Deep Generative Models for Synthetic Financial Data: Applications to Portfolio and Risk Modeling
Published in arXiv Preprint, 2025
This work explores deep generative modeling techniques for synthetic financial data generation with applications to portfolio optimization and risk modeling. The study evaluates model performance and demonstrates practical financial applications.
Recommended citation: Christophe D. Hounwanou, Ulrich Yae Gaba. (2025). "Deep Generative Models for Synthetic Financial Data: Applications to Portfolio and Risk Modeling." arXiv preprint arXiv:2512.21798.
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