Sequential hypothesis testing in machine learning, and crude oil price jump size detection
Methodology
2022-01-26 v3 Mathematical Finance
Machine Learning
Abstract
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.
Cite
@article{arxiv.2004.08889,
title = {Sequential hypothesis testing in machine learning, and crude oil price jump size detection},
author = {Michael Roberts and Indranil SenGupta},
journal= {arXiv preprint arXiv:2004.08889},
year = {2022}
}
Comments
24 pages, 7 figures