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

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  • Michael Roberts
  • Indranil SenGupta

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.

Suggested Citation

  • Michael Roberts & Indranil SenGupta, 2020. "Sequential Hypothesis Testing in Machine Learning, and Crude Oil Price Jump Size Detection," Applied Mathematical Finance, Taylor & Francis Journals, vol. 27(5), pages 374-395, September.
    • Handle: RePEc:taf:apmtfi:v:27:y:2020:i:5:p:374-395
    DOI: 10.1080/1350486X.2020.1859943
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    Citations

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    Cited by:

    1. Minglian Lin & Indranil SenGupta, 2021. "Analysis of optimal portfolio on finite and small time horizons for a stochastic volatility market model," Papers 2104.06293, arXiv.org.
    2. Xianfei Hui & Baiqing Sun & Hui Jiang & Indranil SenGupta, 2021. "Analysis of stock index with a generalized BN-S model: an approach based on machine learning and fuzzy parameters," Papers 2101.08984, arXiv.org, revised Feb 2022.
    3. Minglian Lin & Indranil SenGupta, 2023. "Analysis of optimal portfolio on finite and small-time horizons for a stochastic volatility model with multiple correlated assets," Papers 2302.06778, arXiv.org, revised Dec 2023.
    4. Shubham Ekapure & Nuruddin Jiruwala & Sohan Patnaik & Indranil SenGupta, 2021. "A data-science-driven short-term analysis of Amazon, Apple, Google, and Microsoft stocks," Papers 2107.14695, arXiv.org.
    5. Xianfei Hui & Baiqing Sun & Indranil SenGupta & Yan Zhou & Hui Jiang, 2022. "Stochastic volatility modeling of high-frequency CSI 300 index and dynamic jump prediction driven by machine learning," Papers 2204.02891, arXiv.org, revised Jan 2023.

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