Hedging and machine learning driven crude oil data analysis using a refined Barndorff-Nielsen and Shephard model

Humayra Shoshi and Indranil SenGupta

Papers from arXiv.org

Abstract: In this paper, a refined Barndorff-Nielsen and Shephard (BN-S) model is implemented to find an optimal hedging strategy for commodity markets. The refinement of the BN-S model is obtained with various machine and deep learning algorithms. The refinement leads to the extraction of a deterministic parameter from the empirical data set. The problem is transformed to an appropriate classification problem with a couple of different approaches: the volatility approach and the duration approach. The analysis is implemented to the Bakken crude oil data and the aforementioned deterministic parameter is obtained for a wide range of data sets. With the implementation of this parameter in the refined model, the resulting model performs much better than the classical BN-S model.

Date: 2020-04, Revised 2021-02
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ene, nep-gen and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Published in International Journal of Financial Engineering, 2021

Downloads: (external link)
http://arxiv.org/pdf/2004.14862 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2004.14862

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().