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COVID-19, Credit Risk and Macro Fundamentals

Author

Listed:
  • Anna Dubinova

    (Vrije Universiteit Amsterdam)

  • Andre Lucas

    (Vrije Universiteit Amsterdam)

  • Sean Telg

    (Vrije Universiteit Amsterdam)

Abstract

We investigate the relationship between macro fundamentals and credit risk, rating migrations and defaults during the start of the COVID-19 pandemic. We find that credit risk models that use macro fundamentals as covariates overestimate credit risk incidence due to the unprecedented drops in economic activity in the first lockdowns. We argue that this break in the macro-credit linkage is less affected if we take an unobserved components modeling framework, both at shorter and longer credit risk horizons.

Suggested Citation

  • Anna Dubinova & Andre Lucas & Sean Telg, 2021. "COVID-19, Credit Risk and Macro Fundamentals," Tinbergen Institute Discussion Papers 21-059/III, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20210059
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    File URL: https://papers.tinbergen.nl/21059.pdf
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    References listed on IDEAS

    as
    1. Drew Creal & Bernd Schwaab & Siem Jan Koopman & Andr� Lucas, 2014. "Observation-Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk," The Review of Economics and Statistics, MIT Press, vol. 96(5), pages 898-915, December.
    2. Duffie, Darrell & Saita, Leandro & Wang, Ke, 2007. "Multi-period corporate default prediction with stochastic covariates," Journal of Financial Economics, Elsevier, vol. 83(3), pages 635-665, March.
    3. Koopman, Siem Jan & Kräussl, Roman & Lucas, André & Monteiro, André B., 2009. "Credit cycles and macro fundamentals," Journal of Empirical Finance, Elsevier, vol. 16(1), pages 42-54, January.
    4. Koopman, Siem Jan & Lucas, Andre & Monteiro, Andre, 2008. "The multi-state latent factor intensity model for credit rating transitions," Journal of Econometrics, Elsevier, vol. 142(1), pages 399-424, January.
    5. Zhang, Dayong & Hu, Min & Ji, Qiang, 2020. "Financial markets under the global pandemic of COVID-19," Finance Research Letters, Elsevier, vol. 36(C).
    6. Azizpour, S & Giesecke, K. & Schwenkler, G., 2018. "Exploring the sources of default clustering," Journal of Financial Economics, Elsevier, vol. 129(1), pages 154-183.
    7. Andrew Harvey & Alessandra Luati, 2014. "Filtering With Heavy Tails," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1112-1122, September.
    8. Augustin, Patrick & Sokolovski, Valeri & Subrahmanyam, Marti G. & Tomio, Davide, 2022. "In sickness and in debt: The COVID-19 impact on sovereign credit risk," Journal of Financial Economics, Elsevier, vol. 143(3), pages 1251-1274.
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    11. Drew Creal & Siem Jan Koopman & André Lucas, 2013. "Generalized Autoregressive Score Models With Applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(5), pages 777-795, August.
    12. F. Blasques & S. J. Koopman & A. Lucas, 2015. "Information-theoretic optimality of observation-driven time series models for continuous responses," Biometrika, Biometrika Trust, vol. 102(2), pages 325-343.
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    COVID-19; credit risk; macro fundamentals; frailty factors; dynamic latent factors;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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