Stess-testing the system: Financial shock contagion in the realm of uncertainty

TL;DR

This paper enhances DebtRank with uncertainty intervals to better assess systemic financial risk, providing a more sensitive early-warning tool and policy instruments for monitoring and stress-testing interconnected banking networks.

Contribution

An augmented DebtRank algorithm incorporating uncertainty intervals for improved systemic risk assessment in incomplete data scenarios.

Findings

DebtRank captures increasing systemic risk more sensitively.

The method acts as an effective early-warning signal.

Policy tools for monitoring and stress-testing are proposed.

Abstract

This work proposes an augmented variant of DebtRank with uncertainty intervals as a method to investigate and assess systemic risk in financial networks, in a context of incomplete data. The algorithm is tested against a default contagion algorithm on three ensembles of networks with increasing density, estimated from real-world banking data related to the largest 227 EU15 financial institutions indexed in a stock market. Results suggest that DebtRank is capable of capturing increasing rates of systemic risk in a more sensitive and continuous way, thereby acting as an early-warning signal. The paper proposes three policy instruments based on this approach: the monitoring of systemic risk over time by applying the augmented DebtRank on time snapshots of interbank networks, a stress-testing framework able to test the systemic importance of financial institutions on different shock scenarios, and the evaluation of distribution of systemic losses in currency value.