Quantitative, Data-driven Network Model for Global Cascading Financial Failure

TL;DR

This paper introduces a fast, quantitative network model to estimate and analyze the propagation of financial failures globally, aiding policy decisions and risk assessments of catastrophic economic events.

Contribution

It presents a simple, single-parameter, data-driven model for global financial cascading failures, validated against historical crises and used for hypothetical scenario analysis.

Findings

Model accurately fits the Great Recession data

Successfully tests against historical examples

Provides predictions for hypothetical conflicts

Abstract

Global catastrophic risk events, such as nuclear war, pose a severe threat to the stability of international financial systems. As evidenced by even less severe scenarios like the Great Recession, an economic failure can propagate through the world trade network, wreaking havoc on the global economy. While the contemporary literature on cascading failure models addresses this issue qualitatively, a simple and intuitive quantitative estimation that could be used in integrated assessment frameworks is missing. In this study, we introduce a quantitative network model of global financial cascading failure. Our proposal is a fast, efficient, single free parameter model, following a straightforward logic of propagating failures. We fit the model to the Great Recession and test it against historical examples and commercial analysis. We also provide predictions for a hypothetical armed conflict between India and Pakistan. Our aim is to introduce a quantitative approach that could inform policy decisions by contextualising global catastrophic scenarios regarding financial losses and assessing the effectiveness of resilience strategies, complementing existing models and frameworks for broader risk assessment.