based MSV model. Furthermore, empirical studies based on equity returns show that the MSV models outperform alternative specifications in both in-sample and out-of-sample performances.

报告人简介：Jian Huang is a Chair Professor of Data Science and Analytics in the Departments of Data Science and AI, and Applied Mathematics at The Hong Kong Polytechnic University. He earned his Ph.D. in Statistics from the University of Washington in Seattle. His current research interests include machine learning, deep generative models, representation learning, large model statistics, and AI for science. He has published extensively in the fields of Statistics, Biostatistics, Machine Learning, Bioinformatics, and Econometrics. He was designated a Highly Cited Researcher in the field of Mathematics by Clarivate from 2015 to 2019. He was also included in the list of the top 2% of the world's most cited scientists by Stanford University from 2019 to 2024. He serves on the editorial boards of the Journal of the American Statistical Association and the Journal of the Royal Statistical Society (Series B). Professor Huang is a Fellow of the American Statistical Association and a Fellow of the Institute of Mathematical Statistics.