Diversification failures and improved measures of uncertainty. The project aims to develop new statistical tools, applicable when the conventional paradigm that diversification reduces risk fails and when textbook approaches to risk quantification severely under-report risk. The new tools enhance our capacity to build and manage natural, social and human-made systems in uncertain environments. Our effective response to many threats including financial crises and natural events, depends on this c ....Diversification failures and improved measures of uncertainty. The project aims to develop new statistical tools, applicable when the conventional paradigm that diversification reduces risk fails and when textbook approaches to risk quantification severely under-report risk. The new tools enhance our capacity to build and manage natural, social and human-made systems in uncertain environments. Our effective response to many threats including financial crises and natural events, depends on this capacity. Thus, the expected benefits in the form of more reliable and robust risk analytics will accrue when they are most needed.Read moreRead less
The predictive, behavioural and economic forecasting performance of alternative credit risk and bankruptcy models: a global study. This study empirically evaluates a range of "new age" credit risk models using a large global sample of failed firms and bond ratings data. The study will provide a substantive body of empirical evidence to assist regulators, creditors, investors and other users assess the merits, strengths and limitations of alternative risk modelling approaches.
Trending time series models with non- and semi-parametric methods. The outcomes of this project will not only complement but also enhance the existing strengths and reputation of Australian researchers in the field of econometrics. The outcomes are also expected to help improve model building and forecasting from better models in climatology, economics, environmetrics and financial econometrics.
High-frequency Estimation of Term Structure Models at the Zero Lower Bound. This project aims to quantify monetary policy shocks as shifts of the entire term structure of interest rates, when the central bank’s policy rate is constrained at the near-zero level. The proposed method will use a high-dimensional panel of high frequency government bond data. The term structure and resultant policy shocks estimated at intra-day frequencies for major economies including Australia, will be made publicly ....High-frequency Estimation of Term Structure Models at the Zero Lower Bound. This project aims to quantify monetary policy shocks as shifts of the entire term structure of interest rates, when the central bank’s policy rate is constrained at the near-zero level. The proposed method will use a high-dimensional panel of high frequency government bond data. The term structure and resultant policy shocks estimated at intra-day frequencies for major economies including Australia, will be made publicly available. This project expects to deepen our understanding of how monetary policy decisions affect the macroeconomy in a near-zero interest-rate environment. This should provide significant benefits to policymakers for implementing and monitoring monetary policy in achieving desired economic outcomes.Read moreRead less
Deep learning based time series modeling and financial forecasting. This project pursues breakthroughs in time series modelling and develops novel statistical models and inference techniques, with a focus on modelling of financial time series data. The advances will be achieved through interdisciplinary research, combining recent advances in machine learning, Bayesian computation, financial econometrics and the increasing availability of Big Data. The outcomes will provide a new range of proven ....Deep learning based time series modeling and financial forecasting. This project pursues breakthroughs in time series modelling and develops novel statistical models and inference techniques, with a focus on modelling of financial time series data. The advances will be achieved through interdisciplinary research, combining recent advances in machine learning, Bayesian computation, financial econometrics and the increasing availability of Big Data. The outcomes will provide a new range of proven and powerful approaches for analysing time series and understanding time effects. The methodologies developed will lead to a greater accuracy in financial forecasting and risk management, and open up new horizons for the wider scientific community to analyse time series data.Read moreRead less