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 Copula to model the joint dependence. Applied to cryptocurrency markets, our approach demonstrates superior performance over state-of-the-art baselines in forecasting systemic extremes of both marginal and joint events. Crucially, we demonstrate that while baseline models classify simultaneous market crashes as statistically impossible "Black Swans" (high surprise), our framework identifies them as "Expected Crashes" (low surprise), successfully preserving the correlation structure necessary for robust risk management during contagion events.

翻译：准确评估金融风险需要捕捉单个资产波动性及极端市场事件中出现的复杂非对称依赖结构。尽管现代基于扩散的模型推进了多变量预测技术，但当端到端训练时，这些模型常存在"正态性偏差"——为保持联合一致性而牺牲边际校准精度，持续低估尾部风险。为此，我们提出扩散连接函数框架（Diffusion-Copula），显式解耦边际分布学习与依赖结构建模。具体采用深度混合密度网络捕捉资产的重尾动态特征，继而通过分类扩散连接函数（Classification-Diffusion Copula）建模联合依赖关系。在加密货币市场应用中，该方法在预测系统性边际事件与联合事件的极端情形时，性能超越了多个最先进基线模型。关键发现表明：当基线模型将同步市场崩盘归类为统计上不可能的"黑天鹅"事件（高意外性）时，本框架将其识别为"可预期崩盘"（低意外性），成功保留了风险蔓延事件中实现稳健风险管理所需的关键相关性结构。