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Number of results
2015 | 127 | 3A | A-66-A-69

Article title

Application of Data Envelopment Analysis to Calculating Probability of Default for High Rated Portfolio

Content

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Languages of publication

EN

Abstracts

EN
The aim of our research is to propose a method of rating companies which is based on efficiency measure given by Data Envelopment Analysis (DEA). Proper rating of borrowers is an essential requirement of PD estimation. The difficulty in DEA application is the selection of input and output from the set of indicators describing evaluated objects, which is usually based on expert knowledge. Therefore we apply random forests and gradient boosting to select financial indicators used by the DEA approach and to obtain a ranking of companies needed for PD estimation.

Keywords

EN

Contributors

author
  • Department of Informatics, Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences, Nowoursynowska 159, PL-02776 Warszawa, Poland
author
  • Department of Informatics, Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences, Nowoursynowska 159, PL-02776 Warszawa, Poland

References

  • [1] Capital requirements regulation and directive,European Commission, 2006, http://ec.europa.eu/finance/bank/regcapital/legislation-in-force/index_en.htm
  • [2] Capital requirements regulation and directive,European Commission, 2009, http://ec.europa.eu/finance/bank/regcapital/legislation-in-force/index_en.htm
  • [3] Capital requirements regulation and directive,European Commission, 2010, http://ec.europa.eu/finance/bank/regcapital/legislation-in-force/index_en.htm
  • [4] Capital requirements regulation and directive,European Commission, 2013, http://ec.europa.eu/finance/bank/regcapital/legislation-in-force/index_en.htm
  • [5] L. Dzidzeviciute, Ekonomika 91, 132 (2012)
  • [6] K. Pluto, D. Tasche, Estimating Probabilities of Default for Low Default Portfolios in: The Basel II Risk Parameters, Eds. B. Engelmann, R. Rauhmeier et al., Springer, Berlin 2006, p. 79
  • [7] W.W. Cooper, L.M. Seiford, K. Tone, Introduction to Data Envelopment Analysis and Its Uses with DEA-Solver Software and References, Springer, New York 2006
  • [8] B. Kaczmarska, Econometrica 20, 79 (2010)
  • [9] P. Andersen, N.C. Petersen, Manage. Sci. 39, 1261 (1993), doi: 10.1287/mnsc.39.10.1261
  • [10] R.A. Berk, Statistical learning from a regression perspective, Springer, New York 2008
  • [11] J. Koronacki, J. Ćwik, Statystyczne systemy uczące się, Akademicka Oficyna Wydawnicza EXIT, Warszawa 2008
  • [12] T. Hastie, R. Tibshirani, J. Friedman, The elements of statistical learning. Data Mining, Inference and Prediction, Springer, New York 2009
  • [13] L. Breiman, Mach. Learn. 45, 5 (2001), doi: 10.1023/A:1010933404324
  • [14] A. Feruś, Bank i Kredyt 37, 44 (2006)
  • [15] E. Chodakowska, K. Wardzińska, Quantitative Methods in Economics 14, 74 (2013)

Document Type

Publication order reference

Identifiers

YADDA identifier

bwmeta1.element.bwnjournal-article-appv127n3a11kz
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