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Zastosowanie modeli zdarzen konkurujacych do badania ryzyka kredytowego

Author

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  • Ewa Wycinka

    (Uniwersytet Gdanski, Katedra Statystyki)

Abstract

Credit risk arises from the debtor’s possible failure to meet the terms and conditions of the credit contract. As a result, the bank does not receive a particular payment stipulated by the contractual provisions. Credit risk usually equates with the credit taker’s insolvency. Hu and Cheng (2015) note the shortage of studies devoted to other kinds of credit risks competing with the risk of default and their influence on the evaluation of the probability of default. In the article, a default and an early repayment are considered to be competing risks. Two approaches were used to research the intensity of competing risks: evaluation of cause-specific hazard and sub-distribution hazard respectively. The interpretation principles within the results acquired by the use of either method have been discussed. For either of the approaches, proper regression models have been set up, alongside conducting the sensitivity analysis. The results have been duly compared. The empirical study employed a sample of 5000 sixty-months’ credits granted by one of the Polish financial institutions. Application characteristics of the credit takers have been used in regression models as covariates.

Suggested Citation

  • Ewa Wycinka, 2017. "Zastosowanie modeli zdarzen konkurujacych do badania ryzyka kredytowego," Problemy Zarzadzania, University of Warsaw, Faculty of Management, vol. 15(66), pages 145-161.
    • Handle: RePEc:sgm:pzwzuw:v:15:i:66:y:2017:p:145-161
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    References listed on IDEAS

    as
    1. G Andreeva, 2006. "European generic scoring models using survival analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(10), pages 1180-1187, October.
    2. Thomas, Lyn C., 2000. "A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers," International Journal of Forecasting, Elsevier, vol. 16(2), pages 149-172.
    3. J Banasik & J N Crook & L C Thomas, 1999. "Not if but when will borrowers default," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(12), pages 1185-1190, December.
    4. Maria Stepanova & Lyn Thomas, 2002. "Survival Analysis Methods for Personal Loan Data," Operations Research, INFORMS, vol. 50(2), pages 277-289, April.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Fine-Gray models; sensitivity analysis of Cox models; probability of default; early repayments;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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