  In this paper we present a sequential hypothesis test for the detection of
general jump size distrubution. Infinitesimal generators for the corresponding
log-likelihood ratios are presented and analyzed. Bounds for infinitesimal
generators in terms of super-solutions and sub-solutions are computed. This is
shown to be implementable in relation to various classification problems for a
crude oil price data set. Machine and deep learning algorithms are implemented
to extract a specific deterministic component from the crude oil data set, and
the deterministic component is implemented to improve the Barndorff-Nielsen and
Shephard model, a commonly used stochastic model for derivative and commodity
market analysis.
