Chao Wang

Photo of Chao Wang

BEng SUST; MEng BIT; MSc HUT; PhD Sydney
Casual Lecturer

Rm 4036
H70 - Abercrombie Building
The University of Sydney
NSW 2006 Australia

Telephone +61 2 9036 9101


Dr Chao Wang received his PhD degree in Econometrics from The University of Sydney. He has two master degrees major in Machine Learning & Data Mining from Helsinki University of Technology and Mechatronic Engineering from Beijing Institute of Technology respectively.

Chao Wang’s main research interests are financial econometrics and time series modelling. He has developed a series of parametric and non-parametric volatility models incorporating intra-day and high frequency volatility measures (realized variance, realized range, etc) applied on the financial market risk forecasting, employing Bayesian adaptive Markov chain Monte Carlo estimation. His work has also considered different techniques, including scaling and sub-sampling, to deal with the micro-structure noisy of the high frequency volatility measures. Further, Chao’s research interests also include big data, machine learning and data mining, text mining, etc.

Selected publications


Journal Articles

Gerlach R, and Wang C (2019) Semi-parametric Dynamic Asymmetric Laplace Models for Tail Risk Forecasting, Incorporating Realized Measures International Journal of Forecasting, In Press.

Wang C, Chen Q, and Gerlach R (2019) Bayesian Realized-GARCH Models for Financial Tail Risk Forecasting Incorporating the Two-sided Weibull Distribution Quantitative Finance, 19 (6), 1017-1042. [More Information]


Journal Article

Gerlach R, Walpole D, and Wang C (2017) Semi-parametric Bayesian Tail Risk Forecasting Incorporating Realized Measures of Volatility Quantitative Finance, 17 (2), 199-215. [More Information]


Journal Article

Gerlach R, and Wang C (2016) Forecasting risk via realized GARCH, incorporating the realized range Quantitative Finance, 16 (4), 501-511. [More Information]

Recent Units Taught

  • BUSS1020 Quantitative Business Analysis

  • QBUS2820 Predictive Analytics

  • QBUS5001 Quantitative Methods for Business

  • QBUS6840 Predictive Analytics

  • QBUS6850 Machine Learning for Business