Robust and Efficient Deep Hedging via Linearized Objective Neural Network

• URL: http://arxiv.org/abs/2502.17757v1
• Date: Tue, 25 Feb 2025 01:23:21 GMT
• Title: Robust and Efficient Deep Hedging via Linearized Objective Neural Network
• Authors: Lei Zhao, Lin Cai,
• Abstract summary: We propose Deep Hedging with Linearized-objective Neural Network (DHLNN), a robust and generalizable framework.<n>DHLNN stabilizes the training process, accelerates convergence, and improves robustness to noisy financial data.<n>We show that DHLNN achieves faster convergence, improved stability, and superior hedging performance across diverse market scenarios.
• Score: 9.658615377672929
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Adaptive Nesterov Accelerated Distributional Deep Hedging for Efficient Volatility Risk Management [8.593840398820971]
  We introduce a new framework for dynamic Vega hedging, the Adaptive Nesterov Accelerated Distributional Deep Hedging (ANADDH)<n>ANADDH combines distributional reinforcement learning with a tailored design based on adaptive Nesterov acceleration.<n>Our results confirm that this innovative combination of distributional reinforcement learning with the proposed optimization techniques improves financial risk management.
  arXiv  Detail & Related papers  (2025-02-25T02:12:16Z)
• A New Way: Kronecker-Factored Approximate Curvature Deep Hedging and its Benefits [0.0]
  This paper advances the computational efficiency of Deep Hedging frameworks through the novel integration of Kronecker-Factored Approximate Curvature (K-FAC) optimization. The proposed architecture couples Long Short-Term Memory (LSTM) networks with K-FAC second-order optimization.
  arXiv  Detail & Related papers  (2024-11-22T15:19:40Z)
• Data-Aware Training Quality Monitoring and Certification for Reliable Deep Learning [13.846014191157405]
  We introduce YES training bounds, a novel framework for real-time, data-aware certification and monitoring of neural network training. We show that YES bounds offer insights beyond conventional local optimization perspectives, such as identifying when training losses plateau in suboptimal regions. We offer a powerful tool for real-time evaluation, setting a new standard for training quality assurance in deep learning.
  arXiv  Detail & Related papers  (2024-10-14T18:13:22Z)
• Data-Driven Lipschitz Continuity: A Cost-Effective Approach to Improve Adversarial Robustness [47.9744734181236]
  We explore the concept of Lipschitz continuity to certify the robustness of deep neural networks (DNNs) against adversarial attacks. We propose a novel algorithm that remaps the input domain into a constrained range, reducing the Lipschitz constant and potentially enhancing robustness. Our method achieves the best robust accuracy for CIFAR10, CIFAR100, and ImageNet datasets on the RobustBench leaderboard.
  arXiv  Detail & Related papers  (2024-06-28T03:10:36Z)
• Dynamic Environment Responsive Online Meta-Learning with Fairness Awareness [30.44174123736964]
  We introduce an innovative adaptive fairness-aware online meta-learning algorithm, referred to as FairSAOML. Our experimental evaluation on various real-world datasets in dynamic environments demonstrates that our proposed FairSAOML algorithm consistently outperforms alternative approaches.
  arXiv  Detail & Related papers  (2024-02-19T17:44:35Z)
• Achieving Constraints in Neural Networks: A Stochastic Augmented Lagrangian Approach [49.1574468325115]
  Regularizing Deep Neural Networks (DNNs) is essential for improving generalizability and preventing overfitting. We propose a novel approach to DNN regularization by framing the training process as a constrained optimization problem. We employ the Augmented Lagrangian (SAL) method to achieve a more flexible and efficient regularization mechanism.
  arXiv  Detail & Related papers  (2023-10-25T13:55:35Z)
• Multiplicative update rules for accelerating deep learning training and increasing robustness [69.90473612073767]
  We propose an optimization framework that fits to a wide range of machine learning algorithms and enables one to apply alternative update rules. We claim that the proposed framework accelerates training, while leading to more robust models in contrast to traditionally used additive update rule.
  arXiv  Detail & Related papers  (2023-07-14T06:44:43Z)
• Implicit Stochastic Gradient Descent for Training Physics-informed Neural Networks [51.92362217307946]
  Physics-informed neural networks (PINNs) have effectively been demonstrated in solving forward and inverse differential equation problems. PINNs are trapped in training failures when the target functions to be approximated exhibit high-frequency or multi-scale features. In this paper, we propose to employ implicit gradient descent (ISGD) method to train PINNs for improving the stability of training process.
  arXiv  Detail & Related papers  (2023-03-03T08:17:47Z)
• Augmented Bilinear Network for Incremental Multi-Stock Time-Series Classification [83.23129279407271]
  We propose a method to efficiently retain the knowledge available in a neural network pre-trained on a set of securities. In our method, the prior knowledge encoded in a pre-trained neural network is maintained by keeping existing connections fixed. This knowledge is adjusted for the new securities by a set of augmented connections, which are optimized using the new data.
  arXiv  Detail & Related papers  (2022-07-23T18:54:10Z)
• Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets [84.90242084523565]
  Traditional time-series econometric methods often appear incapable of capturing the true complexity of the multi-level interactions driving the price dynamics. By adopting a state-of-the-art second-order optimization algorithm, we train a Bayesian bilinear neural network with temporal attention. By addressing the use of predictive distributions to analyze errors and uncertainties associated with the estimated parameters and model forecasts, we thoroughly compare our Bayesian model with traditional ML alternatives.
  arXiv  Detail & Related papers  (2022-03-07T18:59:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.