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

Humayra Shoshi () and Indranil SenGupta
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Humayra Shoshi: Department of Mathematics, North Dakota State University, Fargo, North Dakota, USA
Indranil SenGupta: Department of Mathematics, North Dakota State University, Fargo, North Dakota, USA

International Journal of Financial Engineering (IJFE), 2021, vol. 08, issue 04, 1-29

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.

Keywords: Variance swaps; quadratic hedging; drawdown; classification problems; stochastic models (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1142/S2424786321500158

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