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
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
Pooling econometric models for prediction and decision making. The project develops methods for combining econometric models with the goal of improving prediction. It applies these methods to macroeconomic models used to improve monetary policy and to asset return models used to improve financial risk management.
Prior sensitivity analysis for Bayesian Markov chain Monte Carlo output. This project aims to develop the first set of techniques to implement an automated output sensitivity analysis for Markov Chain Monte Carlo (MCMC) estimation methods. Computationally intense Bayesian MCMC provide a powerful alternative to classical methods for the estimation of economic models. An obstacle to their wider application is that researchers need to specify prior beliefs about model parameters that will affect t ....Prior sensitivity analysis for Bayesian Markov chain Monte Carlo output. This project aims to develop the first set of techniques to implement an automated output sensitivity analysis for Markov Chain Monte Carlo (MCMC) estimation methods. Computationally intense Bayesian MCMC provide a powerful alternative to classical methods for the estimation of economic models. An obstacle to their wider application is that researchers need to specify prior beliefs about model parameters that will affect the results. The expected outcomes will enable researchers to undertake a routine assessment of the sensitivity of the results to prior inputs.Read moreRead less
Estimation of the continuous piecewise linear model and macroeconomic applications. Relationships between economic variables are often characterised by non-linearities. This project develops a method to analyse a type of non-linearity that is frequently encountered in economics and uses this method to study four specific applications concerning the dynamics of inflation, growth, and the exchange rate.
Discovery Early Career Researcher Award - Grant ID: DE170100787
Funder
Australian Research Council
Funding Amount
$331,000.00
Summary
Misspecification in models of economic behaviour. This project aims to develop a robust method for estimation and inference with misspecified economic models. Economic models are designed to test hypotheses about economic behaviour and to estimate key parameters, but their validity and accuracy critically depend on the assumption that the model is correctly specified, which is often doubtful. This project will reparametrize the model to allow for misspecification. The project aims to help modell ....Misspecification in models of economic behaviour. This project aims to develop a robust method for estimation and inference with misspecified economic models. Economic models are designed to test hypotheses about economic behaviour and to estimate key parameters, but their validity and accuracy critically depend on the assumption that the model is correctly specified, which is often doubtful. This project will reparametrize the model to allow for misspecification. The project aims to help modellers produce results that better inform decision-makers and help them make more reliable decisions.Read moreRead less
Robust methods for heteroscedastic regression models for time series. What is the variability of the exchange rate of the Euro to the Australian dollar? Can the use of the electrocardiogram of a patient be improved as a diagnostic tool for heart disease? A well-known limitation of the existing statistical methods for answering these types of questions is that a small proportion of extreme observations have the potential to lead to results that are more in agreement with the outliers than with bu ....Robust methods for heteroscedastic regression models for time series. What is the variability of the exchange rate of the Euro to the Australian dollar? Can the use of the electrocardiogram of a patient be improved as a diagnostic tool for heart disease? A well-known limitation of the existing statistical methods for answering these types of questions is that a small proportion of extreme observations have the potential to lead to results that are more in agreement with the outliers than with bulk of the data. As a consequence, the statistical analyses may lead to wrong conclusions. This project aims to develop new methodologies to solve this problem for a large class of studies. Applications to stock market risk, exchange rate, and diagnosis of heart diseases will illustrate the new methods.Read moreRead less