Sequential Hypothesis Testing in Machine Learning, and Crude Oil Price Jump Size Detection

ABSTRACT

In this paper, we present a sequential hypothesis test for the detection of the distribution of jump size in Lévy processes. Infinitesimal generators for the corresponding log-likelihood ratios are presented and analysed. 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 data set, and the deterministic component is implemented to improve the Barndorff-Nielsen & Shephard model, a commonly used stochastic model for derivative and commodity market analysis.

KEYWORDS:

• Lévy processes
• hypothesis test
• machine learning
• crude oil price
• Barndorff-Nielsen & Shephard model

Acknowledgments

The authors would like to thank the anonymous reviewers for their careful reading of the manuscript and for suggesting points to improve the quality of the paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).