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This paper develops a framework for assessing systemic risks and for predicting (out-of-sample) systemic events, i.e. periods of extreme financial instability with potential real costs. The authors test the ability of a wide range of "Stand alone" and composite indicators in predicting systemic events and evaluate them by taking into account policy makers' preferences between false alarms and missing signals. The results highlight the importance of considering jointly various indicators in a multivariate framework. They find that taking into account jointly domestic and global macro-financial vulnerabilities greatly improves the performance of discrete choice models in forecasting systemic events.
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