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