Deep Generative Models for Synthetic Financial Data: Applications to Portfolio and Risk Modeling

By Christophe D. Hounwanou, Yaé Ulrich Gaba

Published 2025-12-30

Everscope rating

1687.9

Relevance to quantitative trading

8 / 10

Implementation complexity

7 / 10

Reproducibility

4 / 5

About this paper

Methodology: Deep Generative Modeling for Synthetic Financial Time Series. Problem types: Generative Modeling, Portfolio Optimization, Risk Management, Time Series Forecasting, Density Estimation, Optimization.

arXiv:2512.21798 · Paper rankings

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