A posteriori error bounds for the Uncertain Volatility Model

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Highly relevant for robust pricing and hedging in markets with volatility uncertainty. Provides rigorous error bounds for neural network-based pricing models, which is crucial for institutional risk management and regulatory compliance.

Requires implementing stochastic policy gradient algorithms, physics-informed neural networks, and complex derivative calculations (Hessians) within a deep learning framework. The dual evaluation involves Monte Carlo resimulation and pathwise optimization.

The paper provides detailed hyperparameters, network architectures, and algorithmic steps for both SPG and PINN methods. However, it does not explicitly link to a public code repository in the provided text, though it references prior work [ATCL+26] for implementation details.