Default Resilience and Worst-Case Effects in Financial Networks

Paper: arXiv 2403.10631

Authors: Giuseppe Calafiore, Giulia Fracastoro, Anton Proskurnikov

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}, $ε^$, 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 $ε^$, the network absorbs the shocks remaining default free. When the perturbation amplitude goes beyond $ε^$, 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 $ε^$ and of the worst-case loss and of a corresponding worst-case asset price scenario, amounts to solving suitable linear programming problems.}

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 3.0/10
• Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: The paper presents a solid theoretical framework using linear programming to calculate default resilience margins and worst-case systemic losses in financial networks. However, it relies primarily on mathematical formulations and structural network theory rather than empirical backtesting or real-world dataset validation.

Research Flowchart

  flowchart TD
    A[Research Goal: Assess financial network resilience & worst-case defaults from asset price shocks] --> B[Inputs: Interbank network balance sheets & shared asset exposures]
    B --> C[Methodology: Model coordinated price fluctuations & default contagion]
    C --> D[Linear Programming Optimization]
    D --> E[Outcome 1: Calculate Default Resilience Margin ε*]
    D --> F[Outcome 2: Identify Worst-Case Asset Price Scenarios & Maximum Systemic Loss]

Cited References (117)

1. Systemic Risk and Stability in Financial Networks (2015)
2. Monitoring Indirect Contagion (2019)
3. Optimal financial crises (1998)
4. Financial Contagion (2000)
5. Asset commonality, debt maturity and systemic risk (2012)
6. Systemic bank runs without aggregate risk: How a misallocation of liquidity may trigger a solvency crisis (2024)
7. Mandatory disclosure and financial contagion (2015)
8. To fully net or not to net: Adverse effects of partial multilateral netting (2016)

0 of 117 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.