Hedging and machine learning driven crude oil data analysis using a refined Barndorff-Nielsen and Shephard model
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.
Cite
@article{arxiv.2004.14862,
title = {Hedging and machine learning driven crude oil data analysis using a refined Barndorff-Nielsen and Shephard model},
author = {Humayra Shoshi and Indranil SenGupta},
journal= {arXiv preprint arXiv:2004.14862},
year = {2022}
}
Comments
27 pages, 9 figures. arXiv admin note: text overlap with arXiv:1911.13300