Occam’s razor, machine learning and stochastic modeling of complex systems: the case of the Italian energy market

Abstract

In the spirit of Occam’s razor, we propose a parsimoniuos regime-switching model for describing the complex dynamics of electricity and natural gas prices observed in real markets. The model was built using a machine learning-based methodology, namely a cluster analysis to investigate the properties of the stable dynamics and a deep neural network appropriately trained on market data to drive transitions between different regimes. The main purposes of this study are twofold: (1) to build the simplest model capable of incorporating the main stylized facts of electricity and natural gas prices, including dynamic correlation; (2) to define an appropriate calibration procedure on market data. We applied this methodology to the Italian energy market. The results obtained show remarkable agreement with the empirical data, satisfactorily reproducing the first four moments of the empirical distributions of log-returns.