Wasserstein Distributionally Robust Rare-Event Simulation

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Highly relevant to quantitative trading and risk management. The paper directly addresses portfolio loss probability estimation (Example 1 uses European call/put options with delta-gamma approximation), which is fundamental to VaR and tail risk management. The distributionally robust framework is particularly valuable for stress testing and model risk assessment in trading desks. The method provides worst-case bounds that protect against model misspecification, a critical concern in quantitative finance. However, it is primarily a simulation/estimation methodology rather than a trading strategy or pricing model.

Moderate complexity. The core algorithm involves: (1) sampling from exponential and normal distributions (simple), (2) computing distances to convex sets (requires optimization for general convex sets), (3) a deterministic root-finding procedure (standard numerical methods), and (4) evaluating likelihood ratios. The paper emphasizes that the method is 'simple to implement' with 'low sampling costs.' The main computational challenge is the distance computation d(x, E) for general convex sets and the root-finding step, though no additional sampling is needed for root-finding.

The paper provides a complete algorithm (Algorithm 1), detailed mathematical formulations, and numerical experiments with specific parameter settings. However, no code repository is mentioned. The toy example and portfolio loss example are well-specified with parameters from Glasserman et al. [2000]. Theoretical proofs are provided in appendices. Reproducibility is moderate as it requires implementing the DRIS algorithm, root-finding procedures, and distance computations for convex sets.