Probabilistic Multivariate Time Series Forecasting with Diffusion Copulas
Accurately assessing financial risk requires capturing both individual asset volatility and the complex, asymmetric dependence structures that emerge during extreme market events. While modern diffusion-based models have advanced multivariate forecasting, they often suffer from a "normality bias" when trained end-to-end, sacrificing marginal calibration for joint coherence and consistently underestimating tail risk. To address this, we propose a Diffusion-Copula framework that explicitly decouples the learning of marginal distributions from their dependence structure. We employ deep Mixture Density Networks to capture heavy-tailed asset dynamics, followed by a Classification-Diffusion Cop...
| Authors | David Huk, Dongshan Wang, Miha Bresar |
|---|---|
| Primary category | stat.ML |
| Published | 19 May 2026 |
| Last updated at source | 19 May 2026 |
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