Modeling Inverse Demand Function with Explainable Dual Neural Networks

TL;DR

This paper introduces an explainable dual neural network model that predicts equilibrium asset prices and liquidations in financial contagion scenarios, overcoming data limitations and capturing complex relationships without predefined analytical forms.

Contribution

The novel dual neural network architecture effectively models price-mediated contagion, accurately predicts asset prices and liquidations, and operates with limited observable data.

Findings

Accurately predicts equilibrium prices from initial shocks

Aligns well with true liquidations in simulated data

Works effectively without observable liquidation data

Abstract

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.