IDEAS home Printed from https://ideas.repec.org/a/rsr/supplm/v61y2013i3p73-80.html
   My bibliography  Save this article

Measurement of the Credit Risk

Author

Listed:
  • Danut CULETU

    („Andrei Saguna” University of Constanta)

  • Andreea Gabriela BALTAC

    („Artifex” University of Bucharest/Academy of Economic Studies Bucharest)

  • Alexandru URSACHE

    (Academy of Economic Studies Bucharest)

Abstract

Credit risk should, in general, be considered as a component of market risk, as explained in previous pages. However, the methods of analysis of this type of risk are more extensive than those used in the case of market risk just as a result of difficulties information may be obtained and the period of time as long as an investor (an individual, a company, the bank) must make reference. Loss of credit risk is usually calculated as the difference between the current value of the portfolio and its value at a given moment in the future.

Suggested Citation

  • Danut CULETU & Andreea Gabriela BALTAC & Alexandru URSACHE, 2013. "Measurement of the Credit Risk," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 61(3), pages 73-80, September.
  • Handle: RePEc:rsr:supplm:v:61:y:2013:i:3:p:73-80
    as

    Download full text from publisher

    File URL: http://www.revistadestatistica.ro/suplimente/2013/3_2013/srrs3_2013a09.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Treacy, William F. & Carey, Mark, 2000. "Credit risk rating systems at large US banks," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 167-201, January.
    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. Rosch, Daniel, 2005. "An empirical comparison of default risk forecasts from alternative credit rating philosophies," International Journal of Forecasting, Elsevier, vol. 21(1), pages 37-51.
    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. Giampaolo Gabbi & Andrea Sironi, 2005. "Which factors affect corporate bonds pricing? Empirical evidence from eurobonds primary market spreads," The European Journal of Finance, Taylor & Francis Journals, vol. 11(1), pages 59-74.
    7. Andrea Sironi, 2001. "An Analysis of European Banks' SND Issues and its Implications for the Design of a Mandatory Subordinated Debt Policy," Journal of Financial Services Research, Springer;Western Finance Association, vol. 20(2), pages 233-266, October.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. Hall, Stephen G. & Mitchell, James, 2007. "Combining density forecasts," International Journal of Forecasting, Elsevier, vol. 23(1), pages 1-13.
    15. 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.
    16. Francisco Peñaranda, 2004. "Are Vector Autoregressions an Accurate Model for Dynamic Asset Allocation?," Working Papers wp2004_0419, CEMFI.
    17. 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.
    18. Lazar, Emese & Zhang, Ning, 2019. "Model risk of expected shortfall," Journal of Banking & Finance, Elsevier, vol. 105(C), pages 74-93.
    19. 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.
    20. 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.

    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:rsr:supplm:v:61:y:2013:i:3:p:73-80. 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: Adrian Visoiu (email available below). General contact details of provider: https://edirc.repec.org/data/stagvro.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.