用拓扑特征降低深度对冲模型训练的批量需求
A Topological Approach to Parameterizing Deep Hedging Networks
- 引入拓扑特征改进深度对冲网络参数化
- 批量大小减少后仍保持良好对冲效果
- 适合金融工程与量化交易研究者
深度对冲利用循环神经网络来对冲不完全市场中无法完全对冲的金融产品。以往研究通过计算路径梯度最小化二次对冲误差，但需较大批量，导致训练耗时长且效率低。本文表明，加入特定拓扑特征后，可显著降低批量需求，使模型训练更实用，同时对冲性能下降有限。
原文摘要 · Abstract (English)
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.
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