IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1607.00448.html
   My bibliography  Save this paper

Estimation and prediction of credit risk based on rating transition systems

Author

Listed:
  • Jinghai Shao
  • Siming Li
  • Yong Li

Abstract

Risk management is an important practice in the banking industry. In this paper we develop a new methodology to estimate and predict the probability of default (PD) based on the rating transition matrices, which relates the rating transition matrices to the macroeconomic variables. Our method can overcome the shortcomings of the framework of Belkin et al. (1998), and is especially useful in predicting the PD and doing stress testing. Simulation is conducted at the end, which shows that our method can provide more accurate estimate than that obtained by the method of Belkin et al. (1998).

Suggested Citation

  • Jinghai Shao & Siming Li & Yong Li, 2016. "Estimation and prediction of credit risk based on rating transition systems," Papers 1607.00448, arXiv.org, revised Mar 2018.
    • Handle: RePEc:arx:papers:1607.00448
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1607.00448
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nickell, Pamela & Perraudin, William & Varotto, Simone, 2000. "Stability of rating transitions," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 203-227, January.
    2. 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..
    3. Bernd Engelmann & Robert Rauhmeier (ed.), 2011. "The Basel II Risk Parameters," Springer Books, Springer, number 978-3-642-16114-8, June.
    4. Wei, Jason Z., 2003. "A multi-factor, credit migration model for sovereign and corporate debts," Journal of International Money and Finance, Elsevier, vol. 22(5), pages 709-735, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Landini, S. & Uberti, M. & Casellina, S., 2019. "Credit risk migration rates modelling as open systems II: A simulation model and IFRS9-baseline principles," Structural Change and Economic Dynamics, Elsevier, vol. 50(C), pages 175-189.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Trueck, Stefan & Rachev, Svetlozar T., 2008. "Rating Based Modeling of Credit Risk," Elsevier Monographs, Elsevier, edition 1, number 9780123736833.
    2. Fuertes, Ana-Maria & Kalotychou, Elena, 2007. "On sovereign credit migration: A study of alternative estimators and rating dynamics," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3448-3469, April.
    3. Tomáš Vaněk & David Hampel, 2017. "The Probability of Default Under IFRS 9: Multi-period Estimation and Macroeconomic Forecast," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 65(2), pages 759-776.
    4. Kim, Yoonseong & Sohn, So Young, 2008. "Random effects model for credit rating transitions," European Journal of Operational Research, Elsevier, vol. 184(2), pages 561-573, January.
    5. Stefan Truck, 2008. "Forecasting credit migration matrices with business cycle effects—a model comparison," The European Journal of Finance, Taylor & Francis Journals, vol. 14(5), pages 359-379.
    6. Sumon Bhaumik & John S. Landon-Lane, 2007. "Directional Mobility of Ratings," William Davidson Institute Working Papers Series wp900, William Davidson Institute at the University of Michigan.
    7. Siem Jan Koopman & André Lucas & Pieter Klaassen, 2002. "Pro-Cyclicality, Empirical Credit Cycles, and Capital Buffer Formation," Tinbergen Institute Discussion Papers 02-107/2, Tinbergen Institute.
    8. 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.
    9. André Lucas & Siem Jan Koopman, 2005. "Business and default cycles for credit risk," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(2), pages 311-323.
    10. Valerio Vacca, 2011. "An unexpected crisis? Looking at pricing effectiveness of different banks," Temi di discussione (Economic working papers) 814, Bank of Italy, Economic Research and International Relations Area.
    11. Simonne Varotto, 2001. "Credit Risk Diversification," ICMA Centre Discussion Papers in Finance icma-dp2001-07, Henley Business School, University of Reading.
    12. Wozabal, David & Hochreiter, Ronald, 2012. "A coupled Markov chain approach to credit risk modeling," Journal of Economic Dynamics and Control, Elsevier, vol. 36(3), pages 403-415.
    13. Wei, Jason Z., 2003. "A multi-factor, credit migration model for sovereign and corporate debts," Journal of International Money and Finance, Elsevier, vol. 22(5), pages 709-735, October.
    14. Jeffrey R. Stokes, 2023. "A nonlinear inversion procedure for modeling the effects of economic factors on credit risk migration," Review of Quantitative Finance and Accounting, Springer, vol. 61(3), pages 855-878, October.
    15. Tamás Kristóf, 2021. "Sovereign Default Forecasting in the Era of the COVID-19 Crisis," JRFM, MDPI, vol. 14(10), pages 1-24, October.
    16. Areski Cousin & Jérôme Lelong & Tom Picard, 2023. "Rating transitions forecasting: a filtering approach," Post-Print hal-03347521, HAL.
    17. Xing, Haipeng & Sun, Ning & Chen, Ying, 2012. "Credit rating dynamics in the presence of unknown structural breaks," Journal of Banking & Finance, Elsevier, vol. 36(1), pages 78-89.
    18. Michael Kalkbrener & Natalie Packham, 2024. "A Markov approach to credit rating migration conditional on economic states," Papers 2403.14868, arXiv.org.
    19. 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.
    20. Areski Cousin & J'er^ome Lelong & Tom Picard, 2021. "Rating transitions forecasting: a filtering approach," Papers 2109.10567, arXiv.org, revised Jun 2023.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:1607.00448. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.