Chao Wang

Dr Chao Wang is an Associate Professor in the BUS department at The University of Sydney. He holds a PhD in Econometrics from The University of Sydney and has previously earned master's degrees in Machine Learning & Data Mining from Helsinki University of Technology and Mechatronic Engineering from Beijing Institute of Technology. His research interests include financial econometrics, time series forecasting, machine learning, deep learning, and Bayesian methods. His work focuses on developing parametric and semi-parametric models for financial risk forecasting, incorporating intraday and high-frequency realized volatility measures and employing Bayesian adaptive Markov Chain Monte Carlo inference. He has also proposed novel approaches that integrate machine learning and deep learning techniques into financial time series forecasting, bridging traditional econometric modeling with modern data-driven methods to enhance predictive accuracy while maintaining interpretability and statistical rigor.

Chao Wang is an Associate Professor at The University of Sydney, specializing in Econometrics, Financial Time Series Forecasting, Bayesian methods, and Machine Learning. His research focuses on developing advanced statistical models for financial risk forecasting, particularly in the areas of tail risk, volatility, and expected shortfall. Wang's work integrates realized measures, such as realized range and volatility, with Bayesian and semi-parametric approaches to improve the accuracy of financial forecasts. His publications explore topics like Bayesian realized-GARCH models, semi-parametric dynamic asymmetric Laplace models, and deep learning techniques for electricity price forecasting. His research contributes to both theoretical advancements and practical applications in financial risk management.