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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.

Management --- Business & Economics --- Management Styles & Communication --- Financial risk management. --- Risk management. --- Insurance --- Risk management --- Banks and Banking --- Finance: General --- Macroeconomics --- Foreign Exchange --- General Financial Markets: Government Policy and Regulation --- Financial Crises --- Banks --- Depository Institutions --- Micro Finance Institutions --- Mortgages --- Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook: General --- Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook: Other --- Financial Markets and the Macroeconomy --- Finance --- Economic & financial crises & disasters --- Banking --- Currency --- Foreign exchange --- Systemic risk --- Systemic crises --- Commercial banks --- Systemic risk assessment --- Financial sector policy and analysis --- Financial crises --- Financial institutions --- Real effective exchange rates --- Financial risk management --- Banks and banking --- United States