Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/41324

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

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Title:Possibilistic Clustering for Crisis Prediction: Systemic Risk States and Membership Degrees
Authors:Mezei, Jozsef
Sarlin, Peter
Keywords:possibilistic clustering
typicality values
systemic risk
classification
banking crisis
Date Issued:04 Jan 2017
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.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/41324
ISBN:978-0-9981331-0-2
DOI:10.24251/HICSS.2017.171
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Machine Learning and Network Analytics in Finance Minitrack


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