Optimal Design of an Early Warning Systems for Sovereign Debt Crises

International Journal of Forecasting 23, 85-100

Posted: 29 Dec 2004 Last revised: 11 Sep 2019

See all articles by Ana-Maria Fuertes

Ana-Maria Fuertes

Cass Business School, City University of London

Elena Kalotychou

Cass Business School, City, University of London

Date Written: December 10, 2004

Abstract

This paper tackles the design of an optimal early warning system (EWS) for sovereign default from two distinct angles: the choice of the econometric methodology and the evaluation of the EWS itself. It compares K-means clustering of macrodata, a logit regression for macrodata, a logit regression for credit ratings and the combined forecasts from all three methods. The optimal choice of forecast method is shown to depend on the desired trade-off between missed defaults and false alarms. Hence, it is crucial to account for the decision-maker's preferences which are characterized through a loss function and risk aversion parameter. Recursive forecast combining generally yields a better balance of Type I and Type II errors than any of the individual forecasting methods and outperforms the naive predictions.

Keywords: Debt crises, K-means clustering, logistic regression, bank internal ratings, loss function, forecast combination

JEL Classification: C15, C22, C52

Suggested Citation

Fuertes, Ana-Maria and Kalotychou, Elena, Optimal Design of an Early Warning Systems for Sovereign Debt Crises (December 10, 2004). International Journal of Forecasting 23, 85-100 , Available at SSRN: https://ssrn.com/abstract=634063

Ana-Maria Fuertes (Contact Author)

Cass Business School, City University of London ( email )

Faculty of Finance
106 Bunhill Row
London, EC1Y 8TZ
United Kingdom
+44 207 477 0186 (Phone)
+44 207 477 8881 (Fax)

HOME PAGE: http://www.city.ac.uk/people/academics/ana-maria-fuertes

Elena Kalotychou

Cass Business School, City, University of London ( email )

106 Bunhill Row
London, EC1Y 8TZ
Great Britain

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