Monitoring Systemic Risk Based on Dynamic Thresholds

Abstract

Successful implementation of macroprudential policy is contingent on the ability to identify and estimate systemic risk in real time. In this paper,systemic risk is defined as the conditional probability of a systemic banking crisis and this conditional probability is modeled in a fixed effect binary response model framework. The model structure is dynamic and is designed for monitoring as the systemic risk forecasts only depend on data that are available in real time. Several risk factors are identified and it is hereby shown that the level of systemic risk contains a predictable component which varies through time. Furthermore, it is shown how the systemic risk forecasts map into crisis signals and how policy thresholds are derived in this framework. Finally, in an out-of-sample exercise, it is shown that the systemic risk estimates provided reliable early warning signals ahead of the recent financial crisis for several economies.

Keywords: Systemic Risk, Financial Stability, Macroprudential Policy, banking, systemic risk, banking crisis, systemic banking crisis, banking crises, banking sector, systemic banking crises, contagion, bank liabilities, crisis probability, bank runs, banking distress, probability model, deposit insurance, financial crisis, competitiveness, financial risk, banking sector fragility, credit boom, systemic banking distress, banking system distress, early warning systems, banking system, credit booms, bank default, financial crises, systemic risk, early warning system, bank distress, debt crisis, financial liberalization, financial contagion, systemic crisis, banking system stability, recession, economic

JEL Classification: E60, E69, E44