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Explaining Aggregated Recovery Rates

Author

Listed:
  • Stephan Höcht

    (Assenagon Asset Management S.A., Zweigniederlassung München, Prannerstraße 8, 80333 München, Germany)

  • Aleksey Min

    (Chair of Mathematical Finance, Technical University of Munich, Parkring 11, 85748 Garching, Germany)

  • Jakub Wieczorek

    (Rothesay, The Post Building, 100 Museum Street, London WC1A 1PB, UK)

  • Rudi Zagst

    (Chair of Mathematical Finance, Technical University of Munich, Parkring 11, 85748 Garching, Germany)

Abstract

This study on explaining aggregated recovery rates (ARR) is based on the largest existing loss and recovery database for commercial loans provided by Global Credit Data, which includes defaults from 5 continents and over 120 countries. The dependence of monthly ARR from bank loans on various macroeconomic factors is examined and sources of their variability are stated. For the first time, an influence of stochastically estimated monthly growth of GDP USA and Europe is quantified. To extract monthly signals of GDP USA and Europe, dynamic factor models for panel data of different frequency information are employed. Then, the behavior of the ARR is investigated using several regression models with unshifted and shifted explanatory variables in time to improve their forecasting power by taking into account the economic situation after the default. An application of a Markov switching model shows that the distribution of the ARR differs between crisis and prosperity times. The best fit among the compared models is reached by the Markov switching model. Moreover, a significant influence of the estimated monthly growth of GDP in Europe is observed for both crises and prosperity times.

Suggested Citation

  • Stephan Höcht & Aleksey Min & Jakub Wieczorek & Rudi Zagst, 2022. "Explaining Aggregated Recovery Rates," Risks, MDPI, vol. 10(1), pages 1-30, January.
    • Handle: RePEc:gam:jrisks:v:10:y:2022:i:1:p:18-:d:721934
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    References listed on IDEAS

    as
    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. Daniel M. Covitz & Song Han, 2004. "An empirical analysis of bond recovery rates: exploring a structural view of default," Finance and Economics Discussion Series 2005-10, Board of Governors of the Federal Reserve System (U.S.).
    3. Qi, Min & Zhao, Xinlei, 2011. "Comparison of modeling methods for Loss Given Default," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 2842-2855, November.
    4. Joao A. Bastos, 2010. "Predicting bank loan recovery rates with neural networks," CEMAPRE Working Papers 1003, Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon.
    5. Raffaella Calabrese, 2012. "Estimating bank loans loss given default by generalized additive models," Working Papers 201224, Geary Institute, University College Dublin.
    6. Aleksey Min & Matthias Scherer & Amelie Schischke & Rudi Zagst, 2020. "Modeling Recovery Rates of Small- and Medium-Sized Entities in the US," Mathematics, MDPI, vol. 8(11), pages 1-18, October.
    7. Dermine, J. & de Carvalho, C. Neto, 2006. "Bank loan losses-given-default: A case study," Journal of Banking & Finance, Elsevier, vol. 30(4), pages 1219-1243, April.
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    Cited by:

    1. Frank Ranganai Matenda & Mabutho Sibanda, 2022. "Determinants of Default Probability for Audited and Unaudited SMEs under Stressed Conditions in Zimbabwe," Economies, MDPI, vol. 10(11), pages 1-28, November.
    2. Frank Ranganai Matenda & Mabutho Sibanda & Eriyoti Chikodza & Victor Gumbo, 2022. "Corporate Loan Recovery Rates under Downturn Conditions in a Developing Economy: Evidence from Zimbabwe," Risks, MDPI, vol. 10(10), pages 1-24, October.

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