Deep learning based regime-switching models of energy commodity prices

We discuss a deep learning based approach to model the complex dynamics of commodity prices observed in real markets. A regime-switching model is proposed to describe the time evolution of market prices. In this model, the base regime is described by a mean-reverting diffusion process and the second regime is driven by the predictions of a deep neural network trained on the market log-returns time series. A statistical technique, based on the method of simulated moments, is proposed to estimate the model on market data. We applied this methodology to energy commodity price time series with very different characteristics, namely the US wholesale electricity, natural gas and crude oil price daily time series. The obtained results show a good agreement with empirical data. In particular, the model seems to reproduce in a very interesting way the first four central moments of the empirical distributions of log-returns as well as the shape of the observed price time series.