Speaker：Prof GAO Junbin, Business School, the University of Sydney
Venue: 本部览秀楼105学术报告厅
Date：2026年10月8日 周四下午4:00-5:00
Abstract:
This talk presents a continuous-time stochastic volatility model for one-day-ahead integrated-variance forecasting. Motivated by Realized GARCH, the model introduces a jointly evolving realized-measure state that feeds directly into latent log-variance dynamics, together with correlated Brownian innovations capturing return-volatility dependence. The model is estimated by simulation-based predictive learning and evaluated on 17 international equity indices using daily returns and 5-minute realized variance from 2004 to 2022. Compared with GARCH, GARCH-It?, Realized GARCH, and HEAVY benchmarks, the proposed model delivers strong out-of-sample forecasting performance under QLIKE, MSE, and MAE losses. Ablation results suggest that realized-measure feedback is the main source of improvement, while return-linked Brownian dependence provides a modest complementary gain.
Bio:
Junbin Gao is Professor of Big Data Analytics at the University of Sydney Business School. Prior to joining the University of Sydney in 2016, he was Professor in Computing from 2010 to 2016 and Associate Professor from 2005 to 2010 at Charles Sturt University (CSU). He was Senior Lecturer from Jan 2005 to July 2005 and Lecturer from Nov 2001 to Jan 2005 in the School of Mathematics, Statistics and Computer Science (now the School of Science and Technology) at University of New England (UNE). Between 1999 and 2001, he worked as a Research Fellow in the Department of Electronics and Computer Science at University of Southampton, England.
Junbin Gao graduated from Huazhong University of Science and Technology (HUST) in 1982 with a Bachelor Degree in Computational Mathematics. He obtained his PhD from Dalian University of Technology in 1991. Between 1991 and 1993 he worked as a postdoctoral research fellow investigating wavelet applications at Wuhan University. He was appointed as an Associate Professor in July 1993 and promoted to Professor in October 1997 in Department of Mathematics of HUST. He was Guest Professor (2003-2006) in the State Key Lab of Information Engineering in Surveying, Mapping and Remote Sensing at Wuhan University, China; Guest Professor (2007-2010) in the School of Computer Science and Technology at Huazhong University of Science and Technology, China; Guest Professor (2008-2011) in the School of Computers at Guangdong University of Technology, China; and Visiting Professor (2012-2015) in Beijing Municipal Key Lab of Multimedia and Intelligent Software Technology at Beijing University of Technology.
Until recently his major research interest has been machine learning and its application in data science, image analysis, pattern recognition, Bayesian learning & inference, and numerical optimization etc. He is the author of 260 academic research papers and two books. His recent research has involved new machine learning algorithms for big data in business. Prof Gao won three research grants in Discovery Project theme from the prestigious Australian Research Council (ARC).
