Possibilistic Clustering for Crisis Prediction: Systemic Risk States and Membership Degrees

Date
2017-01-04
Authors
Mezei, Jozsef
Sarlin, Peter
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Abstract
Research on understanding and predicting systemic financial \ risk has been of increasing importance in the recent \ years. A common approach is to build predictive models \ based on macro-financial vulnerability indicators to \ identify systemic risk at an early stage. In this article, we \ outline an approach for identifying different systemic risk \ states through possibilistic fuzzy clustering. Instead of directly \ using a supervised classification method, we aim at \ identifying coherent groups of vulnerability with macrofinancial \ indicators for pre-crisis data, and determine the \ level of risk for a new observation based on its similarity \ to the identified groups. The approach allows for differentiating \ among different possible pre-crisis states, and \ using this information for estimating the possibility of systemic \ risk. In this work, we compare different fuzzy clustering \ methods, as well as conduct an empirical exercise \ for European systemic banking crises.
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Keywords
possibilistic clustering, typicality values, systemic risk, classification, banking crisis
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