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Multiperiod default probability forecasting

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  • Oliver Blümke

Abstract

Accounting standards require that financial institutions must measure default risk with respect to the full maturity of a financial instrument. This requires forecasting of future default probabilities. The forecast of future default probabilities concerns two aspects: forecasting macroeconomic scenarios and future average (with respect to the macroeconomy) default probabilities. The present paper addresses the modeling of future average default probabilities. For corporations and depending on the initial rating grade, default probabilities change in different patterns over time. For initial investment‐grade ratings, the risk of a default increases, whereas for the bottom of the rating scale, default probabilities decrease. To model these different patterns, the paper proposes to extend the existing discrete‐time survival model and to incorporate an additional time‐ and covariate‐dependent shape parameter into the hazard function. Using data from Standard & Poor's, the paper shows that the shape parameter is able to reproduce the different patterns. The proposed model is benchmarked against a model that does not employ a shape parameter, and the results show that the additional shape parameter improves in‐sample and out‐of‐sample prediction.

Suggested Citation

  • Oliver Blümke, 2022. "Multiperiod default probability forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(4), pages 677-696, July.
  • Handle: RePEc:wly:jforec:v:41:y:2022:i:4:p:677-696
    DOI: 10.1002/for.2825
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    1. Koopman, Siem Jan & Kräussl, Roman & Lucas, André & Monteiro, André B., 2009. "Credit cycles and macro fundamentals," Journal of Empirical Finance, Elsevier, vol. 16(1), pages 42-54, January.
    2. Pesaran, M. Hashem & Schuermann, Til & Treutler, Bjorn-Jakob & Weiner, Scott M., 2006. "Macroeconomic Dynamics and Credit Risk: A Global Perspective," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(5), pages 1211-1261, August.
    3. Frydman, Halina & Schuermann, Til, 2008. "Credit rating dynamics and Markov mixture models," Journal of Banking & Finance, Elsevier, vol. 32(6), pages 1062-1075, June.
    4. John Y. Campbell & Jens Hilscher & Jan Szilagyi, 2008. "In Search of Distress Risk," Journal of Finance, American Finance Association, vol. 63(6), pages 2899-2939, December.
    5. Giesecke, Kay & Longstaff, Francis A. & Schaefer, Stephen & Strebulaev, Ilya, 2011. "Corporate bond default risk: A 150-year perspective," Journal of Financial Economics, Elsevier, vol. 102(2), pages 233-250.
    6. G. dos Reis & G. Smith, 2018. "Robust and consistent estimation of generators in credit risk," Quantitative Finance, Taylor & Francis Journals, vol. 18(6), pages 983-1001, June.
    7. M. Pfeuffer & L. Möstel & M. Fischer, 2019. "An extended likelihood framework for modelling discretely observed credit rating transitions," Quantitative Finance, Taylor & Francis Journals, vol. 19(1), pages 93-104, January.
    8. Duan, Jin-Chuan & Sun, Jie & Wang, Tao, 2012. "Multiperiod corporate default prediction—A forward intensity approach," Journal of Econometrics, Elsevier, vol. 170(1), pages 191-209.
    9. Djeundje, Viani Biatat & Crook, Jonathan, 2019. "Identifying hidden patterns in credit risk survival data using Generalised Additive Models," European Journal of Operational Research, Elsevier, vol. 277(1), pages 366-376.
    10. Bernd Schwaab & Siem Jan Koopman & André Lucas, 2017. "Global Credit Risk: World, Country and Industry Factors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(2), pages 296-317, March.
    11. Walter Orth, 2013. "Default probability estimation in small samples--with an application to sovereign bonds," Quantitative Finance, Taylor & Francis Journals, vol. 13(12), pages 1891-1902, December.
    12. Robert A. Jarrow & David Lando & Stuart M. Turnbull, 2008. "A Markov Model for the Term Structure of Credit Risk Spreads," World Scientific Book Chapters, in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 18, pages 411-453, World Scientific Publishing Co. Pte. Ltd..
    13. Tony Bellotti & Jonathan Crook, 2014. "Retail credit stress testing using a discrete hazard model with macroeconomic factors," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(3), pages 340-350, March.
    14. Mizen, Paul & Tsoukas, Serafeim, 2012. "Forecasting US bond default ratings allowing for previous and initial state dependence in an ordered probit model," International Journal of Forecasting, Elsevier, vol. 28(1), pages 273-287.
    15. Ruey-Ching Hwang & Chih-Kang Chu, 2013. "Forecasting forward defaults: a simple hazard model with competing risks," Quantitative Finance, Taylor & Francis Journals, vol. 14(8), pages 1467-1477, August.
    16. Lore Dirick & Gerda Claeskens & Bart Baesens, 2017. "Time to default in credit scoring using survival analysis: a benchmark study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(6), pages 652-665, June.
    17. Daniele De Leonardis & Roberto Rocci, 2008. "Assessing the default risk by means of a discrete‐time survival analysis approach," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 24(4), pages 291-306, July.
    18. Refik Soyer & Feng Xu, 2010. "Assessment of mortgage default risk via Bayesian reliability models," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 26(3), pages 308-330, May.
    19. Djeundje, Viani Biatat & Crook, Jonathan, 2019. "Dynamic survival models with varying coefficients for credit risks," European Journal of Operational Research, Elsevier, vol. 275(1), pages 319-333.
    20. Nicholas M. Kiefer, 2011. "Default estimation, correlated defaults, and expert information," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(2), pages 173-192, March.
    21. Jens Hilscher & Mungo Wilson, 2017. "Credit Ratings and Credit Risk: Is One Measure Enough?," Management Science, INFORMS, vol. 63(10), pages 3414-3437, October.
    22. Farnsworth, Heber & Li, Tao, 2007. "The Dynamics of Credit Spreads and Ratings Migrations," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 42(3), pages 595-620, September.
    23. S. Baena‐Mirabete & P. Puig, 2018. "Parsimonious higher order Markov models for rating transitions," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(1), pages 107-131, January.
    24. T Bellotti & J Crook, 2009. "Credit scoring with macroeconomic variables using survival analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(12), pages 1699-1707, December.
    25. Kiefer, Nicholas M., 2010. "Default Estimation and Expert Information," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(2), pages 320-328.
    26. Sanjiv R. Das & Darrell Duffie & Nikunj Kapadia & Leandro Saita, 2007. "Common Failings: How Corporate Defaults Are Correlated," Journal of Finance, American Finance Association, vol. 62(1), pages 93-117, February.
    27. Carling, Kenneth & Jacobson, Tor & Linde, Jesper & Roszbach, Kasper, 2007. "Corporate credit risk modeling and the macroeconomy," Journal of Banking & Finance, Elsevier, vol. 31(3), pages 845-868, March.
    28. Jess N. Cornaggia & Kimberly J. Cornaggia & John E. Hund, 2017. "Credit Ratings Across Asset Classes: A Long-Term Perspective," Review of Finance, European Finance Association, vol. 21(2), pages 465-509.
    29. G. dos Reis & M. Pfeuffer & G. Smith, 2020. "Capturing model risk and rating momentum in the estimation of probabilities of default and credit rating migrations," Quantitative Finance, Taylor & Francis Journals, vol. 20(7), pages 1069-1083, July.
    30. Marius Pfeuffer & Goncalo dos Reis & Greig smith, 2018. "Capturing Model Risk and Rating Momentum in the Estimation of Probabilities of Default and Credit Rating Migrations," Papers 1809.09889, arXiv.org, revised Feb 2020.
    31. Edwin J. Elton & Martin J. Gruber & Deepak Agrawal & Christopher Mann, 2001. "Explaining the Rate Spread on Corporate Bonds," Journal of Finance, American Finance Association, vol. 56(1), pages 247-277, February.
    32. Maria Stepanova & Lyn Thomas, 2002. "Survival Analysis Methods for Personal Loan Data," Operations Research, INFORMS, vol. 50(2), pages 277-289, April.
    33. Hanson, Samuel & Schuermann, Til, 2006. "Confidence intervals for probabilities of default," Journal of Banking & Finance, Elsevier, vol. 30(8), pages 2281-2301, August.
    34. Hannes Kazianka, 2016. "Objective Bayesian estimation of the probability of default," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(1), pages 1-27, January.
    35. Bellotti, Tony & Crook, Jonathan, 2013. "Forecasting and stress testing credit card default using dynamic models," International Journal of Forecasting, Elsevier, vol. 29(4), pages 563-574.
    36. Jonathan Crook & Tony Bellotti, 2010. "Time varying and dynamic models for default risk in consumer loans," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(2), pages 283-305, April.
    37. Tasche, Dirk, 2013. "Bayesian estimation of probabilities of default for low default portfolios," Journal of Risk Management in Financial Institutions, Henry Stewart Publications, vol. 6(3), pages 302-326, July.
    38. Figlewski, Stephen & Frydman, Halina & Liang, Weijian, 2012. "Modeling the effect of macroeconomic factors on corporate default and credit rating transitions," International Review of Economics & Finance, Elsevier, vol. 21(1), pages 87-105.
    39. Sudheer Chava & Robert A. Jarrow, 2008. "Bankruptcy Prediction with Industry Effects," World Scientific Book Chapters, in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 21, pages 517-549, World Scientific Publishing Co. Pte. Ltd..
    40. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, January.
    41. Koopman, Siem Jan & Lucas, André & Schwaab, Bernd, 2011. "Modeling frailty-correlated defaults using many macroeconomic covariates," Journal of Econometrics, Elsevier, vol. 162(2), pages 312-325, June.
    42. Traczynski, Jeffrey, 2017. "Firm Default Prediction: A Bayesian Model-Averaging Approach," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 52(3), pages 1211-1245, June.
    43. Lando, David & Skodeberg, Torben M., 2002. "Analyzing rating transitions and rating drift with continuous observations," Journal of Banking & Finance, Elsevier, vol. 26(2-3), pages 423-444, March.
    44. Luo, Sirong & Kong, Xiao & Nie, Tingting, 2016. "Spline based survival model for credit risk modeling," European Journal of Operational Research, Elsevier, vol. 253(3), pages 869-879.
    45. Christensen, Jens H.E. & Hansen, Ernst & Lando, David, 2004. "Confidence sets for continuous-time rating transition probabilities," Journal of Banking & Finance, Elsevier, vol. 28(11), pages 2575-2602, November.
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