Probabilistic Multivariate Time Series Forecasting with Diffusion Copulas

ArXi:2605.19685v1 Announce Type: cross 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.