论文标题
商品市场上的深层发电机；申请深度对冲
Deep Generators on Commodity Markets; application to Deep Hedging
论文作者
论文摘要
在计算机视觉中获得的良好结果的推动下，时间序列的深层生成方法一直是近年来特别关注的主题，尤其是金融业。在本文中，我们专注于商品市场并测试四种最先进的生成方法，即时间序列生成对抗网络（GAN）Yoon等。 [2019]，因果最佳运输Gan Xu等。 [2020]，Signature Gan Ni等。 [2020]和有条件的Euler Generator Remlinger等。 [2021]在商品时间序列上进行了调整和测试。第一个实验涉及有关商品的历史时间系列的联合一代。第二盘处理了对他产生的时间序列训练的商品选择的深度对冲。此用例说明了一种纯粹的数据驱动方法的风险对冲方法。
Driven by the good results obtained in computer vision, deep generative methods for time series have been the subject of particular attention in recent years, particularly from the financial industry. In this article, we focus on commodity markets and test four state-of-the-art generative methods, namely Time Series Generative Adversarial Network (GAN) Yoon et al. [2019], Causal Optimal Transport GAN Xu et al. [2020], Signature GAN Ni et al. [2020] and the conditional Euler generator Remlinger et al. [2021], are adapted and tested on commodity time series. A first series of experiments deals with the joint generation of historical time series on commodities. A second set deals with deep hedging of commodity options trained on he generated time series. This use case illustrates a purely data-driven approach to risk hedging.
