A4 Conference proceedings

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

LUT Authors / Editors

Publication Details
Authors: Mezei Jozsef, Sarlin Peter
Publication year: 2017
Language: English
Title of parent publication: Proceedings of the 50th Hawaii International Conference on System Sciences (HICSS) 2017
Journal acronym: HICSS
Start page: 1
End page: 10
Number of pages: 10
ISBN: 978-0-9981331-0-2
ISSN: 1530-1605
JUFO-Level of this publication: 1
Open Access: Not an Open Access publication

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.

Last updated on 2018-19-10 at 07:55