Implementation of the vanilla Deep Hedging engine
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Updated
Jan 22, 2026 - Jupyter Notebook
Implementation of the vanilla Deep Hedging engine
A curated list of resources dedicated to Deep Hedging
Coding assignments of the "Machine Learning in Finance & Insurance" course at ETH Zürich (Fall 2024).
Deep Hedging neural network for dynamically pricing and managing derivatives risk under realistic market frictions
A package to learn optimal hedges by a deep feed forward neural network, to minimise the terminal error
Code and reproducibility package for “What Does Deep Hedging Actually Learn? Delta Corrections, Regime Fragility, and Symbolic Distillation.”
Interpretable volatility-surface prototype hedger that matches delta-vega on tail risk while staying auditable (ICAIF 2026)
LightGBM-based hedging strategy under Merton's jump diffusion with custom loss and delta approximation
Numpy-only replication of Deep Hedging (Buehler et al. 2019): a neural hedging policy trained via hand-written backprop-through-time to minimize entropic risk under transaction costs, benchmarked against Black-Scholes delta hedging.
Deep-learning option pricing and hedging: a neural surrogate for Asian options benchmarked against Monte Carlo, a rough Bergomi model for 0DTE, a CVaR deep hedging policy, live calibration, and an interactive dashboard.
Neural hedging policies (GRU, deep-set, no-trade-band) minimising CVaR, entropic and spectral risk under transaction costs, on fused CUDA kernels at billions of paths per second, with a deep BSDE pricer.
Neural SDE framework for rough volatility modeling (H ≈ 0.1) with deep hedging. Implements Davies-Harte fBM, signature-based losses, and convergence analysis.
Deep Hedging under market frictions.
Option-pricing and hedging models built from scratch and verified on real market data, with honest caveats. Black-Scholes to Heston to deep hedging.
Deep‑Hedging in PyTorch (MCPG): europäische & amerikanische Optionen mit RSQP‑Risiko, GJR‑GARCH‑Pfade, IV‑Features und Chebyshev‑Pricing inkl. Baselines.
Adversarial Deep Hedging Benchmark evaluating GAN-based hedging against classical Black-Scholes under market stress.
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