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Forecasting US bond default ratings allowing for previous and initial state dependence in an ordered probit model

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  • Paul Mizen
  • Serafeim Tsoukas

Abstract

In this paper, we investigate the ability of a number of different ordered probit models to predict ratings based on firm-specific data on business and financial risks. We investigate models based on momentum, drift and ageing and compare them against alternatives that take into account the initial rating of the firm and its previous actual rating. Using data on US bond issuing firms rated by Fitch over the years 2000 to 2007 we compare the performance of these models in predicting the rating in-sample and out-of-sample using root mean squared errors, Diebold-Mariano tests of forecast performance and contingency tables. We conclude that initial and previous states have a substantial influence on rating prediction.

Suggested Citation

  • Paul Mizen & Serafeim Tsoukas, 2011. "Forecasting US bond default ratings allowing for previous and initial state dependence in an ordered probit model," Working Papers 2011_19, Business School - Economics, University of Glasgow.
  • Handle: RePEc:gla:glaewp:2011_19
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    1. Hans-Eggert Reimers, 2012. "Early Warning Indicator Model of Financial Developments Using an Ordered Logit," Business and Economic Research, Macrothink Institute, vol. 2(2), pages 171-191, December.
    2. Taneli Mäkinen & Fan Li & Andrea Mercatanti & Andrea Silvestrini, 2020. "Effects of eligibility for central bank purchases on corporate bond spreads," Temi di discussione (Economic working papers) 1300, Bank of Italy, Economic Research and International Relations Area.
    3. Anastasios Petropoulos & Vasilis Siakoulis & Evaggelos Stavroulakis & Aristotelis Klamargias, 2019. "A robust machine learning approach for credit risk analysis of large loan level datasets using deep learning and extreme gradient boosting," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Are post-crisis statistical initiatives completed?, volume 49, Bank for International Settlements.
    4. Makram El‐Shagi & Gregor von Schweinitz, 2022. "Why they keep missing: An empirical investigation of sovereign bond ratings and their timing," Scottish Journal of Political Economy, Scottish Economic Society, vol. 69(2), pages 186-224, May.
    5. A. R. Provenzano & D. Trifir`o & A. Datteo & L. Giada & N. Jean & A. Riciputi & G. Le Pera & M. Spadaccino & L. Massaron & C. Nordio, 2020. "Machine Learning approach for Credit Scoring," Papers 2008.01687, arXiv.org.
    6. Anastasios Petropoulos & Vasilis Siakoulis & Evaggelos Stavroulakis & Aristotelis Klamargias, 2019. "A robust machine learning approach for credit risk analysis of large loan-level datasets using deep learning and extreme gradient boosting," IFC Bulletins chapters, in: Bank for International Settlements (ed.), The use of big data analytics and artificial intelligence in central banking, volume 50, Bank for International Settlements.
    7. Sen Guo & Huiru Zhao & Chunjie Li & Haoran Zhao & Bingkang Li, 2016. "Significant Factors Influencing Rural Residents’ Well-Being with Regard to Electricity Consumption: An Empirical Analysis in China," Sustainability, MDPI, vol. 8(11), pages 1-13, November.
    8. Balios, Dimitris & Thomadakis, Stavros & Tsipouri, Lena, 2016. "Credit rating model development: An ordered analysis based on accounting data," Research in International Business and Finance, Elsevier, vol. 38(C), pages 122-136.
    9. Hantzsche, Arno, 2022. "Fiscal uncertainty and sovereign credit risk," European Economic Review, Elsevier, vol. 148(C).
    10. Fan Li & Andrea Mercatanti & Taneli Mäkinen & Andrea Silvestrini, 2019. "A regression discontinuity design for categorical ordered running variables with an application to central bank purchases of corporate bonds," Temi di discussione (Economic working papers) 1213, Bank of Italy, Economic Research and International Relations Area.
    11. Oliver Blümke, 2022. "Multiperiod default probability forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(4), pages 677-696, July.
    12. Mäkinen, Taneli & Li, Fan & Mercatanti, Andrea & Silvestrini, Andrea, 2022. "Causal analysis of central bank holdings of corporate bonds under interference," Economic Modelling, Elsevier, vol. 113(C).
    13. Sermpinis, Georgios & Tsoukas, Serafeim & Zhang, Ping, 2018. "Modelling market implied ratings using LASSO variable selection techniques," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 19-35.
    14. Irving Fisher Committee, 2019. "The use of big data analytics and artificial intelligence in central banking," IFC Bulletins, Bank for International Settlements, number 50, January.
    15. Doumpos, Michael & Niklis, Dimitrios & Zopounidis, Constantin & Andriosopoulos, Kostas, 2015. "Combining accounting data and a structural model for predicting credit ratings: Empirical evidence from European listed firms," Journal of Banking & Finance, Elsevier, vol. 50(C), pages 599-607.

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    More about this item

    Keywords

    Credit ratings; probit; state dependence;
    All these keywords.

    JEL classification:

    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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