Duration Models in Loan Management

• Authors: Julian A. Vasilev
• Languages of publication: EN

Abstracts

EN

The purpose of this study is to estimate the future duration of a loan contract on the basis of several factors. The main methodology consists of a brief explanation of a survival analysis and a thorough application of a survival analysis in loan management. A real dataset from a credit institution (situated in Varna) is used. All contracts were signed for 30 days but some contracts were ended earlier, others - later. The main research question concerns the following statement. We may try to predict future loan duration by making an econometric model describing the dependency between the loan duration (as a dependent variable) and several independent variables. The dataset is analysed by calculating life tables, applying the Kaplan-Maier method and using Cox regression within SPSS. It is has been proved that the main covariates affecting loan duration are the variables: born in the region, month of birth and age. The formulated conclusions are valid for the analysed credit institution. This work provides a methodology for adapting duration models in credit institutions. The presented methodology (in this paper) may be applied over the dataset of other credit institutions (including banks) for loan duration prediction.

Keywords

EN

survival analysis; SPSS; the Wilcoxon test; the Kaplan-Maier method; Cox regression

• Publisher: Sciendo
• Journal: Folia Oeconomica Stetinensia
• Year: 2015
• Volume: 15
• Issue: 1
• Pages: 114-126

Dates

published

2015-06-01

received

2015-01-05

accepted

2015-04-27

online

2015-12-30

Contributors

author

Julian A. Vasilev

• Varna University of Economics Department of Informatics Knyaz Boris I, 77, Varna, Bulgaria

References

• Bian, H. (2015). Survival analysis using SPSS, http://core.ecu.edu/ofe/StatisticsResearch/Survival%20Analysis%20Using%20SPSS.pdf(19.05.2015).
• Garth A. (2008). Analysing data using SPSS. Sheffield Hallam University, https://students.shu.ac.uk/lits/it/documents/pdf/analysing_data_using_spss.pdf (19.05.2015).
• Gujarati D. (2004). Basic econometrics. 4th edition. The McGraw-Hill Companies, http://egei.vse.cz/english/wp-content/uploads/2012/08/Basic-Econometrics.pdf (19.05.2015).
• Vasilev, J. (2014). Creating a customer profile in a credit institution. International Journal of Advanced Research in Computer Science and Software Engineering, 4 (1): 1108-1112, www.ijarcsse.com/docs/papers/Volume_4/1_January2014/V4I1-0564.pdf (19.05.2015).
• Vasilev, J. (2014). Time series analysis in loan management systems. Theoretical and Applied economics, 21 (3): 57-66, http://store.ectap.ro/articole/962.pdf (19.05.2015).
• Vasilev, J. (2015). Calculating the probability of returning a loan with binary probability models. Romanian Statistical Review, 4: 55-71, www.revistadestatistica.ro/wp-content/uploads/2015/01/RRS_04_2014_A5.pdf (19.05.2015).

Identifiers

DOI

10.1515/foli-2015-0027