IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i8p879-d537271.html
   My bibliography  Save this article

Markov Chain K-Means Cluster Models and Their Use for Companies’ Credit Quality and Default Probability Estimation

Author

Listed:
  • Nora Gavira-Durón

    (Department of Banking and Investments, School of Business and Economics, Universidad de las Americas Puebla, Puebla 72810, Mexico)

  • Octavio Gutierrez-Vargas

    (Higher School of Economics, Instituto Politécnico Nacional, Santo Tomas Campus, Ciudad de México 11350, Mexico)

  • Salvador Cruz-Aké

    (Higher School of Economics, Instituto Politécnico Nacional, Santo Tomas Campus, Ciudad de México 11350, Mexico)

Abstract

This research aims to determine the existence of inflection points when companies’ credit risk goes from being minimal (Hedge) to being high (Ponzi). We propose an analysis methodology that determines the probability of hedge credits to migrate to speculative and then to Ponzi, through simulations with homogeneous Markov chains and the k-means clustering method to determine thresholds and migration among clusters. To prove this, we used quarterly financial data from a sample of 35 public enterprises over the period between 1 July 2006 and 28 March 2020 (companies listed on the USA, Mexico, Brazil, and Chile stock markets). For simplicity, we make the assumption of no revolving credits for the companies and that they face their next payment only with their operating cash flow. We found that Ponzi companies (1) have a 0.79 probability average of default, while speculative ones had (0) 0.28, and hedge companies (−1) 0.009, which are the inflections point we were looking for. Our work’s main limitation lies in not considering the entities’ behavior when granting credits in altered states (credit relaxation due to credit supply excess).

Suggested Citation

  • Nora Gavira-Durón & Octavio Gutierrez-Vargas & Salvador Cruz-Aké, 2021. "Markov Chain K-Means Cluster Models and Their Use for Companies’ Credit Quality and Default Probability Estimation," Mathematics, MDPI, vol. 9(8), pages 1-14, April.
  • Handle: RePEc:gam:jmathe:v:9:y:2021:i:8:p:879-:d:537271
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/8/879/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/9/8/879/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Valerie R. Bencivenga & Bruce D. Smith, 1991. "Financial Intermediation and Endogenous Growth," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 195-209.
    2. N. Berger, Allen & F. Udell, Gregory, 1998. "The economics of small business finance: The roles of private equity and debt markets in the financial growth cycle," Journal of Banking & Finance, Elsevier, vol. 22(6-8), pages 613-673, August.
    3. Robert E. McCulloch & Ruey S. Tsay, 1994. "Statistical Analysis Of Economic Time Series Via Markov Switching Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 15(5), pages 523-539, September.
    4. M. Tudela & G. Young, 2005. "A Merton-Model Approach To Assessing The Default Risk Of Uk Public Companies," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 8(06), pages 737-761.
    5. Merton, Robert C, 1974. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates," Journal of Finance, American Finance Association, vol. 29(2), pages 449-470, May.
    6. Tobias Adrian & Hyun Song Shin, 2008. "Liquidity, monetary policy, and financial cycles," Current Issues in Economics and Finance, Federal Reserve Bank of New York, vol. 14(Jan).
    7. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    8. Tobias Adrian & Hyun Song Shin, 2008. "Liquidity and financial cycles," BIS Working Papers 256, Bank for International Settlements.
    9. Amir Ahmad Dar & N. Anuradha & Shahid Qadir, 2019. "Estimating probabilities of default of different firms and the statistical tests," Journal of Global Entrepreneurship Research, Springer;UNESCO Chair in Entrepreneurship, vol. 9(1), pages 1-15, December.
    10. Hyman P. Minsky, 1992. "The Financial Instability Hypothesis," Economics Working Paper Archive wp_74, Levy Economics Institute.
    11. Afik, Zvika & Arad, Ohad & Galil, Koresh, 2016. "Using Merton model for default prediction: An empirical assessment of selected alternatives," Journal of Empirical Finance, Elsevier, vol. 35(C), pages 43-67.
    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. Claudio Borio & Mathias Drehmann, 2011. "Toward an Operational Framework for Financial Stability: “Fuzzy” Measurement and Its Consequences," Central Banking, Analysis, and Economic Policies Book Series, in: Rodrigo Alfaro (ed.),Financial Stability, Monetary Policy, and Central Banking, edition 1, volume 15, chapter 4, pages 063-123, Central Bank of Chile.
    2. André, Christophe & Caraiani, Petre & Călin, Adrian Cantemir & Gupta, Rangan, 2022. "Can monetary policy lean against housing bubbles?," Economic Modelling, Elsevier, vol. 110(C).
    3. Justine Pedrono, 2015. "Banking Leverage with Currency Diversification," AMSE Working Papers 1539, Aix-Marseille School of Economics, France, revised Sep 2015.
    4. Moritz Schularick & Alan M. Taylor, 2012. "Credit Booms Gone Bust: Monetary Policy, Leverage Cycles, and Financial Crises, 1870-2008," American Economic Review, American Economic Association, vol. 102(2), pages 1029-1061, April.
    5. Zhang, Jinqing & He, Liang & An, Yunbi, 2020. "Measuring banks’ liquidity risk: An option-pricing approach," Journal of Banking & Finance, Elsevier, vol. 111(C).
    6. Juan Laborda & Sonia Ruano & Ignacio Zamanillo, 2023. "Multi-Country and Multi-Horizon GDP Forecasting Using Temporal Fusion Transformers," Mathematics, MDPI, vol. 11(12), pages 1-26, June.
    7. Ngene, Geoffrey M. & Tah, Kenneth A., 2023. "How are policy uncertainty, real economy, and financial sector connected?," Economic Modelling, Elsevier, vol. 123(C).
    8. Tobias Adrian & Hyun Song Shin, 2008. "Financial intermediary leverage and value at risk," Staff Reports 338, Federal Reserve Bank of New York.
    9. Justine Pedrono, 2015. "Banking Leverage Procyclicality: A Theoretical Model Introducing Currency Diversification," Working Papers halshs-01203758, HAL.
    10. Luca Riccetti & Alberto Russo & Mauro Gallegati, 2015. "An agent based decentralized matching macroeconomic model," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 10(2), pages 305-332, October.
    11. Daniel Oda, 2013. "Introducing Liquidity Risk in the Contingent-Claim Analysis for the Banks," Working Papers Central Bank of Chile 681, Central Bank of Chile.
    12. Rainer Masera, 2014. "CRR/CRD IV: the trees and the forest," PSL Quarterly Review, Economia civile, vol. 67(271), pages 381-422.
    13. Masaru Chiba, 2023. "Robust and efficient specification tests in Markov-switching autoregressive models," Statistical Inference for Stochastic Processes, Springer, vol. 26(1), pages 99-137, April.
    14. Galo Nuño & Carlos Thomas, 2017. "Bank Leverage Cycles," American Economic Journal: Macroeconomics, American Economic Association, vol. 9(2), pages 32-72, April.
    15. Penelope A. Smith & Peter M. Summers, 2005. "How well do Markov switching models describe actual business cycles? The case of synchronization," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(2), pages 253-274.
    16. Sylvia Frühwirth‐Schnatter & Sylvia Kaufmann, 2006. "How do changes in monetary policy affect bank lending? An analysis of Austrian bank data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(3), pages 275-305, April.
    17. Koresh Galil & Neta Gilat, 2019. "Predicting Default More Accurately: To Proxy or Not to Proxy for Default?," International Review of Finance, International Review of Finance Ltd., vol. 19(4), pages 731-758, December.
    18. Dong-Mei Zhu & Jiejun Lu & Wai-Ki Ching & Tak-Kuen Siu, 2019. "Option Pricing Under a Stochastic Interest Rate and Volatility Model with Hidden Markovian Regime-Switching," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 555-586, February.
    19. 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.
    20. Dagum, Estela Bee, 2010. "Business Cycles and Current Economic Analysis/Los ciclos económicos y el análisis económico actual," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 28, pages 577-594, Diciembre.

    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:gam:jmathe:v:9:y:2021:i:8:p:879-:d:537271. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.