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Refinements of Barndorff-Nielsen and Shephard Model: An Analysis of Crude Oil Price with Machine Learning

Author

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  • Indranil SenGupta

    (North Dakota State University)

  • William Nganje

    (North Dakota State University)

  • Erik Hanson

    (North Dakota State University)

Abstract

A commonly used stochastic model for derivative and commodity market analysis is the Barndorff-Nielsen and Shephard (BN–S) model. Though this model is very efficient and analytically tractable, it suffers from the absence of long range dependence and many other issues. For this paper, the analysis is restricted to crude oil price dynamics. A simple way of improving the BN–S model with the implementation of various machine learning algorithms is proposed. This refined BN–S model is more efficient and has fewer parameters than other models which are used in practice as improvements of the BN–S model. The procedure and the model show the application of data science for extracting a “deterministic component” out of processes that are usually considered to be completely stochastic. Empirical applications validate the efficacy of the proposed model for long range dependence.

Suggested Citation

  • Indranil SenGupta & William Nganje & Erik Hanson, 2021. "Refinements of Barndorff-Nielsen and Shephard Model: An Analysis of Crude Oil Price with Machine Learning," Annals of Data Science, Springer, vol. 8(1), pages 39-55, March.
    • Handle: RePEc:spr:aodasc:v:8:y:2021:i:1:d:10.1007_s40745-020-00256-2
    DOI: 10.1007/s40745-020-00256-2
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    Cited by:

    1. Aidin Zehtab-Salmasi & Ali-Reza Feizi-Derakhshi & Narjes Nikzad-Khasmakhi & Meysam Asgari-Chenaghlu & Saeideh Nabipour, 2023. "Multimodal Price Prediction," Annals of Data Science, Springer, vol. 10(3), pages 619-635, June.
    2. Arai, Takuji & Imai, Yuto, 2024. "Monte Carlo simulation for Barndorff–Nielsen and Shephard model under change of measure," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 218(C), pages 223-234.
    3. Humayra Shoshi & Indranil SenGupta, 2020. "Hedging and machine learning driven crude oil data analysis using a refined Barndorff-Nielsen and Shephard model," Papers 2004.14862, arXiv.org, revised Feb 2021.
    4. Michael Roberts & Indranil SenGupta, 2020. "Sequential hypothesis testing in machine learning, and crude oil price jump size detection," Papers 2004.08889, arXiv.org, revised Dec 2020.
    5. Shantanu Awasthi & Indranil SenGupta, 2020. "First exit-time analysis for an approximate Barndorff-Nielsen and Shephard model with stationary self-decomposable variance process," Papers 2006.07167, arXiv.org, revised Jan 2021.
    6. 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.
    7. 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.
    8. Michael Roberts & Indranil SenGupta, 2020. "Infinitesimal generators for two-dimensional Lévy process-driven hypothesis testing," Annals of Finance, Springer, vol. 16(1), pages 121-139, March.
    9. Akshit Kurani & Pavan Doshi & Aarya Vakharia & Manan Shah, 2023. "A Comprehensive Comparative Study of Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting," Annals of Data Science, Springer, vol. 10(1), pages 183-208, February.
    10. 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.
    11. Xianfei Hui & Baiqing Sun & Hui Jiang & Yan Zhou, 2022. "Modeling dynamic volatility under uncertain environment with fuzziness and randomness," Papers 2204.12657, arXiv.org, revised Oct 2022.
    12. Manoj Verma & Harish Kumar Ghritlahre & Ghrithanchi Chandrakar, 2023. "Wind Speed Prediction of Central Region of Chhattisgarh (India) Using Artificial Neural Network and Multiple Linear Regression Technique: A Comparative Study," Annals of Data Science, Springer, vol. 10(4), pages 851-873, August.
    13. Michael Roberts & Indranil SenGupta, 2019. "Infinitesimal generators for two-dimensional L\'evy process-driven hypothesis testing," Papers 1911.08412, arXiv.org.

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