The ability to test for statistical causality in linear and non-linear contexts, in stationary or non-stationary settings and to identify whether statistical causality influences trend of volatility forms a piratically important class of problems to explore in multi-modal and multivariate...

Nonlinear non-Gaussian state-space models arise in numerous applications in statistics and signal processing. In this context, one of the most successful and popular approximation techniques is the Sequential Monte Carlo (SMC) algorithm, also known as particle filtering. Nevertheless, this...

It is important to understand the statistical features of mortality data if one is to accurately undertake mortality projection and forecasting when constructing life tables. The ability to accurately forecast mortality is a critical aspect for the study of demography, life insurance product...

This paper has two objectives, the first is to present a detailed overview in the form of a tutorial for the developments of several key quantile time series modelling approaches. The second objective is to develop a general framework to represent such quantile models in a unifying manner in...

We construct a general multi-factor model for estimation and calibration of commodity spot prices and futures valuation. This extends the multi-factor long-short model in Schwartz and Smith (2000) and Yan (2002) in two important aspects: firstly we allow for both the long and short term dynamic...

Increasing the accuracy of forecasting of mortality rates and improving the projection of life expectancy is an important consideration for insurance companies and governments since misleading predictions may result in insufficient funds for retirement and pension plans. The existence of long...

Forecasting life expectancy and mortality are two important aspects for the study of demography. We demonstrate in this work that the existence of long memory in mortality data improves the understanding of mortality and the model incorporating a long memory structure provides a new approach to...

Natural Language Processing (NLP) has started from linguists and the computational linguistics community and has evolved to become a multi-disciplinary field that includes mathematics, statistics, and more recently, data science and machine learning. Together with this expansion, there has also...

A novel hybrid Autoregressive Distributed Lag Mixed Data Sampling (ARDL-MIDAS) model is developed that integrates a combination of both deep neural network multi-head attention Transformer mechanisms and sophisticated stochastic text time-series feature and covariate constructions into a...