2025/10/19 by Alok Das, Das, Alok, Lee, Kiseop
Decision Sciences · Economics, Econometrics and Finance · Computer Science · #Stock Market Forecasting Methods #Credit Risk and Financial Regulations #Blockchain Technology Applications and Security
paper · pdf · doi:10.48550/arxiv.2510.16938
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.