论文标题
使用注意力模拟财务时间序列
Simulating financial time series using attention
论文作者
论文摘要
财务时间序列模拟是一个核心主题，因为它扩展了有限的实际数据，以供交易策略培训和评估。由于真实财务数据的复杂统计特性，这也很具有挑战性。我们介绍了两个生成的对抗网络（GAN），该网络利用引起关注的卷积网络和变压器进行财务时间序列模拟。甘斯以数据驱动的方式学习统计属性，注意机制有助于复制长期依赖性。在标准普尔500指数和期权数据上测试了所提出的gan，该数据通过基于程式化的事实进行了分数检查，并与纯卷积GAN（即Quantgan）进行了比较。基于注意力的甘斯不仅重现了风格化的事实，而且还要平滑回报的自相关。
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.
