使用改进的Barndorff-Nielsen和Shephard模型进行套期保值和机器学习驱动的原油数据分析
Hedging and machine learning driven crude oil data analysis using a refined Barndorff-Nielsen and Shephard model
摘要：
在本文中，一个改进的Barndorff-Nielsen和Shephard（BN-S）模型实现了商品市场的最优套期保值策略。BN-S模型的细化是通过各种机器和深度学习算法获得的。细化导致从经验数据集的确定性参数的提取。该问题被转化为一个适当的分类问题，有几个不同的方法：波动率的方法和持续时间的方法。对巴肯原油数据进行了分析，得到了上述确定性参数。通过在改进模型中实现该参数，所得到的模型比经典的BN-S模型表现得更好。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
