IDEAS home Printed from https://ideas.repec.org/p/tcb/econot/2017.html
   My bibliography  Save this paper

Alternative Approaches for Modelling Corporate Sector Credit Risk

Author

Listed:
  • Gulcan Yildirim Gungor
  • Tuba Pelin Sumer

Abstract

[EN] This note aims to estimate credit riskiness of the corporate sector in Turkey with alternative methods for January 2007 -March 2019 period. Initially, probability of default is calculated by option pricing method for the listed companies and the relationship with non-performing loan (NPL) ratio is examined. In the one-year period following the increase (decrease) in the probability of default, a similar upward (downward) movement is observed in the corporate NPL ratio of the banking sector. Since the option pricing method focuses on relatively large scale companies listed on the stock exchange, credit riskiness is also calculated using NPL additions and commercial loan interest rates to increase the comprehensiveness of the study and include financials of the relatively small scale firms (SMEs). Although the sample size and assumptions differ, credit risk indicators estimated by alternative methods move together.Therefore, the credit riskiness indicators estimated with high frequency market data is important for monitoring the financial fragilities of corporate sector and their reflections on asset quality of the banking sector. [TR] Bu calismada, Turkiye’de faaliyet gosteren reel sektor firmalarinin kredi riskliligi alternatif yontemlerle Ocak 2007-Mart 2019 donemi icin tahmin edilmektedir. Oncelikle opsiyon fiyatlama yontemiyle borsaya kote firmalar icin temerrut olasiligi hesaplanmakta ve firma kredisi tahsili gecikmis alacak (TGA) oraniyla arasindaki iliski incelenmektedir. Analiz sonuclarina gore reel sektorun temerrut olasiligindaki artisi (azalisi) izleyen bir yillik surecte bankacilik sektoru TGA oraninda da benzer bir yukari (asagi) yonlu hareket oldugu gorulmektedir. Opsiyon fiyatlama yonteminde borsaya kote gorece buyuk olcekli firmalara odaklanildigi icin, temsil kuvvetini arttirmak ve nispeten kucuk olcekli firmalarin finansal gelismelerini de analize dahil etmek amaciyla kredi riskliligi, TGA ilaveleri ve ticari kredi faiz oranlari kullanilarak da hesaplanmaktadir. Kapsanan orneklem ve varsayimlar farkli olsa da alternatif yontemlerle hesaplanan kredi riski gostergelerinin beraber hareket ettigi gorulmektedir. Dolayisiyla, yuksek frekanstaki piyasa verileri kullanilarak hesaplanan kredi riskliligi gostergelerinin, gecikmeli finansal tabloveri akisina sahip reel kesim firmalarinin finansal kirilganliklarinin izlenmesi ve bankacilik sektoru aktif kalitesine yansimasi icin onemli bir gosterge oldugu degerlendirilmektedir.

Suggested Citation

  • Gulcan Yildirim Gungor & Tuba Pelin Sumer, 2020. "Alternative Approaches for Modelling Corporate Sector Credit Risk," CBT Research Notes in Economics 2017, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    • Handle: RePEc:tcb:econot:2017
    as

    Download full text from publisher

    File URL: https://www.tcmb.gov.tr/wps/wcm/connect/fd5c80d1-886b-48b9-8b25-9cba0de968d0/en2017eng.pdf?MOD=AJPERES&CACHEID=ROOTWORKSPACE-fd5c80d1-886b-48b9-8b25-9cba0de968d0-nnADzYG
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Arnaud Jobert & Ms. Janet Kong & Mr. Jorge A Chan-Lau, 2004. "An Option-Based Approach to Bank Vulnerabilities in Emerging Markets," IMF Working Papers 2004/033, International Monetary Fund.
    2. 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.
    3. Mr. Marcos R Souto & Mr. Rodolphe Blavy, 2009. "Estimating Default Frequencies and Macrofinancial Linkages in the Mexican Banking Sector," IMF Working Papers 2009/109, International Monetary Fund.
    4. Münür Yayla & Alper Hekimoglu & Mahmut Kutlukaya, 2008. "Financial Stability of the Turkish Banking Sector," Journal of BRSA Banking and Financial Markets, Banking Regulation and Supervision Agency, vol. 2(1), pages 9-26.
    5. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    6. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    7. repec:bla:jfinan:v:59:y:2004:i:2:p:831-868 is not listed on IDEAS
    8. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    9. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    10. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    11. Zmijewski, Me, 1984. "Methodological Issues Related To The Estimation Of Financial Distress Prediction Models," Journal of Accounting Research, Wiley Blackwell, vol. 22, pages 59-82.
    12. Mr. Jorge A Chan-Lau, 2006. "Market-Based Estimation of Default Probabilities and its Application to Financial Market Surveillance," IMF Working Papers 2006/104, International Monetary Fund.
    13. Canbas, Serpil & Cabuk, Altan & Kilic, Suleyman Bilgin, 2005. "Prediction of commercial bank failure via multivariate statistical analysis of financial structures: The Turkish case," European Journal of Operational Research, Elsevier, vol. 166(2), pages 528-546, October.
    14. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    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. Ahsan Habib & Mabel D' Costa & Hedy Jiaying Huang & Md. Borhan Uddin Bhuiyan & Li Sun, 2020. "Determinants and consequences of financial distress: review of the empirical literature," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(S1), pages 1023-1075, April.
    2. 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.
    3. Duc Hong Vo & Binh Ninh Vo Pham & Chi Minh Ho & Michael McAleer, 2019. "Corporate Financial Distress of Industry Level Listings in Vietnam," JRFM, MDPI, vol. 12(4), pages 1-17, September.
    4. Vo, D.H. & Pham, B.V.-N. & Pham, T.V.-T. & McAleer, M.J., 2019. "Corporate Financial Distress of Industry Level Listings in an Emerging Market," Econometric Institute Research Papers EI2019-15, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    5. B Korcan Ak & Patricia M Dechow & Yuan Sun & Annika Yu Wang, 2013. "The use of financial ratio models to help investors predict and interpret significant corporate events," Australian Journal of Management, Australian School of Business, vol. 38(3), pages 553-598, December.
    6. Nawaf Almaskati & Ron Bird & Yue Lu & Danny Leung, 2019. "The Role of Corporate Governance and Estimation Methods in Predicting Bankruptcy," Working Papers in Economics 19/16, University of Waikato.
    7. Jackson, Richard H.G. & Wood, Anthony, 2013. "The performance of insolvency prediction and credit risk models in the UK: A comparative study," The British Accounting Review, Elsevier, vol. 45(3), pages 183-202.
    8. Bhanu Pratap Singh & Alok Kumar Mishra, 2016. "Re-estimation and comparisons of alternative accounting based bankruptcy prediction models for Indian companies," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 2(1), pages 1-28, December.
    9. Trueck, Stefan & Rachev, Svetlozar T., 2008. "Rating Based Modeling of Credit Risk," Elsevier Monographs, Elsevier, edition 1, number 9780123736833.
    10. Serrano-Cinca, Carlos & Gutiérrez-Nieto, Begoña & Bernate-Valbuena, Martha, 2019. "The use of accounting anomalies indicators to predict business failure," European Management Journal, Elsevier, vol. 37(3), pages 353-375.
    11. Marielle de Jong & Lauren Stagnol, 2016. "A fundamental bond index including solvency criteria," Journal of Asset Management, Palgrave Macmillan, vol. 17(4), pages 280-294, July.
    12. Zhang, Xuan & Ouyang, Ruolan & Liu, Ding & Xu, Liao, 2020. "Determinants of corporate default risk in China: The role of financial constraints," Economic Modelling, Elsevier, vol. 92(C), pages 87-98.
    13. Catherine Refait, 2000. "Estimation du risque de défaut par une modélisation stochastique du bilan : Application à des firmes industrielles françaises," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-03718527, HAL.
    14. Adler Haymans Manurung & Derwin Suhartono & Benny Hutahayan & Noptovius Halimawan, 2023. "Probability Bankruptcy Using Support Vector Regression Machines," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 13(1), pages 1-3.
    15. Nguyen, Ha, 2023. "An empirical application of Particle Markov Chain Monte Carlo to frailty correlated default models," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 103-121.
    16. Frieda Rikkers & Andre E. Thibeault, 2009. "A Structural form Default Prediction Model for SMEs, Evidence from the Dutch Market," Multinational Finance Journal, Multinational Finance Journal, vol. 13(3-4), pages 229-264, September.
    17. Bai, Qing & Tian, Shaonan, 2020. "Innovate or die: Corporate innovation and bankruptcy forecasts," Journal of Empirical Finance, Elsevier, vol. 59(C), pages 88-108.
    18. Antonio Trujillo-Ponce & Reyes Samaniego-Medina & Clara Cardone-Riportella, 2014. "Examining what best explains corporate credit risk: accounting-based versus market-based models," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 15(2), pages 253-276, April.
    19. Zhou Lu & Zhuyao Zhuo, 2021. "Modelling of Chinese corporate bond default – A machine learning approach," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(5), pages 6147-6191, December.
    20. Bauer, Julian & Agarwal, Vineet, 2014. "Are hazard models superior to traditional bankruptcy prediction approaches? A comprehensive test," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 432-442.

    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:tcb:econot:2017. 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: the person in charge or the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/tcmgvtr.html .

    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.