Deep Hedging: Learning to Simulate Equity Option Markets
This work provides a tool for extending limited real-world datasets to train and evaluate option trading strategies, which is a domain-specific problem for financial analysts and researchers.
The authors tackled the problem of generating realistic equity option market data by developing simulators using generative adversarial networks (GANs), showing that these network-based generators outperform classical methods on benchmark metrics with adversarial training achieving the best performance.
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.