Automated financial multi-path GETS modelling

Abstract

General-to-Specific (GETS) modelling has witnessed major advances over the last decade thanks to the automation of multi-path GETS specification search. However, several scholars have argued that the estimation complexity associated with financial models constitutes an obstacle to multi-path GETS modelling in finance. We provide a result with associated methods that overcome many of the problems, and develop a simple but general and flexible algorithm that automates financial multi-path GETS modelling. Starting from a general model where the mean specification can contain autoregressive (AR) terms and explanatory variables, and where the exponential variance specification can include log-ARCH terms, log-GARCH terms, asymmetry terms, Bernoulli jumps and other explanatory variables, the algorithm we propose returns parsimonious mean and variance specifications, and a fat-tailed distribution of the standardised error if normality is rejected. The finite sample properties of the methods and of the algorithm are studied by means of extensive Monte Carlo simulations, and two empirical applications suggest the methods and algorithm are very useful in practice.