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Sovereign credit risk in a hidden Markov regime-switching framework. Part 1: Methodology

Potgeiter, L. & Fusai, G. (2013). Sovereign credit risk in a hidden Markov regime-switching framework. Part 1: Methodology. Journal of Financial Transformation, 37, pp. 99-209.


Standard approaches to estimating credit default probability estimation have certain drawbacks, most importantly regarding the underestimation of the true default probability which remains an undesirable property in sovereign risk management. As an alternative, this research applies a discrete-time Markov-modulated model to default probability estimation and applies it to Merton’s contingent claims approach, offering an attractive combination of possibly resolving the underestimation inherent in most standard structural models with a more conservative approach when predicting valuable information from a sovereign’s economic balance sheet. The crucial advantage of the estimation is that it backs the hypothesis that a regime-switching framework that allows for structural shifts can substantially improve default risk estimators, and proves that the methodology can be tractably extended to a contingent claims approach. Moreover, there are likely practical situations with certain policy implications when the predictions of the model could be used to detect systematic sovereign risk.

Publication Type: Article
Additional Information: © 2013 The Capital Markets Company, N.V. All rights reserved. This journal may not be duplicated in any way without the express written consent of the publisher except in the form of brief excerpts or quotations for review purposes. Making copies of this journal or any portion there of for any purpose other than your own is a violation of copyright law. Permission to add this article to City Research Online has been granted by the publisher.
Publisher Keywords: G21 G32; sovereign credit risk; credit risk; Markov model; Markov framework; credit default probability; regime-switching; sovereign risk
Subjects: H Social Sciences > HG Finance
Departments: Bayes Business School > Finance
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