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

Michael Roberts and Indranil SenGupta

Applied Mathematical Finance, 2020, vol. 27, issue 5, 374-395

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.

Date: 2020
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DOI: 10.1080/1350486X.2020.1859943

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