Find us on Facebook Find us on LinkedIn Follow us on Twitter Subscribe to our YouTube channel Instagram

Chao Wang

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
chao.wang@sydney.edu.au

Bio

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.

Publications

2017

4
Journal Article/s

Gerlach R, Walpole D and Wang C 2017 'Semi-parametric Bayesian Tail Risk Forecasting Incorporating Realized Measures of Volatility', Quantitative Finance, vol.17:2, pp. 199-215 Link

2016

4
Journal Article/s

Gerlach R and Wang C 2016 'Forecasting risk via realized GARCH, incorporating the realized range', Quantitative Finance, vol.16:4, pp. 501-11 Link

Research Expertise

  • Financial Econometrics and Time Series Modelling
  • Financial Risk Forecasting and Management
  • High Frequency Volatility Measures
  • Range-based Time Series Models
  • Bayesian Econometrics
  • Markov Chain Monte Carlo
  • Big Data
  • Machine Learning and Data Mining

Recent Units Taught

  • BUSS1020 Quantitative Business Analysis

    2017: S1,
    2016: S2,

  • QBUS5001 Quantitative Methods for Business

    2017: S1,
    2016: S2,
    2015: S1, S2,