用线性化目标提升深度对冲的稳定性和效率
Robust and Efficient Deep Hedging via Linearized Objective Neural Network
- 引入周期性固定梯度优化，稳定训练过程
- 收敛速度更快，对噪声数据更鲁棒，表现优于传统方法
- 适合金融衍生品风险对冲场景，尤其在波动市场中
深度对冲通过深度学习技术实现了金融衍生品风险管理的前沿突破。然而，现有方法常面临计算效率低、对噪声数据敏感及优化复杂等问题，限制了其在动态多变市场中的实际应用。为此，我们提出深度对冲线性化目标神经网络（DHLNN），一种更具鲁棒性与泛化能力的框架，改进深度学习模型的训练过程。该框架结合周期性固定梯度优化与线性化训练动态，稳定训练、加速收敛，并增强对噪声金融数据的鲁棒性。同时融合轨迹级优化与黑-斯科尔斯德尔塔锚定机制，在保持与经典金融理论一致的同时，具备适应真实市场条件的灵活性。在合成数据和真实市场数据上的大量实验验证了DHLNN的有效性，证明其在多种市场情景下均能实现更快收敛、更高稳定性及更优对冲表现。
原文摘要 · Abstract (English)
Deep hedging represents a cutting-edge approach to risk management for financial derivatives by leveraging the power of deep learning. However, existing methods often face challenges related to computational inefficiency, sensitivity to noisy data, and optimization complexity, limiting their practical applicability in dynamic and volatile markets. To address these limitations, we propose Deep Hedging with Linearized-objective Neural Network (DHLNN), a robust and generalizable framework that enhances the training procedure of deep learning models. By integrating a periodic fixed-gradient optimization method with linearized training dynamics, DHLNN stabilizes the training process, accelerates convergence, and improves robustness to noisy financial data. The framework incorporates trajectory-wide optimization and Black-Scholes Delta anchoring, ensuring alignment with established financial theory while maintaining flexibility to adapt to real-world market conditions. Extensive experiments on synthetic and real market data validate the effectiveness of DHLNN, demonstrating its ability to achieve faster convergence, improved stability, and superior hedging performance across diverse market scenarios.
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