用生成模型模拟市场状态，优化投资组合以降低暴跌风险。
Multi-Agent Regime-Conditioned Diffusion (MARCD) for CVaR-Constrained Portfolio Decisions
- 基于隐变量模型识别市场状态，生成对应情景。
- 在2020-2025年外样本中最大回撤降为9.3%，比基准低34%。
- 适合关注风险控制与可解释决策的量化投资者。
我们研究了结合状态条件生成情景与凸约束CVaR分配器是否能提升应对市场状态转变的投资决策。提出MARCD框架：(i) 使用高斯隐马尔可夫模型推断潜在状态；(ii) 通过扩散生成器生成状态条件情景；(iii) 采用混合收缩矩进行信号提取；(iv) 构建受控的CVaR上图二次规划。创新点：在情景生成阶段引入尾部加权扩散目标，强化对低分位结果（关乎回撤）的关注；并设计状态专家门控去噪器，其门控值随危机后验概率上升。所有组件均通过分配器端到端评估。在2005-2025年流动性多资产ETF上严格走查测试中，MARCD展现出更强的情景校准能力与显著更小的回撤——2020-2025年外样本最大回撤为9.3%，相较基准BL的14.1%下降34%。该框架提供可审计流程，明确包含预算、箱型与换手率约束，验证了金融决策感知生成建模的价值。
原文摘要 · Abstract (English)
We examine whether regime-conditioned generative scenarios combined with a convex CVaR allocator improve portfolio decisions under regime shifts. We present MARCD, a generative-to-decision framework with: (i) a Gaussian HMM to infer latent regimes; (ii) a diffusion generator that produces regime-conditioned scenarios; (iii) signal extraction via blended, shrunk moments; and (iv) a governed CVaR epigraph quadratic program. Contributions: Within the Scenario stage we introduce a tail-weighted diffusion objective that up-weights low-quantile outcomes relevant for drawdowns and a regime-expert (MoE) denoiser whose gate increases with crisis posteriors; both are evaluated end-to-end through the allocator. Under strict walk-forward on liquid multi-asset ETFs (2005-2025), MARCD exhibits stronger scenario calibration and materially smaller drawdowns: MaxDD 9.3% versus 14.1% for BL (a 34% reduction) over 2020-2025 out-of-sample. The framework provides an auditable pipeline with explicit budget, box, and turnover constraints, demonstrating the value of decision-aware generative modeling in finance.
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