Ensemble predictions of recovery rates
AbstractIn many domains, the combined opinion of a committee of experts provides better decisions than the judgment of a single expert. This paper shows how to implement a successful ensemble strategy for predicting recovery rates on defaulted debts. Using data from Moody's Ultimate Recovery Database, it is shown that committees of models derived from the same regression method present better forecasts of recovery rates than a single model. More accurate predictions are observed whether we forecast bond or loan recoveries, and across the entire range of actual recovery values.
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Bibliographic InfoPaper provided by Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon in its series CEMAPRE Working Papers with number 1301.
Length: 26 pages
Date of creation: Mar 2013
Date of revision:
Recovery rate; Loss given default; Forecasting; Ensemble learning; Credit risk;
Find related papers by JEL classification:
- G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
- G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
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- Papke, Leslie E & Wooldridge, Jeffrey M, 1996.
"Econometric Methods for Fractional Response Variables with an Application to 401(K) Plan Participation Rates,"
Journal of Applied Econometrics,
John Wiley & Sons, Ltd., vol. 11(6), pages 619-32, Nov.-Dec..
- Leslie E. Papke & Jeffrey M. Wooldridge, 1993. "Econometric Methods for Fractional Response Variables with an Application to 401(k) Plan Participation Rates," NBER Technical Working Papers 0147, National Bureau of Economic Research, Inc.
- Stefano Caselli & Stefano Gatti & Francesca Querci, 2008. "The Sensitivity of the Loss Given Default Rate to Systematic Risk: New Empirical Evidence on Bank Loans," Journal of Financial Services Research, Springer, vol. 34(1), pages 1-34, August.
- Grunert, Jens & Weber, Martin, 2009. "Recovery rates of commercial lending: Empirical evidence for German companies," Journal of Banking & Finance, Elsevier, vol. 33(3), pages 505-513, March.
- Loterman, Gert & Brown, Iain & Martens, David & Mues, Christophe & Baesens, Bart, 2012. "Benchmarking regression algorithms for loss given default modeling," International Journal of Forecasting, Elsevier, vol. 28(1), pages 161-170.
- Sanjiv Das, 2007. "Basel II: Correlation Related Issues," Journal of Financial Services Research, Springer, vol. 32(1), pages 17-38, October.
- 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.
- Bastos, João A., 2010.
"Forecasting bank loans loss-given-default,"
Journal of Banking & Finance,
Elsevier, vol. 34(10), pages 2510-2517, October.
- Altman, Edward I. & Kalotay, Egon A., 2014. "Ultimate recovery mixtures," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 116-129.
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