Modeling and forecasting international credit risk : the case of sovereign loans
type: CreativeWork
description: This thesis investigates the relative merits of econometric modeling, statistical and judgmental techniques for predicting debt crises and assessing the risk of credit migration. The increased reliance on econometric or statistical approaches and credit rating systems in risk management has intensified the need for more rigorous analysis of their finite sample properties. A better understanding of the available tools has implications for credit risk management, regulation and policy decision-making. The thesis contributes to the extant sovereign risk literature in three areas. First, it addresses the question of whether controlling for unobserved heterogeneity is important for predicting debt crises and explores a pervasive inference problem in Early Warning Systems (EWSs). Second, it addresses the development of an `optimal' EWS for sovereign debt crises that accommodates the decision maker's preferences. Third, it considers the measurement of sovereign credit migration matrices using different estimators and explores non Markov effects in the rating dynamics. Chapter 2 confronts competing models of sovereign default that differ in how country-, region- and time-specific effects are treated. Statistical tests and information criteria overwhelmingly favour more complex models with country heterogeneity that possibly changes over time. However, simplicity beats complexity in terms of forecasting. Simple pooled logit parameterization, that control either for regional heterogeneity or for time effects produce the most accurate forecasts and outperform several naive predictors. Chapter 3 investigates the severity of the autocorrelation problem in EWS of sovereign default. This stems from seeking to provide crisis warnings over a horizon that is longer than the frequency at which the forecasts are updated and from the sluggishness of the typical exogenous indicators. Neglecting residual serial autocorrelation in such models is shown to be far from innocuous. Inferences a…
abstract: This thesis investigates the relative merits of econometric modeling, statistical and judgmental techniques for predicting debt crises and assessing the risk of credit migration. The increased reliance on econometric or statistical approaches and credit rating systems in risk management has intensified the need for more rigorous analysis of their finite sample properties. A better understanding of the available tools has implications for credit risk management, regulation and policy decision-making. The thesis contributes to the extant sovereign risk literature in three areas. First, it addresses the question of whether controlling for unobserved heterogeneity is important for predicting debt crises and explores a pervasive inference problem in Early Warning Systems (EWSs). Second, it addresses the development of an `optimal' EWS for sovereign debt crises that accommodates the decision maker's preferences. Third, it considers the measurement of sovereign credit migration matrices using different estimators and explores non Markov effects in the rating dynamics. Chapter 2 confronts competing models of sovereign default that differ in how country-, region- and time-specific effects are treated. Statistical tests and information criteria overwhelmingly favour more complex models with country heterogeneity that possibly changes over time. However, simplicity beats complexity in terms of forecasting. Simple pooled logit parameterization, that control either for regional heterogeneity or for time effects produce the most accurate forecasts and outperform several naive predictors. Chapter 3 investigates the severity of the autocorrelation problem in EWS of sovereign default. This stems from seeking to provide crisis warnings over a horizon that is longer than the frequency at which the forecasts are updated and from the sluggishness of the typical exogenous indicators. Neglecting residual serial autocorrelation in such models is shown to be far from innocuous. Inferences a…
author: Kalotychou, E.
keywords: HG Finance
date published: 2004
date modified: 2026-07-24T01:39:14Z
provider: City University of London
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: City University London