Deep Hedging: Learning to Simulate Equity Option Markets

2019/11/05 by Magnus Wiese, Lianjun Bai, Ben Wood +1 · 1 citation
Economics, Econometrics and Finance · Computer Science · Mathematics · #q-fin.CP #cs.LG #q-fin.MF #q-fin.ST #stat.ML

paper · pdf

published as NeurIPS 2019 Workshop on Robust AI in Financial Services: Data, Fairness, Explainability, Trustworthiness, and Privacy

arxiv created 2019/11/05 · arxiv updated 2020/04/21

Abstract

We construct realistic equity option market simulators based on generative adversarial networks (GANs). We consider recurrent and temporal convolutional architectures, and assess the impact of state compression. Option market simulators are highly relevant because they allow us to extend the limited real-world data sets available for the training and evaluation of option trading strategies. We show that network-based generators outperform classical methods on a range of benchmark metrics, and adversarial training achieves the best performance. Our work demonstrates for the first time that GANs can be successfully applied to the task of generating multivariate financial time series.

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