Last updated
2022-12-20T06:50:56.233+00:00
Abstract
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 suitable performance evaluation metric for financial time series and discuss some connections between their signature-based market generator and deep hedging.
Symplectic ID
1131326
Submitted to ORA
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Publication type
Journal Article
Publication date
09 Jun 2021