Financial contagion has been widely recognized as a fundamental risk to the financial system. Particularly potent is price-mediated contagion, wherein forced liquidations by firms depress asset prices and propagate financial stress, enabling crises to proliferate across a broad spectrum of seemingly unrelated entities. Price impacts are currently modeled via exogenous inverse demand functions. However, in real-world scenarios, only the initial shocks and the final equilibrium asset prices are typically observable, leaving actual asset liquidations largely obscured. This missing data presents significant limitations to calibrating the existing models. To address these challenges, we introduce a novel dual neural network structure that operates in two sequential stages: the first neural network maps initial shocks to predicted asset liquidations, and the second network utilizes these liquidations to derive resultant equilibrium prices. This data-driven approach can capture both linear and non-linear forms without pre-specifying an analytical structure; furthermore, it functions effectively even in the absence of observable liquidation data. Experiments with simulated datasets demonstrate that our model can accurately predict equilibrium asset prices based solely on initial shocks, while revealing a strong alignment between predicted and true liquidations. Our explainable framework contributes to the understanding and modeling of price-mediated contagion and provides valuable insights for financial authorities to construct effective stress tests and regulatory policies.

翻译：金融传染已被广泛认为是金融体系的基本风险。其中，价格中介传导尤为显著，即企业被迫清算会压低资产价格并传播金融压力，使危机在表面上毫无关联的广泛实体间扩散。当前，价格影响通过外生反需求函数进行建模。然而，在实际场景中，通常仅能观察到初始冲击和最终均衡资产价格，实际资产清算过程大多难以观测。这种数据缺失对现有模型的校准构成了重大限制。为应对这些挑战，我们提出了一种新颖的双神经网络结构，该结构按序运行两个阶段：第一个神经网络将初始冲击映射为预测的资产清算量，第二个网络利用这些清算量推导出最终的均衡价格。这种数据驱动方法无需预设分析结构即可捕获线性和非线性形式；此外，即使在缺乏可观测清算数据的情况下，它也能有效运行。基于模拟数据集的实验表明，我们的模型仅依据初始冲击就能准确预测均衡资产价格，同时揭示预测清算量与真实清算量之间的高度一致性。这一可解释性框架有助于理解和建模价格中介传导，并为金融当局构建有效的压力测试与监管政策提供宝贵见解。