Chao Wang

Dr. Chao Wang is a Senior Lecturer at the University of Sydney's Sydney Business School. He holds a PhD in Econometrics from the same university, along with master's degrees in Machine Learning & Data Mining (Helsinki University of Technology) and Mechatronic Engineering (Beijing Institute of Technology). His research focuses on financial econometrics, time series modeling, and volatility analysis, particularly using Bayesian methods and high-frequency data. He teaches courses like Quantitative Business Analysis (BUSS1020) and Machine Learning for Business (QBUS6840).

Dr. Wang's research interests include parametric/non-parametric volatility models, Bayesian MCMC estimation, and applications of machine learning in finance. His work addresses microstructure noise in high-frequency data and integrates realized measures like variance and range. He has supervised students on topics such as spatiotemporal volatility forecasting and financial technology-based risk management.

• Grants: Machine learning and high-frequency data-based risk forecasting (2022), financial tail risk forecasting with deep learning (2021), and parametric tail risk forecasting (2020).
• Key collaborations: With Prof. Robert Gerlach and Minh-Ngoc Tran on Bayesian frameworks and realized measures.
• Publications: Over 15 peer-reviewed articles in top journals like Quantitative Finance and Journal of Financial Econometrics, focusing on risk forecasting methodologies.

His work bridges econometric theory and practical financial applications, emphasizing robust risk prediction under volatile market conditions.