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Research_

Time Series and Forecasting Research Group

Using data to make sense of business trends and forecast the future
A collaborative approach to time series and forecasting research to inform business and policy.

About us

We facilitate within-group and external research collaborations (promoting collaborative publications, ARC and other grant funding applications and PhD supervisions), in areas related to time series and forecasting.

What is Time Series?

Time Series data captures information on variables whose values change, and are collected, over time.  The time interval for collection can be anywhere from a micro second to a century, and can be synchronous or asynchronous/irregular.

Analyses of such data helps make sense of trends and gradual or abrupt changes over time – primarily by identifying patterns, associations and causation between variables. Knowledge about these matters is used in a wide variety of business and policy settings – especially in forecasting future developments and predicting behavior in real time.

Our work

In addition to ongoing research collaborations, we aim to build professional links and awareness about time series and forecasting by:

  • working towards the production of a real time, time series forecasting website, to be administered and operated by the group
  • running professional development workshops in specific areas related to time series and forecasting
  • organising and running a quality annual one day research symposium on time series and forecasting.

Our people

  • Associate Professor Shumi Akhtar, University of Sydney Business School
  • Professor John Buchanan, University of Sydney  Business School
  • Associate Professor Jennifer Chan, School of Mathematics and Statistics, University of Sydney
  • Dr Qian Chen, Assistant Professor, HSBC Business School, Peking University, China
  • Dr Wilson Chen, Postdoctoral Research Fellow, University of Sydney Business School
  • Dr Boris Choy, University of Sydney Business School 
  • Dr Christian Contino, Quantitative Analyst, Head of Research (Advisory), RF Capital 
  • Professor Sally Cripps, Centre for Translational Data Science, University of Sydney 
  • Dr Peter Exterkate, School of Economics, University of Sydney 
  • Professor Junbin Gao, University of Sydney Business School
  • Professor Robert Kohn, ASB, UNSW 
  • Dr Simon Kwok, School of Economics, University of Sydney
  • Dr Henry Leung, University of Sydney Business School 
  • Professor Michael McAleer, University Research Professor, Department of Finance, College of Management, Asia University, Taiwan
  • Dr Roman Marchant, Centre for Translational Data Science, University of Sydney 
  • Dr Laurent Pauwels, University of Sydney Business School 
  • Associate Professor Shelton Peiris, School of Mathematics and Statistics, University of Sydney
  • Professor Gareth Peters, Heriot-Watt University, UK 
  • Professor Artem Prokhorov, University of Sydney Business School
  • Dr Marcel Scharth, University of Sydney Business School 
  • Dr Richard Szalco, Centre for Translational Data Science, University of Sydney
  • Dr Minh-Ngoc Tran, University of Sydney Business School
  • Dr David Ubilava, School of Economics, University of Sydney 
  • Associate Professor Andrey Vasnev, University of Sydney Business School 
  • Dr Chao Wang, University of Sydney Business School
  • Dr Nuttanan Wichitaksorn, Department of Mathematical Sciences, Auckland University of Technology

Recent publications

Chan F and Pauwels L 2018 'Some theoretical results on forecast combinations', International Journal of Forecasting, vol.34:1, pp. 64-74

Chan JSK, Choy STB, Makov UE and Landsman Z 2018 Forthcoming 'Modeling insurance losses using contaminated generalised Beta type II distribution', ASTIN Bulletin

Sutton M, Vasnev A and Gerlach R 2018 Forthcoming 'Mixed Interval Realized Variance: A Robust Estimator of Stock Price Volatility', Econometrics and Statistics

Yatigammana R, Peiris S, Gerlach R and Allen D 2018 'Modelling and Forecasting stock price movements with serially dependent determinants', Risks, vol.6:2

Yeap C, Choy STB and Kwok SS 2018 'The Skew-t Option Pricing Model' in Econometrics for Financial Applications (Studies in Computational Intelligence - Volume 760), ed. Anh LH, Dong LS, Kreinovich V & Thach NN, Springer International Publishing, Cham, Switzerland, pp. 309-326

Yeap C, Kwok S and Choy STB 2018 Forthcoming 'A Flexible Generalized Hyperbolic Option Pricing Model and Its Special Cases', Journal of Financial Econometrics

Zhu X, Wang T, Choy STB and Autchariyapanitkul K 2018 'Measures of Mutually Complete Dependence for Discrete Random Vectors' in Predictive Econometrics and Big Data (Studies in Computational Intelligence: volume 753), ed. Vladik Kreinovich, Songsak Sriboonchitta & Nopasit Chakpitak, Springer International Publishing, Cham, Switzerland, pp. 302-317

Contino C and Gerlach R 2017 'Bayesian tail-risk forecasting using realized GARCH', Applied Stochastic Models in Business and Industry, vol.33:2, pp. 213-36 

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 

Pauwels L and Vasnev A 2017 'Forecast combination for discrete choice models: predicting FOMC monetary policy decisions', Empirical Economics, vol.52:1, pp. 229-54