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Current Selection
Status : Active
Australian State/Territory : NSW
Research Topic : methods
Field of Research : Financial Econometrics
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  • Active Funded Activity

    Discovery Projects - Grant ID: DP200103015

    Funder
    Australian Research Council
    Funding Amount
    $280,000.00
    Summary
    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.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP200103549

    Funder
    Australian Research Council
    Funding Amount
    $305,400.00
    Summary
    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.
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