Deep Learning Based Financial Tail Risk Modeling and Forecasting

Abstract

This project pursues breakthroughs in modelling time effects which help reveal the hidden underlying structure in time series data, with a focus on flexible 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 data 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 their time series data.