Modelling and forecasting of asset volatility and covariance is of prime importance in the construction of portfolios. In this paper, we present a generalised multi-factor model that incorporates heteroskedasticity and dependence in the idiosyncratic error terms. We apply this model to...

The uncovered interest rate parity puzzle questions the economic relation existing between short term interest rate differentials and exchange rates. One would indeed expect that the differential of interest rates between two countries should be offset by an opposite evolution of the exchange...

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...

This tutorial explores the class of non-parametric time series basis decomposition methods particularly suited for non-stationary time series known as Empirical Mode Decomposition (EMD). A detailed review of the state of the art statistical approaches that combine finite basis signal...

A time-series basis decomposition and trend extraction technique known as Empirical Mode Decomposition (EMD), designed for multi-scale time-frequency decomposition in non-stationary time-series settings, will be combined with Regularised Covariance Regression (RCR) methods to produce a framework...

Abstract In crush spread commodity trading strategies it is a common practice to select portfolio positions not based on statistical properties, but instead based on physical refinery conditions and efficiency in extracting byproducts from crushing raw soybeans to get soymeal and soyoil. The...