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A commonly used stochastic model for derivative and commodity market analysisis the Barndorff-Nielsen and Shephard (BN-S) model. Though this model is veryefficient and analytically tractable, it suffers from the absence of long rangedependence and many other issues. For this paper, the analysis is restricted tocrude oil price dynamics. A simple way of improving the BN-S model with theimplementation of various machine learning algorithms is proposed. This refinedBN-S model is more efficient and has fewer parameters than other models whichare used in practice as improvements of the BN-S model. The procedure and themodel show the application of data science for extracting a "deterministiccomponent" out of processes that are usually considered to be completelystochastic. Empirical applications validate the efficacy of the proposed modelfor long range dependence.

Refinements of Barndorff-Nielsen and Shephard model: an analysis of crude oil price with machine learning