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A framework to assess vulnerabilities arising from household indebtedness using microdata

In: Proceedings of the IFC Conference on "Initiatives to address data gaps revealed by the financial crisis", Basel, 25-26 August 2010

Author

Listed:
  • Ramdane Djoudad

Abstract

Rising levels of household indebtedness have created concerns about the vulnerabilities of households to adverse economic shocks and the impact on financial stability. To assess these risks, the author presents a formal stress-testing framework that uses microdata to simulate how various economic shocks affect the distribution of the debt-service ratio (DSR) for the household sector. Data from an Ipsos Reid Canadian Financial Monitor survey are used to construct the actual DSR distribution for households. Changes in the distribution are then simulated using a macro scenario describing the evolution of some aggregate variables, and micro behavioural relationships; for example, to simulate credit growth for individual households, cross-sectional data are used to estimate debt-growth equations as a function of household income, interest rates and housing prices. The simulated distributions provide information on vulnerabilities in the household sector. The author also describes a combined methodology where changes in the probability of default on household loans are used as a metric to evaluate the quantitative impact of negative employment shocks on the resilience of households and loan losses at financial institutions.
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Suggested Citation

  • Ramdane Djoudad, 2011. "A framework to assess vulnerabilities arising from household indebtedness using microdata," IFC Bulletins chapters,in: Bank for International Settlements (ed.), Proceedings of the IFC Conference on "Initiatives to address data gaps revealed by the financial crisis", Basel, 25-26 August 2010, volume 34, pages 151-168 Bank for International Settlements.
  • Handle: RePEc:bis:bisifc:34-13
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    References listed on IDEAS

    as
    1. Ramdane Djoudad, 2009. "Simulations du ratio du service de la dette des consommateurs en utilisant des données micro," Staff Working Papers 09-18, Bank of Canada.
    2. Shubhasis Dey & Ramdane Djoudad & Yaz Terajima, 2008. "A Tool for Assessing Financial Vulnerabilities in the Household Sector," Bank of Canada Review, Bank of Canada, vol. 2008(Summer), pages 47-56.
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    Citations

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    Cited by:

    1. Gross, Marco & Población, Javier, 2017. "Assessing the efficacy of borrower-based macroprudential policy using an integrated micro-macro model for European households," Economic Modelling, Elsevier, vol. 61(C), pages 510-528.
    2. repec:bis:bisifc:46-16 is not listed on IDEAS
    3. Mikus Arins & Nadezda Sinenko & Laura Laube, 2014. "Survey-Based Assessment of Household Borrowers' Financial Vulnerability," Discussion Papers 2014/01, Latvijas Banka.
    4. Tom Bilston & Robert Johnson & Matthew Read, 2015. "Stress Testing the Australian Household Sector Using the HILDA Survey," RBA Research Discussion Papers rdp2015-01, Reserve Bank of Australia.
    5. repec:unt:jnapdj:v:24:y:2017:i:2:p:23-52 is not listed on IDEAS
    6. Gross, Marco & Población García, Francisco Javier, 2016. "Assessing the efficacy of borrower-based macroprudential policy using an integrated micro-macro model for European households," Working Paper Series 1881, European Central Bank.
    7. Daisy J. Pacheco-Bernal & Santiago D. Segovia-Baquero & Ana M. Yaruro-Jaime, 2017. "Vulnerabilidades financieras de los hogares en Colombia," Borradores de Economia 1026, Banco de la Republica de Colombia.
    8. Gaston Giordana & Michael Ziegelmeyer, 2017. "Household debt burden and financial vulnerability in Luxembourg," IFC Bulletins chapters,in: Bank for International Settlements (ed.), Data needs and Statistics compilation for macroprudential analysis, volume 46 Bank for International Settlements.
    9. Céline Gauthier & Moez Souissi & Xuezhi Liu, 2014. "Introducing Funding Liquidity Risk in a Macro Stress-Testing Framework," International Journal of Central Banking, International Journal of Central Banking, vol. 10(4), pages 105-142, December.

    More about this item

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • D14 - Microeconomics - - Household Behavior - - - Household Saving; Personal Finance
    • E51 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Money Supply; Credit; Money Multipliers

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