Intra-day price variations in financial markets are driven by the sequence of orders, called the order flow, that is submitted at high frequency by traders. This paper introduces a novel application of the Sequence Generative Adversarial Networks framework to model the order flow, such that random sequences of the order flow can then be generated to simulate the intra-day variation of prices. As a benchmark, a well-known parametric model from the quantitative finance literature is selected. The models are fitted, and then multiple random paths of the order flow sequences are sampled from each model. Model performances are then evaluated by using the generated sequences to simulate price variations, and we compare the empirical regularities between the price variations produced by the generated and real sequences. The empirical regularities considered include the distribution of the price log-returns, the price volatility, and the heavy-tail of the log-returns distributions. The results show that the order sequences from the generative model are better able to reproduce the statistical behaviour of real price variations than the sequences from the benchmark.
翻译:金融市场的日常价格变化是由交易商以高频率提交的订单顺序(称为订单流程)驱动的。本文介绍了“序列生成反逆网络”框架的新应用,以模拟订单流程,这样就可以产生订单流动的随机序列,以模拟价格的日常变化。作为一个基准,从定量金融文献中选择了一个众所周知的参数模型。模型是合适的,然后从每个模型中抽样选取订单流程序列的多条随机路径。然后,通过使用生成序列模拟价格变化来评估模型的性能,我们比较所生成的价格变化与实际序列之间的实证规律性。考虑的经验规律包括价格日志回报的分布、价格波动以及日志回报分布的重尾。结果显示,从基因化模型中选取的顺序更能复制实际价格变化的统计行为,而不是基准序列的顺序。