Systemic Risk Surveillance

TL;DR

This paper introduces online surveillance schemes for systemic risk in financial markets, enabling real-time detection of forecast misspecifications and early signs of trouble to help prevent financial crises.

Contribution

It proposes novel online monitoring procedures for multiple systemic risk series, ensuring controlled false rejection rates and demonstrating effectiveness through simulations and empirical US bank data.

Findings

Procedures maintain size control during monitoring.

Simulations show good finite-sample performance.

Empirical application confirms practical usefulness.

Abstract

Following several episodes of financial market turmoil in recent decades, changes in systemic risk have drawn growing attention. Therefore, we propose surveillance schemes for systemic risk, which allow to detect misspecified systemic risk forecasts in an "online" fashion. This enables daily monitoring of the forecasts while controlling for the accumulation of false test rejections. Such online schemes are vital in taking timely countermeasures to avoid financial distress. Our monitoring procedures allow multiple series at once to be monitored, thus increasing the likelihood and the speed at which early signs of trouble may be picked up. The tests hold size by construction, such that the null of correct systemic risk assessments is only rejected during the monitoring period with (at most) a pre-specified probability. Monte Carlo simulations illustrate the good finite-sample properties of our procedures. An empirical application to US banks during multiple crises demonstrates the usefulness of our surveillance schemes for both regulators and financial institutions.