Elements in the Design of an Early Warning System for Sovereign Default

Ana-Maria Fuertes and Elena Kalotychou

No 231, Computing in Economics and Finance 2004 from Society for Computational Economics

Abstract: This paper utilizes two different classification techniques to explore issues in the development of an early warning system for sovereign default. Specifically, the paper develops K-means clustering and logit models to illustrate how the optimal choice of parameters, such as assignment rule of fitted observations to binary groups depend on the decision-makers' preferences. It proposes optimization approaches to tailor these parameters to the decision-maker's loss-function and degree of risk-aversion towards unpredicted defaults. The paper also investigates the potential benefits of combining the optimal forecasts from three methods: logit based on objective macroeconomic variables, logit based on judgmental bankers' credit ratings and non-parametric K-means clustering using both objective and judgmental variables. Unlike continuous-variable forecasts, combining forecasts of discrete-variables requires different techniques based on logit regression or voting rules. In this context, the benefit from combination is not as clear-cut, since the expected loss is not directly related to the error variance. We find that the forecast combining approach can also be chosen optimally to account for the decision-makers' loss-function and risk-aversion

Keywords: Early warning systems; financial crises; classification techniques; forecast combination (search for similar items in EconPapers)
JEL-codes: C15 C22 C52 (search for similar items in EconPapers)
Date: 2004-08-11
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:sce:scecf4:231

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