IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/80641.html
   My bibliography  Save this paper

Point-in-Time PD Term Structure Models with Loan Credit Quality as a Component

Author

Listed:
  • Yang, Bill Huajian

Abstract

Most point-in-time PD term structure models used in industry for stress testing and IFRS9 expected loss estimation apply only to macroeconomic scenarios. Loan level credit quality is not a factor in these models. In practice, credit profile at assessment time plays an important role in the performance of the loan during its lifetime. A forward-looking point-in-time PD term structure model with loan credit quality as a component is widely expected. In this paper, we propose a forward-looking point-in-time PD term structure model based on forward survival probability, extending the model proposed in [8] by including a loan specific credit quality score as a component. The model can be derived under the Merton model framework. Under this model, the forward survival probability for a forward term is driven by a loan credit quality score in addition to macroeconomic factors. Empirical results show, the inclusion of the loan specific credit score can significantly improve the performance of the model. The proposed approaches provide a tool for modeling point-in-time PD term structure in cases where loan credit profile is essential. The model can be implemented easily by using, for example, the SAS procedure PROC NLMIXED.

Suggested Citation

  • Yang, Bill Huajian, 2017. "Point-in-Time PD Term Structure Models with Loan Credit Quality as a Component," MPRA Paper 80641, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:80641
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/80641/1/MPRA_paper_80641.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Rosen, Dan & Saunders, David, 2009. "Analytical methods for hedging systematic credit risk with linear factor portfolios," Journal of Economic Dynamics and Control, Elsevier, vol. 33(1), pages 37-52, 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. Yang, Bill Huajian, 2014. "Modeling Systematic Risk and Point-in-Time Probability of Default under the Vasicek Asymptotic Single Risk Factor Model Framework," MPRA Paper 59025, University Library of Munich, Germany.
    2. Yang, Bill Huajian & Du, Zunwei, 2016. "Rating Transition Probability Models and CCAR Stress Testing: Methodologies and implementations," MPRA Paper 76270, University Library of Munich, Germany.
    3. Yang, Bill Huajian, 2013. "Estimating Long-Run PD, Asset Correlation, and Portfolio Level PD by Vasicek Models," MPRA Paper 57244, University Library of Munich, Germany.
    4. Yang, Bill Huajian & Du, Zunwei, 2015. "Stress Testing and Modeling of Rating Migration under the Vasicek Model Framework - Empirical approaches and technical implementation," MPRA Paper 65168, University Library of Munich, Germany.
    5. Yang, Bill Huajian, 2017. "Forward Ordinal Probability Models for Point-in-Time Probability of Default Term Structure," MPRA Paper 79934, University Library of Munich, Germany.
    6. Yang, Bill Huajian, 2017. "Point-in-time PD term structure models for multi-period scenario loss projection: Methodologies and implementations for IFRS 9 ECL and CCAR stress testing," MPRA Paper 76271, University Library of Munich, Germany.
    7. Yang, Bill Huajian & Wu, Biao & Cui, Kaijie & Du, Zunwei & Fei, Glenn, 2019. "IFRS9 Expected Credit Loss Estimation: Advanced Models for Estimating Portfolio Loss and Weighting Scenario Losses," MPRA Paper 93634, University Library of Munich, Germany.
    8. Yang, Bill Huajian, 2019. "Monotonic Estimation for the Survival Probability over a Risk-Rated Portfolio by Discrete-Time Hazard Rate Models," MPRA Paper 93398, University Library of Munich, Germany.
    9. Gady Jacoby & Chuan Liao & Jonathan A. Batten, 2007. "A Pure Test for the Elasticity of Yield Spreads," The Institute for International Integration Studies Discussion Paper Series iiisdp195, IIIS.
    10. Gerardo Manzo & Antonio Picca, 2020. "The Impact of Sovereign Shocks," Management Science, INFORMS, vol. 66(7), pages 3113-3132, July.
    11. Neus, Werner, 2014. "Eigenkapitalnormen, Boni und Risikoanreize in Banken," Die Unternehmung - Swiss Journal of Business Research and Practice, Nomos Verlagsgesellschaft mbH & Co. KG, vol. 68(2), pages 92-107.
    12. Giordani, Paolo & Jacobson, Tor & Schedvin, Erik von & Villani, Mattias, 2014. "Taking the Twists into Account: Predicting Firm Bankruptcy Risk with Splines of Financial Ratios," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 49(4), pages 1071-1099, August.
    13. Lily Y. Liu, 2017. "Estimating Loss Given Default from CDS under Weak Identification," Supervisory Research and Analysis Working Papers RPA 17-1, Federal Reserve Bank of Boston.
    14. Jeremy Leake, 2003. "Credit spreads on sterling corporate bonds and the term structure of UK interest rates," Bank of England working papers 202, Bank of England.
    15. Xin Huang & Hao Zhou & Haibin Zhu, 2012. "Systemic Risk Contributions," Journal of Financial Services Research, Springer;Western Finance Association, vol. 42(1), pages 55-83, October.
    16. Milne, Alistair, 2014. "Distance to default and the financial crisis," Journal of Financial Stability, Elsevier, vol. 12(C), pages 26-36.
    17. Zhijian (James) Huang & Yuchen Luo, 2016. "Revisiting Structural Modeling of Credit Risk—Evidence from the Credit Default Swap (CDS) Market," JRFM, MDPI, vol. 9(2), pages 1-20, May.
    18. Masahiko Egami & Rusudan Kevkhishvili, 2020. "Time reversal and last passage time of diffusions with applications to credit risk management," Finance and Stochastics, Springer, vol. 24(3), pages 795-825, July.
    19. Pesaran, M. Hashem & Schuermann, Til & Treutler, Bjorn-Jakob & Weiner, Scott M., 2006. "Macroeconomic Dynamics and Credit Risk: A Global Perspective," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(5), pages 1211-1261, August.
    20. Sandrine Lardic & Claire Gauthier, 2003. "Un modèle multifactoriel des spreads de crédit : estimation sur panels complets et incomplets," Économie et Prévision, Programme National Persée, vol. 159(3), pages 53-69.

    More about this item

    Keywords

    PD term structure; loan credit quality score; macroeconomic scenario; forward survival probability; maximum likelihood;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • G31 - Financial Economics - - Corporate Finance and Governance - - - Capital Budgeting; Fixed Investment and Inventory Studies
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • G38 - Financial Economics - - Corporate Finance and Governance - - - Government Policy and Regulation
    • M4 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting

    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:pra:mprapa:80641. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.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.