IDEAS home Printed from https://ideas.repec.org/a/pts/journl/y2015i3p60-65.html
   My bibliography  Save this article

Contingencies For Measurement Of The Credit Risk

Author

Listed:
  • Marinela BARBULESCU

    (University of Pitesti, Faculty of Economics, Romania)

  • Alina HAGIU

    (University of Pitesti, Faculty of Economics, Romania)

  • Cristina BALDAN

    (University of Pitesti, Faculty of Economics, Romania)

Abstract

The Global Financial Crisis, which affected various banks, some of them very important banks, highlighted the importance of an accurate credit risk measurement in order to be able to overcome it. There are a variety of such credit risk measurement models, so we can say that banks face a real dilemma when having to choose the most appropriate one. The aim of this paper is to examine the most popular methods used to measure the credit risk and to identify the strengths and the weaknesses of each one of it. The research was accomplished from a double perspective, in which the conceptual methodological approach is correlated to a variety of references to practical actions aiming the measurement and the prevention of credit risk. The study includes the presentation of the objectives of credit risk analysis, the most appropriate moments for doing such an analysis, the steps that have to be done in order to measure the credit risk, the errors that can overcome in the credit risk measurement system, generated by the misclassifications of the studied company, and the presentation of the specific information of financial creditors. The findings expressed in this paper were mainly the result of a qualitative analysis which showed that there is no best model for credit risk measurement, each one having both strengths and weaknesses, some providing a comprehensive analysis of the individual customer’s financial strength others allowing banks permanently monitor fluctuating default risk and identify the possibly problems at an early stage.

Suggested Citation

  • Marinela BARBULESCU & Alina HAGIU & Cristina BALDAN, 2015. "Contingencies For Measurement Of The Credit Risk," Scientific Bulletin - Economic Sciences, University of Pitesti, vol. 14(3), pages 60-65.
  • Handle: RePEc:pts:journl:y:2015:i:3:p:60-65
    as

    Download full text from publisher

    File URL: http://economic.upit.ro/repec/pdf/2015_3_7.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Franck Moraux & O. Renault, 2002. "30 ans de modèles structurels de risque de défaut," Post-Print halshs-00076643, HAL.
    2. Berkowitz, Jeremy, 2001. "Testing Density Forecasts, with Applications to Risk Management," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 465-474, October.
    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. Marinela BARBULESCU & Alina HAGIU, 2016. "Aspects Of The Financial Risk In The Romanian Economy Versus The French Economy - Comparative Perspective And Analysis," Scientific Bulletin - Economic Sciences, University of Pitesti, vol. 15(1), pages 69-76.
    2. Ivanova, Vesela & Puigvert Gutiérrez, Josep Maria, 2014. "Interest rate forecasts, state price densities and risk premium from Euribor options," Journal of Banking & Finance, Elsevier, vol. 48(C), pages 210-223.
    3. Li, Xiao-Ming & Rose, Lawrence C., 2009. "The tail risk of emerging stock markets," Emerging Markets Review, Elsevier, vol. 10(4), pages 242-256, December.
    4. Juan Carlos Escanciano & Zaichao Du, 2015. "Backtesting Expected Shortfall: Accounting for Tail Risk," CAEPR Working Papers 2015-001, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    5. M. Hashem Pesaran & Paolo Zaffaroni, 2004. "Model Averaging and Value-at-Risk Based Evaluation of Large Multi Asset Volatility Models for Risk Management," CESifo Working Paper Series 1358, CESifo.
    6. Mehmet Balcilar & Rangan Gupta & Stephen M. Miller, 2015. "The out-of-sample forecasting performance of nonlinear models of regional housing prices in the US," Applied Economics, Taylor & Francis Journals, vol. 47(22), pages 2259-2277, May.
    7. Markus Haas & Stefan Mittnik & Marc Paolella, 2006. "Modelling and predicting market risk with Laplace-Gaussian mixture distributions," Applied Financial Economics, Taylor & Francis Journals, vol. 16(15), pages 1145-1162.
    8. Monica Billio & Roberto Casarin & Francesco Ravazzolo & Herman K. van Dijk, 2010. "Combining predictive densities using Bayesian filtering with applications to US economics data," Working Paper 2010/29, Norges Bank.
    9. Miguel A. Ferreira, 2005. "Evaluating Interest Rate Covariance Models Within a Value-at-Risk Framework," Journal of Financial Econometrics, Oxford University Press, vol. 3(1), pages 126-168.
    10. Ricardo Crisóstomo, 2021. "Estimating real‐world probabilities: A forward‐looking behavioral framework," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(11), pages 1797-1823, November.
    11. Anne Sofie Jore & James Mitchell & Shaun P. Vahey, 2010. "Combining forecast densities from VARs with uncertain instabilities," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 621-634.
    12. Hall, Stephen G. & Mitchell, James, 2007. "Combining density forecasts," International Journal of Forecasting, Elsevier, vol. 23(1), pages 1-13.
    13. Borowska, Agnieszka & Hoogerheide, Lennart & Koopman, Siem Jan & van Dijk, Herman K., 2020. "Partially censored posterior for robust and efficient risk evaluation," Journal of Econometrics, Elsevier, vol. 217(2), pages 335-355.
    14. Francisco Peñaranda, 2004. "Are Vector Autoregressions an Accurate Model for Dynamic Asset Allocation?," Working Papers wp2004_0419, CEMFI.
    15. Knut Are Aastveit & Karsten R. Gerdrup & Anne Sofie Jore & Leif Anders Thorsrud, 2014. "Nowcasting GDP in Real Time: A Density Combination Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(1), pages 48-68, January.
    16. Lazar, Emese & Zhang, Ning, 2019. "Model risk of expected shortfall," Journal of Banking & Finance, Elsevier, vol. 105(C), pages 74-93.
    17. Knotek, Edward S. & Zaman, Saeed, 2023. "Real-time density nowcasts of US inflation: A model combination approach," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1736-1760.
    18. Giot, Pierre & Petitjean, Mikael, 2007. "The information content of the Bond-Equity Yield Ratio: Better than a random walk?," International Journal of Forecasting, Elsevier, vol. 23(2), pages 289-305.
    19. Knut Are Aastveit & Francesco Ravazzolo & Herman K. van Dijk, 2018. "Combined Density Nowcasting in an Uncertain Economic Environment," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(1), pages 131-145, January.
    20. Michael K. Adjemian & Valentina G. Bruno & Michel A. Robe, 2020. "Incorporating Uncertainty into USDA Commodity Price Forecasts," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(2), pages 696-712, March.

    More about this item

    Keywords

    Credit Risk; Global Financial Crisis; Risk Analysis; Bankruptcy; Measurement of a credit risk.;
    All these keywords.

    JEL classification:

    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

    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:pts:journl:y:2015:i:3:p:60-65. 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: Alina Hagiu (email available below). General contact details of provider: https://edirc.repec.org/data/fepitro.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.