Author: Shoshi, Humayra; SenGupta, Indranil
Title: Hedging and machine learning driven crude oil data analysis using a refined Barndorff-Nielsen and Shephard model Cord-id: 88r7pnoa Document date: 2020_4_29
ID: 88r7pnoa
Snippet: 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 an
Document: 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.
Search related documents:
Co phrase search for related documents- activation function and long lstm short term memory: 1, 2
- activation function and lstm short term memory: 1, 2
- activation function and machine learning: 1, 2, 3, 4, 5, 6
- actual value and logistic regression: 1, 2, 3
- actual value and long duration: 1
- actual value and machine learning: 1, 2, 3
- local minimum and logistic regression: 1
- local minimum and long lstm short term memory: 1
Co phrase search for related documents, hyperlinks ordered by date