## Monitoring Systemic Risk Basedon Dynamic Thresholds

_IMF Working Papers, June 1, 2012_

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## Bibliographic details
- Authors: Kasper Lund-Jensen
- Published: June 1, 2012
- Series: IMF Working Papers
- DOI: https://doi.org/10.5089/9781475504576.001

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### Summary
- Successful implementation of macroprudential policy depends on identifying and estimating systemic risk in real time.
- Systemic risk is defined as the conditional probability of a systemic banking crisis.
- The conditional probability is modeled in a fixed effect binary response model framework.
- The model structure is dynamic and designed for monitoring because systemic risk forecasts depend only on data available in real time.
- Several risk factors are identified and the level of systemic risk contains a predictable component that varies through time.
- The paper shows how systemic risk forecasts map into crisis signals and how policy thresholds are derived within this framework.
- An out-of-sample exercise shows the systemic risk estimates provided reliable early warning signals ahead of the recent financial crisis for several economies.

### Model and methodology
- Modeling approach: fixed effect binary response model framework.
- Dynamic design: forecasts rely exclusively on data available in real time (suitable for monitoring).
- Risk definition: systemic risk = conditional probability of a systemic banking crisis.
- Identified inputs/risk factors: (explicitly listed in the source keywords and subject headings) credit-to-GDP gap, credit-to-GDP growth, equity price growth, banking sector leverage, real effective exchange rates, foreign exchange, and other financial sector indicators.

### Key findings and evidence
- The level of systemic risk contains a predictable component which varies through time.
- Systemic risk forecasts can be converted into crisis signals.
- Policy thresholds for action can be derived within the presented framework.
- Out-of-sample results: systemic risk estimates provided reliable early warning signals ahead of the recent financial crisis for several economies.

### Policy implications and uses
- Macroprudential policy implementation should be supported by real-time systemic risk monitoring based on the proposed dynamic framework.
- Derived crisis signals and policy thresholds offer a structured way to translate risk forecasts into policy action.
- Identified risk factors (e.g., credit-to-GDP gap, credit growth, equity price growth, banking sector leverage, real effective exchange rates) can guide indicator selection for surveillance and policy calibration.

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