Calibration of storage model by multistage statistical and machine learning methods

Calibration of multidimensional economic problems proven to be difficult, as there is a high risk of problem miss-identification. In this paper we propose a multistage calibration method to estimate the six parameters of a commodity market price model that includes storage. We assume that the commodity prices are derived from the optimal commodity storage time when the demand process follows a mean-reverting log-Ornstein–Uhlenbeck process. Using two alternative value functions, first we propose a two-stage method to maximize the likelihood functions obtained by Milstein method. Then by considering a regularized likelihood functions we propose a multistage method to calibrate the parameters of our problem. After we realize our method is perfectly performing on the simulated data, we encounter it to real commodity data and calibrate the parameters. We observe that our multistage calibration method is robust and that the storage model outperforms the non-storage model