IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Login to save this paper or follow this series

A Hybrid Intelligent Early Warning System for Predicting Economic Crises: The Case of China

  • Su, Dongwei
  • He, Xingxing

This paper combines artificial neural networks (ANN), fuzzy optimization and time-series econometric models in one unified framework to form a hybrid intelligent early warning system (EWS) for predicting economic crises. Using quarterly data on 12 macroeconomic and financial variables for the Chinese economy during 1999 and 2008, the paper finds that the hybrid model possesses strong predictive power and the likelihood of economic crises in China during 2009 and 2010 remains high.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://mpra.ub.uni-muenchen.de/19962/1/MPRA_paper_19962.pdf
File Function: original version
Download Restriction: no

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 19962.

as
in new window

Length:
Date of creation: 11 Jan 2010
Date of revision:
Handle: RePEc:pra:mprapa:19962
Contact details of provider: Postal: Schackstr. 4, D-80539 Munich, Germany
Phone: +49-(0)89-2180-2219
Fax: +49-(0)89-2180-3900
Web page: http://mpra.ub.uni-muenchen.de

More information through EDIRC

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

as in new window
  1. Peng, Duan & Bajona, Claustre, 2008. "China's vulnerability to currency crisis: A KLR signals approach," China Economic Review, Elsevier, vol. 19(2), pages 138-151, June.
  2. Graciela L. Kaminsky & Carmen M. Reinhart, 1996. "The twin crises: the causes of banking and balance-of-payments problems," International Finance Discussion Papers 544, Board of Governors of the Federal Reserve System (U.S.).
  3. Bussiere, Matthieu & Fratzscher, Marcel, 2006. "Towards a new early warning system of financial crises," Journal of International Money and Finance, Elsevier, vol. 25(6), pages 953-973, October.
  4. Reinhart, Carmen & Goldstein, Morris & Kaminsky, Graciela, 2000. "Assessing financial vulnerability, an early warning system for emerging markets: Introduction," MPRA Paper 13629, University Library of Munich, Germany.
  5. Kaminsky, Graciela & Lizondo, Saul & Reinhart, Carmen M., 1997. "Leading indicators of currency crises," Policy Research Working Paper Series 1852, The World Bank.
  6. Sawischlewski, Katja & Menkhoff, Lukas & Beckmann, Daniela, 2005. "Robust Lessons about Practical Early Warning Systems," Proceedings of the German Development Economics Conference, Kiel 2005 3, Verein für Socialpolitik, Research Committee Development Economics.
  7. Frankel, Jeffrey A. & Rose, Andrew K., 1996. "Currency crashes in emerging markets: An empirical treatment," Journal of International Economics, Elsevier, vol. 41(3-4), pages 351-366, November.
  8. Morris Goldstein & Carmen M. Reinhart, 2000. "Assessing Financial Vulnerability: An Early Warning System for Emerging Markets," Peterson Institute Press: All Books, Peterson Institute for International Economics, number 100.
  9. W. R. M. Perraudin & Manmohan S. Kumar & Uma Moorthy, 2002. "Predicting Emerging Market Currency Crashes," IMF Working Papers 02/7, International Monetary Fund.
  10. Kalotychou, Elena & Staikouras, Sotiris K., 2006. "An empirical investigation of the loan concentration risk in Latin America," Journal of Multinational Financial Management, Elsevier, vol. 16(4), pages 363-384, October.
  11. KOMULAINEN Tuomas LUKKARILA Johanna, . "What Drives Financial Crises in Emerging Markets?," EcoMod2003 330700082, EcoMod.
  12. Berg, Andrew & Pattillo, Catherine, 1999. "Predicting currency crises:: The indicators approach and an alternative," Journal of International Money and Finance, Elsevier, vol. 18(4), pages 561-586, August.
  13. Alvarez-Plata, Patricia & Schrooten, Mechthild, 2004. "Misleading indicators? The Argentinean currency crisis," Journal of Policy Modeling, Elsevier, vol. 26(5), pages 587-603, July.
  14. Niemira, Michael P. & Saaty, Thomas L., 2004. "An Analytic Network Process model for financial-crisis forecasting," International Journal of Forecasting, Elsevier, vol. 20(4), pages 573-587.
  15. Lean Yu & Kin Keung Lai & Shou-Yang Wang, 2006. "Currency Crisis Forecasting With General Regression Neural Networks," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 5(03), pages 437-454.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:19962. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Ekkehart Schlicht)

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.