Timothy Bianco

SAFE: An Early Warning System for Systemic Banking Risk

This paper builds on existing microprudential and macroprudential early warning systems (EWSs) to develop a new, hybrid class of models for systemic risk that incorporates the structural characteristics of the financial system and a feedback amplification mechanism. The models explain financial stress using both public and proprietary supervisory data from systemically important institutions, regressing institutional imbalances using an optimal lag method. The Systemic Assessment of Financial Environment (SAFE) EWS monitors microprudential information from the largest bank holding companies to anticipate the buildup of macroeconomic stresses in the financial markets. To mitigate inherent uncertainty, SAFE develops a set of medium-term forecasting specifications that gives policymakers enough time to take ex-ante policy action and a set of short-term forecasting specifications for verification and adjustment of supervisory actions. This paper highlights the application of these models to stress testing and policy.

Systemic Risk Early Warning System: A Micro-Macro Prudential Synthesis

From the financial supervisor’s point of view, an early warning system involves an ex ante approach to regulation, targeted to predict and prevent crises. An efficient EWS allows timely ex ante policy action and can reduce the need for ex post regulation. This chapter builds on existing microprudential and macroprudential early warning systems (EWSs) to propose a hybrid class of models for systemic risk, incorporating the structural characteristics of the financial system and a feedback amplification mechanism. The models explain financial stress using data from the five largest bank holding companies, regressing institutional imbalances using an optimal lag method. The z-scores of institutional data are justified as explanatory imbalances. The models utilize both public and proprietary supervisory data. The Systemic Assessment of Financial Environment (SAFE) EWS monitors microprudential information from systemically important institutions to anticipate the buildup of macroeconomic stresses in the financial markets at large. To the supervisor, SAFE offers a toolkit of possible institutional actions that can be used to diffuse the buildup of systemic stress in the financial markets. A hazard inherent in all ex ante models is that the model’s uncertainty may lead to wrong policy choices. To mitigate this risk, SAFE develops two modeling perspectives: a set of medium-term (six-quarter) forecasting specifications that gives policymakers enough time to take ex ante policy action, and a set of short-term (two-quarter) forecasting specifications for verification and adjustment of supervisory actions. Individual financial institutions may utilize the public version of SAFE EWS to enhance systemic risk stress testing and scenario analysis. This chapter shows the econometric results and robustness support for the SAFE set of models. The discussion of results addresses the usability and usefulness tests of supervisory data. In addition, the chapter investigates and suggests which action thresholds are appropriate for this EWS.

The Cleveland Financial Stress Index

To promote stability in a dynamic fi nancial system, supervisors must monitor the system for risks at all times. The Cleveland Fed has developed an index of fi nancial stress, the CFSI, which is designed to track distress in the fi nancial system as it is building. The CFSI will help financial system supervisors monitor and understand the state of fi nancial markets on a real-time basis, and take appropriate regulatory or supervisory action as necessary.