A Topological Approach to Parameterizing Deep Hedging Networks
This work addresses a computational bottleneck in financial deep hedging, making it more accessible for incomplete markets.
The paper tackles the challenge of training deep hedging models efficiently by reducing the required batch sizes through the addition of topological features, achieving practical feasibility without significantly compromising hedging performance.
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.