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 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.

24 pages, 7 figures

References in corpus (2)

• Refinements of Barndorff-Nielsen and Shephard model: an analysis of crude oil price with machine learning
• Infinitesimal generators for two-dimensional Lévy process-driven hypothesis testing

Cited by in corpus (4)

• Analysis of optimal portfolio on finite and small time horizons for a stochastic volatility market model
• Stochastic volatility modeling of high-frequency CSI 300 index and dynamic jump prediction driven by machine learning
• First exit-time analysis for an approximate Barndorff-Nielsen and Shephard model with stationary self-decomposable variance process
• Hedging and machine learning driven crude oil data analysis using a refined Barndorff-Nielsen and Shephard model