论文标题
对冲和机器学习驱动的原油数据分析，使用精致的Barndorff-Nielsen和Shephard模型
Hedging and machine learning driven crude oil data analysis using a refined Barndorff-Nielsen and Shephard model
论文作者
论文摘要
在本文中，实施了精致的Barndorff-Nielsen和Shephard（BN-S）模型，以寻找商品市场的最佳对冲策略。 BN-S模型的改进是通过各种机器和深度学习算法获得的。改进导致从经验数据集中提取确定性参数。该问题通过几种不同的方法转化为适当的分类问题：波动率方法和持续时间方法。该分析是针对Bakken原油数据实施的，并且对于广泛的数据集获得了上述确定性参数。通过在精制模型中实现此参数，所得模型的性能要比经典的BN-S模型好得多。
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.
