Deep kernel hedging paper blends neural nets with kernel methods for robust derivative hedging
chaumian · x · 2026-09-29
A new arXiv paper introduces Deep Kernel Hedging, a framework combining deep learning's flexibility with the inductive bias of kernel methods for financial hedging. The hedging functional lives in an RKHS whose kernel is parameterized by a neural embedding of input features.
Key points:
- Minimizes regularized empirical risk under convex losses, handling path-dependence via truncated time-augmented signature features;
- Derives a generalized representer theorem reducing the joint hedging problem to finite dimensions;
- Uses a scalable random Fourier feature approximation with convergence guarantees, cutting large kernel-matrix costs.
Experiments on synthetic and real data show competitive, robust hedging performance versus standard kernel methods and classical deep hedging, especially in low-data regimes.
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