Czasopismo
Tytuł artykułu
Autorzy
Warianty tytułu
Języki publikacji
Abstrakty
This paper presents two cases of random banking data generators based on migration matrices and scoring rules. The banking data generator is a breakthrough in researches aimed at finding a method to compare various credit scoring techniques. These data are very useful for various analyses to understand the complexity of banking processes in a better way and are also of use for students and their researches. Another application can be in the case of small samples, e.g. when historical data are too fresh or are connected with the processing of a small number of exposures. In these cases a data generator can extend a sample to an adequate size for advanced analysis. The influence of one cyclic macro-economic variable on client characteristics and their stability over time is analyzed. Some stimulating conclusions for crisis behavior are presented, namely that if a crisis is impacted by both factors: application and behavioral, then it is very difficult to clearly indicate these factors in a typical scoring analysis and the crisis becomes widespread in every kind of risk report. (original abstract)
Twórcy
autor
- Warsaw School of Economics, Poland
Bibliografia
- Bellotti, T., Crook, J. (2009). Credit Scoring with Macroeconomic Variables Using Survival Analysis. Journal of the Operational Research Society, 1699-1707.
- BIS-BASEL (2005). International Convergence of Capital Measurement and Capital Standards. Technical Report, Basel Committee on Banking Supervision, Bank For International Settlements. Retrieved from http://www.bis.org.
- BIS-WP14 (2005). Studies on Validation of Internal Rating Systems, Working Paper No. 14, Technical Report, Basel Committee on Banking Supervision, Bank For International Settlements. Retrieved from http://www.bis.org.
- Crook, J. (2008). Dynamic Consumer Risk Models: an Overview. Paper presented at Credit Scoring Conference CRC, Edinburgh. Retrieved from http://www.business-school.ed.ac.uk/crc/conferences/ conference-archive?a=45349.
- Huang, E., Scott, C. (2007). Credit Risk Scorecard Design, Validation and User Acceptance: A Lesson for Modellers and Risk Managers. Paper presented at Credit Scoring Conference CRC, Edinburgh. Retrieved from http://www. business-school.ed. ac.uk/crc/conferences/conference-archive?a=45569.
- Huang, E. (2007). Scorecard Specification, Validation and User Acceptance: A Lesson for Modellers and Risk Managers. Paper presented at Credit Scoring Conference CRC, Edinburgh. Retrieved from http://www.business-school.ed.ac.uk/crc/conferences/ conference-archive?a=45487.
- Majer, I. (2010). Application Scoring: Logit Model Approach and the Divergence Method Compared. Warsaw School of Economics - SGH, Working Paper No. 10-06.
- Malik, M., Thomas, L. C. (2009). Modelling Credit Risk in Portfolios of Consumer Loans: Transition Matrix Model for Consumer Credit Ratings. Paper presented at Credit Scoring Conference CRC, Edinburgh. Retrieved from http://www.business-school.ed.ac.uk/crc/conferences/ conference-archive?a=45281.
- Mays, E. (2009). Systematic Risk Effects on Consumer Lending Products. Paper presented at Credit Scoring Conference CRC, Edinburgh. Retrieved from http://www.business-school.ed.ac.uk/crc/conferences/ conference-archive?a=45269.
- Siddiqi, N. (2005). Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring. Wiley and SAS Business Series.
- Supramaniam, M., Shanmugam, B. (2009). Simulating Retail Banking for Banking Students. Reports -Evaluative, Practitioners and Researchers ERIC Identifier: ED503907. Retrieved fromhttp://www.eric.ed.gov/ERICWebPortal/ contentdelivery/ servlet/ERICServlet?accno=ED503907
- Watson, H. J. (1981). Computer Simulation in Business. New York: John Wiley & Sons.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171290407