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Possibilistic Clustering for Crisis Prediction: Systemic Risk States and Membership Degrees
|Title:||Possibilistic Clustering for Crisis Prediction: Systemic Risk States and Membership Degrees|
|Issue Date:||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.|
|Rights:||Attribution-NonCommercial-NoDerivatives 4.0 International|
|Appears in Collections:||Machine Learning and Network Analytics in Finance Minitrack|
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