Generating financial markets with signatures

Signatures can provide the synthetic data to train deep hedging strategies


While most generative models tend to rely on large amounts of training data, here Hans Buehler et al present a generative model that works reliably even in environments where the amount of available training data is small, irregularly paced or oscillatory. They show how a rough paths-based feature map encoded by the signature of the path outperforms returns-based market generation both numerically and from a theoretical point of view. Finally, they propose a

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