用扩散耦合模型分离资产波动与依赖结构，更准预测极端市场风险。
Probabilistic Multivariate Time Series Forecasting with Diffusion Copulas
- 先用密度网络学单个资产的厚尾分布，再用分类扩散耦合学联合依赖关系。
- 在加密货币市场测试中，对联合极端事件的预测准确率优于现有方法。
- 能识别同时暴跌为可预期事件，适合金融风控和量化投资场景。
精准评估金融风险需同时捕捉单个资产的波动性及极端市场事件中的复杂非对称依赖结构。尽管基于扩散的现代模型已推进多变量预测，但端到端训练常导致‘正态性偏差’，牺牲边际校准以换取联合一致性，持续低估尾部风险。为此，我们提出扩散-耦合框架，显式解耦边缘分布与依赖结构的学习。采用深度混合密度网络捕捉具有厚尾特征的资产动态，随后通过分类扩散耦合建模联合依赖关系。应用于加密货币市场时，该方法在预测边缘与联合极端事件方面均显著优于当前最优基线。关键在于，基线模型将同步市场崩盘视为统计上不可能的‘黑天鹅’（高意外性），而本框架将其识别为‘可预期崩盘’（低意外性），成功保留了传染事件下稳健风险管理所需的关联结构。
原文摘要 · Abstract (English)
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.
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