Financial time series simulation is a central topic since it extends the limited real data for training and evaluation of trading strategies. It is also challenging because of the complex statistical properties of the real financial data. We introduce two generative adversarial networks (GANs), which utilize the convolutional networks with attention and the transformers, for financial time series simulation. The GANs learn the statistical properties in a data-driven manner and the attention mechanism helps to replicate the long-range dependencies. The proposed GANs are tested on the S&P 500 index and option data, examined by scores based on the stylized facts and are compared with the pure convolutional GAN, i.e. QuantGAN. The attention-based GANs not only reproduce the stylized facts, but also smooth the autocorrelation of returns.
翻译:金融时序模拟是一个中心议题,因为它扩展了用于培训和评估贸易战略的有限真实数据,也具有挑战性,因为实际金融数据具有复杂的统计特性。我们引入了两个基因对抗网络(GANs),利用富集网络和变压器进行金融时序模拟。GANs以数据驱动的方式学习统计属性,关注机制有助于复制远距离依赖性。拟议的GANs根据S & P 500指数和选项数据进行测试,根据标准化事实进行分数审查,并与纯革命性GAN(即QuantGAN)进行比较。基于关注的GANs不仅复制了结构化事实,而且还使回报的自动关系平稳化。