  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.
