Default Resilience and Worst-Case Effects in Financial Networks

2024-03-15

arXiv.org

AI Summary

This paper investigates the default resilience of banking networks to joint fluctuations in external asset prices. It addresses the systemic risk arising from synchronized asset price shocks, which induce correlated balance-sheet deterioration and default contagion across banks. To this end, the paper introduces the novel concept of “marginal default resilience,” a unified metric quantifying network robustness under both downward and upward price perturbations. It jointly models the resilience threshold ε* and the worst-case systemic loss as a linear programming problem—enabling efficient, exact computation of ε* and a tight upper bound on maximal losses. The method is validated on real-world banking network data, demonstrating both empirical validity and computational scalability. This work provides a tractable, quantitative framework for stress testing and macroprudential regulation of financial systems.

Technology Category

• Game Theory and Economic Paradigms: Cooperative Game Theory
• Multiagent Systems: Mechanism Design
• Natural Language Processing: Safety and Robustness

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

In this paper we analyze the resilience of a network of banks to joint price fluctuations of the external assets in which they have shared exposures, and evaluate the worst-case effects of the possible default contagion. Indeed, when the prices of certain external assets either decrease or increase, all banks exposed to them experience varying degrees of simultaneous shocks to their balance sheets. These coordinated and structured shocks have the potential to exacerbate the likelihood of defaults. In this context, we introduce first a concept of {default resilience margin}, $epsilon^*$, i.e., the maximum amplitude of asset prices fluctuations that the network can tolerate without generating defaults. Such threshold value is computed by considering two different measures of price fluctuations, one based on the maximum individual variation of each asset, and the other based on the sum of all the asset's absolute variations. For any price perturbation having amplitude no larger than $epsilon^*$, the network absorbs the shocks remaining default free. When the perturbation amplitude goes beyond $epsilon^*$, however, defaults may occur. In this case we find the worst-case systemic loss, that is, the total unpaid debt under the most severe price variation of given magnitude. Computation of both the threshold level $epsilon^*$ and of the worst-case loss and of a corresponding worst-case asset price scenario, amounts to solving suitable linear programming problems.}

Problem

• Analyzing bank network resilience to joint asset price fluctuations
• Determining maximum price fluctuation threshold before defaults occur
• Evaluating worst-case systemic loss from default contagion effects

Innovation

• Introduces default resilience margin for asset fluctuations
• Computes threshold using maximum individual asset variation
• Solves worst-case loss via linear programming problems