Modelling the industrial production of electric and gas utilities through a stochastic three-factor model

This work aims to demonstrate how a trifactorial stochastic model, which we call CIR3, can be turned into a forecasting tool to predict changes in the industrial production of electric and gas utilities. The model accounts for several stylized facts such as the mean reversion of both the process and its volatility to a short-run mean, non-normality, cluster volatility and fat tails. In addition to that, we provide two theoretical results which are of particular importance in modelling and simulations. The first is the proof of existence and uniqueness of the solution to the SDEs system that describes the model. The second theoretical result is to convert, by the means of Lamperti transformations, the correlated system into an uncorrelated one. The forecasting performance is tested versus an ARIMA-GARCH and a nonlinear regression model (NRM).