Fair pricing of superannuation guaranteed benefits with downturn risk. Australians have more than $2.7 trillion in superannuation assets, meaning that Australia is the fourth largest holder of pension fund assets worldwide. Hence the impact of market fluctuations on financial well-being of retirees can be detrimental, especially during market downturns associated with economic crises. The finance industry addresses this issue by complementing variable annuities with riders designed to protect th ....Fair pricing of superannuation guaranteed benefits with downturn risk. Australians have more than $2.7 trillion in superannuation assets, meaning that Australia is the fourth largest holder of pension fund assets worldwide. Hence the impact of market fluctuations on financial well-being of retirees can be detrimental, especially during market downturns associated with economic crises. The finance industry addresses this issue by complementing variable annuities with riders designed to protect the income stream of retirees. This project aims to develop a novel approach to fair pricing and optimal withdrawals and surrender policies for superannuation guaranteed benefit products through a comprehensive analysis of complex optimisation problems in stochastic models of financial markets with downturn risk.Read moreRead less
Random network models with applications in biology. Complex biological systems consist of a large number of interacting agents or components, and so can be studied using mathematical random network models. We aim to gain deeper insights into the laws emerging as the random networks evolve in time. This can help us to deal with dangerous disease epidemics and better understand the human brain.
Development of general methodology for estimating complex time series models. This project will develop novel methods and models for analysing socio-economic and financial data measured over time and will illustrate them with applications. The methods will allow for more efficient and more accurate processing of information and better forecasting which will facilitate better management and more timely policy response.
Asymptotics in non-linear cointegrating regression: theory and applications. This project provides fundamental research in statistics, econometrics and probability. The results on martingales and nonlinear functionals of integrated stochastic processes will apply to a range of statistical, empirical finance and economic models.
Non-linear cointegrating regression with endogeneity. This project aims to develop the asymptotic theory of estimation and statistical inference in models concerned with non-linear co-integrating regression with endogeneity and long memory. This project will tackle a number of long-standing technical problems related to non-linear covariance functionals and non-linear transformation of nonstationary time series. This project is intended to provide technical tools for practitioners to study the l ....Non-linear cointegrating regression with endogeneity. This project aims to develop the asymptotic theory of estimation and statistical inference in models concerned with non-linear co-integrating regression with endogeneity and long memory. This project will tackle a number of long-standing technical problems related to non-linear covariance functionals and non-linear transformation of nonstationary time series. This project is intended to provide technical tools for practitioners to study the long-run relationship of economic variables, and could apply to a range of statistical, empirical finance and economic models, enhancing national leadership in these areas.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
Discovery Early Career Researcher Award - Grant ID: DE200100896
Funder
Australian Research Council
Funding Amount
$427,008.00
Summary
How to beat model uncertainty with more information. Experience of the 2008 financial crisis exposed a weakness in our over-reliance on mathematical models. The main aim of this project is to develop mathematical tools to investigate the role of information in reducing model uncertainty. The project will undertake pressing research in robust finance, which is now one of the most active and dynamic topics in financial mathematics. It expects to quantify the value of information under uncertainty ....How to beat model uncertainty with more information. Experience of the 2008 financial crisis exposed a weakness in our over-reliance on mathematical models. The main aim of this project is to develop mathematical tools to investigate the role of information in reducing model uncertainty. The project will undertake pressing research in robust finance, which is now one of the most active and dynamic topics in financial mathematics. It expects to quantify the value of information under uncertainty in mathematical modelling. It will generate new knowledge in probability theory and stochastic processes providing a significant mathematical contribution in its own right.Read moreRead less
Can green investors drive the transition to a low emissions economy? The project aims to develop a game-theoretical approach to model the impact of climate change on financial markets by studying the interactions between the government, companies and investors. Expected outcomes include novel solution concepts for stochastic games with heterogeneous beliefs, asymmetric information, and model uncertainty, as well as optimal investment and production strategies under climate driven economic transi ....Can green investors drive the transition to a low emissions economy? The project aims to develop a game-theoretical approach to model the impact of climate change on financial markets by studying the interactions between the government, companies and investors. Expected outcomes include novel solution concepts for stochastic games with heterogeneous beliefs, asymmetric information, and model uncertainty, as well as optimal investment and production strategies under climate driven economic transitions. Results will be used to validate and improve the recently launched Australian based climate transition index. The project should yield significant benefits for the financial industry and investors by providing novel insights into financial risks during the transition to a low emissions economy.Read moreRead less
Statistical Analysis of State-Dependent Government Spending Multipliers. This project aims to provide a new statistical analysis of the government spending multiplier by acknowledging that government spending is the sum of sectoral spending which has heterogeneous effects on the economy. An added complication is that the multiplier can also be state-dependent, meaning that its magnitude can differ across recessions and expansions. Expected outcomes of this project include a better understanding ....Statistical Analysis of State-Dependent Government Spending Multipliers. This project aims to provide a new statistical analysis of the government spending multiplier by acknowledging that government spending is the sum of sectoral spending which has heterogeneous effects on the economy. An added complication is that the multiplier can also be state-dependent, meaning that its magnitude can differ across recessions and expansions. Expected outcomes of this project include a better understanding of the components of the multiplier by novel decomposition and the development of a new statistical test for the state-dependency of the multiplier. This should provide significant benefits to researchers by bringing in new tools and insights and to policymakers by providing timely guidance on fiscal policies.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.