Sequential hypothesis testing in machine learning, and crude oil price jump size detection

Michael Roberts and Indranil SenGupta

Papers from arXiv.org

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.

Date: 2020-04, Revised 2020-12
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm, nep-ene and nep-gen
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Citations: View citations in EconPapers (8)

Published in Applied Mathematical Finance, 2020

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2004.08889

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