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