Robust and Efficient Deep Hedging via Linearized Objective Neural Network
Robust and Efficient Deep Hedging via Linearized Objective Neural Network
The paper introduces Deep Hedging with Linearized-objective Neural Network (DHLNN), a robust framework that addresses key limitations of existing deep hedging methods: computational inefficiency, sensitivity to noisy data, and optimization complexity. DHLNN employs a nested training procedure where outer iterations compute and fix gradients, while inner iterations optimize a linearized approximation of the objective. This periodic fixed-gradient approach stabilizes training, accelerates convergence, and reduces sensitivity to market noise. Additionally, the framework anchors hedging positions to the Black-Scholes Delta, ensuring alignment with financial theory while maintaining flexibility. Extensive experiments on synthetic and real market data demonstrate that DHLNN achieves faster convergence, improved stability, and superior hedging performance across various scenarios, including path-dependent derivatives and transaction costs. The method significantly reduces residual risk and capital injection, making it a practical solution for dynamic hedging in volatile markets.
Highlights
- 1Proposes DHLNN, a novel framework integrating periodic fixed-gradient optimization with linearized training dynamics for deep hedging.
- 2Enhances training stability, accelerates convergence, and improves robustness to noisy financial data.
- 3Incorporates Black-Scholes Delta anchoring to align neural network outputs with established financial theory.
- 4Achieves superior hedging performance across diverse market scenarios, including path-dependent derivatives and transaction costs.
Methods
- MLinearized-objective neural network with nested optimization: outer iterations compute fixed gradients, inner iterations optimize a linearized objective.
- MPeriodic fixed-gradient updates to stabilize training and reduce sensitivity to noise.
- MBlack-Scholes Delta anchoring to guide neural network outputs toward theoretically sound hedging positions.
- MTrajectory-wide optimization over the entire hedging horizon to minimize residual risk.
Results
- RDHLNN achieves faster convergence and improved stability compared to standard deep hedging methods.
- RDemonstrates superior hedging performance (lower P&L variance and reduced capital injection) under synthetic and real market data.
- RRobust to noisy data and volatile market conditions, outperforming baselines in out-of-distribution scenarios.
- REffectively handles transaction costs and path-dependent derivatives like lookback options.
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