Hedging and machine learning driven crude oil data analysis using a refined Barndorff-Nielsen and Shephard model
This work addresses hedging strategies for commodity markets, specifically crude oil, but appears incremental as it refines an existing model with machine learning techniques.
The paper tackled the problem of finding an optimal hedging strategy for commodity markets by refining the Barndorff-Nielsen and Shephard (BN-S) model using machine and deep learning algorithms, resulting in a deterministic parameter that significantly improved model performance over the classical BN-S model, as demonstrated on Bakken crude oil data.
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.