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.

使用序列生成对抗网络框架模拟订单流，以生成随机序列来模拟股价波动，通过比较模拟出的股价波动与实际股价波动的经验规律，结果表明生成模型更能够复制真实股价波动的统计行为。