Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/14675
Title: | Optimal design of early warning systems for sovereign debt crises |
Authors: | Fuertes, Ana Maria Kalotychou, Elena |
metadata.dc.contributor.other: | Καλοτύχου, Έλενα |
Major Field of Science: | Social Sciences |
Field Category: | Economics and Business |
Keywords: | Clustering;Loss function;Logit forecast;Forecast combining;Emerging markets;Default prediction;Country risk analysis |
Issue Date: | Jan-2007 |
Source: | International Journal of Forecasting, 2007, vol. 23, no. 1, pp. 85-100 |
Volume: | 23 |
Issue: | 1 |
Start page: | 85 |
End page: | 100 |
Journal: | International Journal of Forecasting |
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 naïve predictions. |
URI: | https://hdl.handle.net/20.500.14279/14675 |
ISSN: | 01692070 |
DOI: | 10.1016/j.ijforecast.2006.07.001 |
Rights: | © Elsevier |
Type: | Article |
Affiliation : | City University London |
Publication Type: | Peer Reviewed |
Appears in Collections: | Άρθρα/Articles |
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