Modeling Inverse Demand Function with Explainable Dual Neural Networks

ArXiv ID: 2307.14322 “View on arXiv”

Authors: Unknown

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.

Keywords: Financial Contagion, Price-Mediated Contagion, Neural Networks, Equilibrium Pricing, Stress Testing, General Financial Assets

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 4.0/10
• Quadrant: Lab Rats
• Why: The paper introduces a novel dual neural network architecture with specific architectural choices and an explainable AI framework, indicating moderate to high math complexity. However, the empirical evidence relies solely on simulated datasets without mention of real-world backtesting, statistical metrics on live data, or code release, placing it lower on empirical rigor.

  flowchart TD
    A["Research Goal:<br>Model Price-Mediated Contagion<br>without Observable Liquidations"] --> B["Methodology:<br>Dual Neural Network Architecture"]
    B --> C{"Input:<br>Initial Shocks"}
    C --> D["1st Neural Network:<br>Map Shocks to Predicted Liquidations"]
    D --> E["2nd Neural Network:<br>Map Liquidations to Equilibrium Prices"]
    E --> F["Key Findings:<br>1. Accurate Price Prediction<br>2. Captures Linear/Non-Linear Effects<br>3. Explainable Framework"]