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What determines creditor recovery rates?

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  • Nada Mora

Abstract

The 2007-09 financial crisis illustrated the importance of healthy banks for the overall stability of the financial system and economy. Because banking is inherently risky, the health of banks depends on their ability to manage risk and exposure to losses. ; An important component of a strong risk management system is a bank?s ability to assess the potential losses on its investments. One factor that determines the extent of losses is the recovery rate on loans and bonds that are in default. For example, the recovery rate is said to be 50 percent if the creditor is able to recover only half the amount of principal and accrued interest due. ; Drawing on more than 30 years of recovery data on defaulted debt instruments, Mora finds that the state of the economy helps determine creditor recovery rates. Industry distress also drives recovery rates, and evidence suggests industry distress can be triggered by a weak economy.

Suggested Citation

  • Nada Mora, 2012. "What determines creditor recovery rates?," Economic Review, Federal Reserve Bank of Kansas City, vol. 97(Q II).
    • Handle: RePEc:fip:fedker:y:2012:i:qii:n:v.97no.2:x:2
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    1. Gambetti, Paolo & Gauthier, Geneviève & Vrins, Frédéric, 2019. "Recovery rates: Uncertainty certainly matters," Journal of Banking & Finance, Elsevier, vol. 106(C), pages 371-383.
    2. Hamid Safaynikou & Mohammad Taher Ahmadi Shadmehri & Ahmad Sabahi & Mohammad Javad Razmi, 2017. "Modeling the Effective Factors on Bank Loans Default Rate Using Delphi, SEM and Tobit Techniques (Evidence from Iran)," Modern Applied Science, Canadian Center of Science and Education, vol. 11(4), pages 1-13, April.
    3. Helberg, Stig & Lindset, Snorre, 2016. "Risk protection from risky collateral: Evidence from the euro bond market," Journal of Banking & Finance, Elsevier, vol. 70(C), pages 193-213.
    4. Candian, Giacomo & Dmitriev, Mikhail, 2020. "Default recovery rates and aggregate fluctuations," Journal of Economic Dynamics and Control, Elsevier, vol. 121(C).
    5. Barbagli, Matteo & Vrins, Frédéric, 2023. "Accounting for PD-LGD dependency: A tractable extension to the Basel ASRF framework," Economic Modelling, Elsevier, vol. 125(C).
    6. Mora, Nada, 2015. "Creditor recovery: The macroeconomic dependence of industry equilibrium," Journal of Financial Stability, Elsevier, vol. 18(C), pages 172-186.
    7. Tatiana Damjanovic & Sarunas Girdenas, 2013. "Should Central Bank respond to the Changes in the Loan to Collateral Value Ratio and in the House Prices?," Discussion Papers 1303, University of Exeter, Department of Economics.
    8. Damjanovic, Tatiana & Girdėnas, Šarūnas, 2014. "Quantitative easing and the loan to collateral value ratio," Journal of Economic Dynamics and Control, Elsevier, vol. 45(C), pages 146-164.
    9. Altdörfer, Marc & Guettler, Andre & Löffler, Gunter, 2024. "Analyst distance and credit rating consistency," Journal of International Money and Finance, Elsevier, vol. 143(C).
    10. Tang, Qihe & Tong, Zhiwei & Yang, Yang, 2021. "Large portfolio losses in a turbulent market," European Journal of Operational Research, Elsevier, vol. 292(2), pages 755-769.
    11. J. Molins & E. Vives, 2015. "Model risk on credit risk," Papers 1502.06984, arXiv.org, revised Dec 2015.

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