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Modelling credit risk

Author

Listed:
  • Somnath Chatterjee

Abstract

Financial institutions have developed sophisticated techniques to quantify and manage credit risk. From a regulator's perspective a clear understanding of the techniques used would enhance supervisory oversight of financial institutions. The role of a credit risk model is to take as input the conditions of the general economy and those of the firm in question, and generate as output a credit spread. This handbook describes the different methods used to arrive at this notion of a credit spread. the field, discussing how text mining is useful for addressing research topics of interest to central banks, and providing a step-by-step primer on how to mine text, including an overview of unsupervised and supervised techniques.

Suggested Citation

  • Somnath Chatterjee, 2015. "Modelling credit risk," Handbooks, Centre for Central Banking Studies, Bank of England, number 34, April.
    • Handle: RePEc:ccb:hbooks:34
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    File URL: https://www.bankofengland.co.uk/ccbs/modelling-credit-risk
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    References listed on IDEAS

    as
    1. Baz,Jamil & Chacko,George, 2004. "Financial Derivatives," Cambridge Books, Cambridge University Press, number 9780521815109, October.
    2. Haldane, Andrew & Hall, Simon & Pezzini, Silvia, 2007. "Financial Stability Paper No 2: A New Approach to Assessing Risks to Financial Stability," Bank of England Financial Stability Papers 2, Bank of England.
    3. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    4. 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.
    5. Sidanius, Che & Zikes, Filip, 2012. "Financial Stability Paper No 18: OTC derivatives reform and collateral demand impact," Bank of England Financial Stability Papers 18, Bank of England.
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    Cited by:

    1. Broeders, Dirk & de Haan, Leo & Willem van den End, Jan, 2023. "How quantitative easing changes the nature of sovereign risk," Journal of International Money and Finance, Elsevier, vol. 137(C).
    2. Marina Dolfin & Damian Knopoff & Michele Limosani & Maria Gabriella Xibilia, 2019. "Credit Risk Contagion and Systemic Risk on Networks," Mathematics, MDPI, vol. 7(8), pages 1-16, August.
    3. Yuan Gao & Biao Jiang & Jietong Zhou, 2023. "Financial Distress Prediction For Small And Medium Enterprises Using Machine Learning Techniques," Papers 2302.12118, arXiv.org.
    4. Talam, Camilla C. & Maru, Lucy, 2023. "The greening of Kenya's banking sector: Macro-financial stability implications of a low carbon transition," KBA Centre for Research on Financial Markets and Policy Working Paper Series 65, Kenya Bankers Association (KBA).
    5. Jumbe, George, 2023. "Credit Risk Assessment Using Default Models: A Review," OSF Preprints ksb8n, Center for Open Science.
    6. 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.

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    More about this item

    Keywords

    Modelling credit risk;

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

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