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Multidimensional Distance‐To‐Collapse Point And Sovereign Default Prediction

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  • Roberto Savona
  • Marika Vezzoli

Abstract

By focusing on sovereign defaults, this paper introduces a multidimensional distance‐to‐collapse point based on a two‐step procedure. The first step is nonparametric and provides an early warning system that signals a potential crisis whenever preselected leading indicators exceed specific thresholds. The second is parametric and incorporates the first‐step country default predictors within a probit specification. Such a two‐step procedure generalizes the distance‐to‐default à la Merton within a multidimensional setting, wherein we care about the distance of each indicator from its threshold. Empirical evidence about debt crises of emerging markets over the period 1975–2002 proves that our methodology predicts 80% of the total defaults and non‐defaults in and out of sample. Copyright © 2012 John Wiley & Sons, Ltd.

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  • Roberto Savona & Marika Vezzoli, 2012. "Multidimensional Distance‐To‐Collapse Point And Sovereign Default Prediction," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 19(4), pages 205-228, October.
    • Handle: RePEc:wly:isacfm:v:19:y:2012:i:4:p:205-228
    DOI: 10.1002/isaf.1332
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    Cited by:

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    2. Casu, Barbara & Clare, Andrew & Saleh, Nashwa, 2011. "Towards a new model for early warning signals for systemic financial fragility and near crises: an application to OECD countries," MPRA Paper 37043, University Library of Munich, Germany.
    3. Rungporn Roengpitya & Pratabjai Nilla-or, 2012. "Challenges on the Validation of PD Models for Low Default Portfolios (LDPs) and Regulatory Policy Implications," Working Papers 2012-02, Monetary Policy Group, Bank of Thailand.
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    5. Salim Lahmiri & Stelios Bekiros & Anastasia Giakoumelou & Frank Bezzina, 2020. "Performance assessment of ensemble learning systems in financial data classification," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 27(1), pages 3-9, January.
    6. Savona, Roberto, 2014. "Hedge fund systemic risk signals," European Journal of Operational Research, Elsevier, vol. 236(1), pages 282-291.
    7. Silvia Figini & Roberto Savona & Marika Vezzoli, 2016. "Corporate Default Prediction Model Averaging: A Normative Linear Pooling Approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(1-2), pages 6-20, January.
    8. Rungporn Roengpitya, 2012. "Proposal of New Hybrid PD Estimation Models for the Low Default Portfolios (LDPs), Empirical Comparisons and Policy Implications," Working Papers 2012-03, Monetary Policy Group, Bank of Thailand.
    9. Salim Lahmiri, 2016. "Features selection, data mining and finacial risk classification: a comparative study," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(4), pages 265-275, October.
    10. Carmine Gabriele, 2019. "Learning from trees: A mixed approach to building early warning systems for systemic banking crises," Working Papers 40, European Stability Mechanism.

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