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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, September.
    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, December.
    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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