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

dc.contributor.author Mezei, Jozsef
dc.contributor.author Sarlin, Peter
dc.date.accessioned 2016-12-29T00:36:31Z
dc.date.available 2016-12-29T00:36:31Z
dc.date.issued 2017-01-04
dc.description.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.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2017.171
dc.identifier.isbn 978-0-9981331-0-2
dc.identifier.uri http://hdl.handle.net/10125/41324
dc.language.iso eng
dc.relation.ispartof Proceedings of the 50th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject possibilistic clustering
dc.subject typicality values
dc.subject systemic risk
dc.subject classification
dc.subject banking crisis
dc.title Possibilistic Clustering for Crisis Prediction: Systemic Risk States and Membership Degrees
dc.type Conference Paper
dc.type.dcmi Text
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