A Topological Approach to Parameterizing Deep Hedging Networks

Alok Das and Kiseop Lee

Papers from arXiv.org

Abstract: Deep hedging uses recurrent neural networks to hedge financial products that cannot be fully hedged in incomplete markets. Previous work in this area focuses on minimizing some measure of quadratic hedging error by calculating pathwise gradients, but doing so requires large batch sizes and can make training effective models in a reasonable amount of time challenging. We show that by adding certain topological features, we can reduce batch sizes substantially and make training these models more practically feasible without greatly compromising hedging performance.

Date: 2025-10
New Economics Papers: this item is included in nep-big, nep-cmp and nep-rmg
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