IDEAS home Printed from https://ideas.repec.org/a/jfr/bmr111/v4y2015i3p32-42.html
   My bibliography  Save this article

Credit Risk Measurement Based on the Markov Chain

Author

Listed:
  • Hao Liu
  • Shijin Chen

Abstract

Credit migration matrices are often used in many credit risk and pricing application, and typically assumed to be generated by a simple Markov process. This paper is going to analyze the basic elements of credit risk research, and Maximum Likelihood estimation will be adopted to estimate the Mover-Stayer model¡¯s parameters in this paper. Furthermore, the recursive method will be used to compute the Maximum Likelihood estimator, and the numerical results can illustrate the strength of the Mover-Stayer model on credit risk analysis. We also use the hypotheses to prove that the Markov chain suit for the data against the hypotheses that the Mover-Stayer model more suitable for the data. Finally, we will make some comparisons according to the output of the program, and obtain some conclusions. The Mover-Stayer Model is more suitable against according the numbered result.

Suggested Citation

  • Hao Liu & Shijin Chen, 2015. "Credit Risk Measurement Based on the Markov Chain," Business and Management Research, Business and Management Research, Sciedu Press, vol. 4(3), pages 32-42, September.
  • Handle: RePEc:jfr:bmr111:v:4:y:2015:i:3:p:32-42
    as

    Download full text from publisher

    File URL: http://www.sciedupress.com/journal/index.php/bmr/article/download/7792/4646
    Download Restriction: no

    File URL: http://www.sciedupress.com/journal/index.php/bmr/article/view/7792
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lutz G. Arnold & John G. Riley, 2009. "On the Possibility of Credit Rationing in the Stiglitz-Weiss Model," American Economic Review, American Economic Association, vol. 99(5), pages 2012-2021, December.
    2. Linda Allen & Anthony Saunders, 2004. "Incorporating Systemic Influences Into Risk Measurements: A Survey of the Literature," Journal of Financial Services Research, Springer;Western Finance Association, vol. 26(2), pages 161-191, October.
    3. Tak-Kuen Siu & Wai-Ki Ching & S. Eric Fung & Michael Ng, 2005. "On a multivariate Markov chain model for credit risk measurement," Quantitative Finance, Taylor & Francis Journals, vol. 5(6), pages 543-556.
    4. Dean Fantazzini & Silvia Figini, 2009. "Random Survival Forests Models for SME Credit Risk Measurement," Methodology and Computing in Applied Probability, Springer, vol. 11(1), pages 29-45, March.
    Full references (including those not matched with items on IDEAS)

    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. Yu-Hsiu Lin & Len-Kuo Hu, 2015. "The cyclicality of bank regulation in a general economic framework," Applied Economics, Taylor & Francis Journals, vol. 47(53), pages 5791-5804, November.
    2. Sangcheol Song, 2014. "Subsidiary Divestment: The Role of Multinational Flexibility," Management International Review, Springer, vol. 54(1), pages 47-70, February.
    3. Hong Sun & Valentina Hartarska & Lezhu Zhang & Denis Nadolnyak, 2018. "The Influence of Social Capital on Farm Household’s Borrowing Behavior in Rural China," Sustainability, MDPI, vol. 10(12), pages 1-20, November.
    4. Gutiérrez López, Cristina & Abad González, Julio, 2014. "¿Permitían los estados financieros predecir los resultados de los tests de estrés de la banca española? Una aplicación del modelo logit," Revista de Contabilidad - Spanish Accounting Review, Elsevier, vol. 17(1), pages 58-70.
    5. Bieta, Volker & Broll, Udo & Siebe, Wilfried, 2014. "Collateral in banking policy: On the possibility of signaling," Mathematical Social Sciences, Elsevier, vol. 71(C), pages 137-141.
    6. Filipe, Sara Ferreira & Grammatikos, Theoharry & Michala, Dimitra, 2016. "Forecasting distress in European SME portfolios," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 112-135.
    7. Waters, George A., 2013. "Quantity rationing of credit and the Phillips curve," Journal of Macroeconomics, Elsevier, vol. 37(C), pages 68-80.
    8. Johannes Reeder & Stefanie Trepl, 2009. "Dependent Revenues, Capital Risk and Credit Rationing," Working Papers 078, Bavarian Graduate Program in Economics (BGPE).
    9. Richard Chamboko & Jorge M. Bravo, 2016. "On the modelling of prognosis from delinquency to normal performance on retail consumer loans," Risk Management, Palgrave Macmillan, vol. 18(4), pages 264-287, December.
    10. Kjenstad, Einar C. & Su, Xunhua & Zhang, Li, 2015. "Credit rationing by loan size: A synthesized model," The Quarterly Review of Economics and Finance, Elsevier, vol. 55(C), pages 20-27.
    11. David Veganzones, 2022. "Corporate failure prediction using threshold‐based models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 956-979, August.
    12. Silvia Figini & Ron Kenett & SILVIA SALINI, 2010. "Integrating Operational and Financial Risk Assessments," UNIMI - Research Papers in Economics, Business, and Statistics unimi-1099, Universitá degli Studi di Milano.
    13. Albrecht, Tobias & Rausch, Theresa Maria & Derra, Nicholas Daniel, 2021. "Call me maybe: Methods and practical implementation of artificial intelligence in call center arrivals’ forecasting," Journal of Business Research, Elsevier, vol. 123(C), pages 267-278.
    14. Aivazian, Varouj & Gu, Xinhua & Qiu, Jiaping & Huang, Bihong, 2015. "Loan collateral, corporate investment, and business cycle," Journal of Banking & Finance, Elsevier, vol. 55(C), pages 380-392.
    15. Yao-Zhi Xu & Jian-Lin Zhang & Ying Hua & Lin-Yue Wang, 2019. "Dynamic Credit Risk Evaluation Method for E-Commerce Sellers Based on a Hybrid Artificial Intelligence Model," Sustainability, MDPI, vol. 11(19), pages 1-17, October.
    16. Tang, Lingxiao & Cai, Fei & Ouyang, Yao, 2019. "Applying a nonparametric random forest algorithm to assess the credit risk of the energy industry in China," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 563-572.
    17. Mohammad Mahdi Mousavi & Jamal Ouenniche & Kaoru Tone, 2023. "A dynamic performance evaluation of distress prediction models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 756-784, July.
    18. Délio José Cordeiro Galvão & Helder Ferreira De Mendonça & Renato Falci Villela Loures, 2011. "Economic Activity And Financialinstitutional Risk: An Empirical Analysis For The Banking Industry," Anais do XXXVIII Encontro Nacional de Economia [Proceedings of the 38th Brazilian Economics Meeting] 088, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    19. de Wet, Albertus H. & van Eyden, Reneé & Gupta, Rangan, 2009. "Linking global economic dynamics to a South African-specific credit risk correlation model," Economic Modelling, Elsevier, vol. 26(5), pages 1000-1011, September.
    20. Carling, Kenneth & Rönnegård, Lars & Roszbach, Kasper, 2004. "Is Firm Interdependence within Industries Important for Portfolio Credit Risk?," Working Paper Series 168, Sveriges Riksbank (Central Bank of Sweden).

    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

    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:jfr:bmr111:v:4:y:2015:i:3:p:32-42. 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: Simon Lee (email available below). General contact details of provider: http://bmr.sciedupress.com .

    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.