Wasserstein Generative Data Modeling for Robust Portfolio Optimization Under Distributional Uncertainty

This study proposes a novel distributionally robust portfolio optimization framework based on Wasserstein generative modeling, aiming to address the challenges of distributional uncertainty, tail risk, and structural drift in financial markets. The model integrates Wasserstein distance-based robust optimization with generative adversarial learning to jointly enhance risk control and return stability. Specifically, a Wasserstein generative adversarial network is employed to reconstruct the latent distribution of asset returns, enabling the capture of non-Gaussian features and tail dependencies in complex market environments. By constructing an uncertainty set under the Wasserstein metric, the optimization process achieves dynamic balance between empirical risk minimization and robustness to distributional perturbations. Furthermore, the framework incorporates a dual optimization mechanism that alternately updates generative and optimization parameters to adaptively align with changing market structures. Experimental evaluations on multi-asset datasets demonstrate that the proposed model achieves higher Sharpe ratios, lower maximum drawdowns, and improved robustness compared with conventional reinforcement learning-based and mean-variance methods. The results verify that integrating Wasserstein generative modeling into distributionally robust optimization provides an effective and interpretable pathway for achieving stable asset allocation and risk-aware decision-making under volatile financial conditions.