1. bookVolume 6 (2017): Issue 2 (May 2017)
Journal Details
License
Format
Journal
eISSN
2336-9205
First Published
11 Mar 2014
Publication timeframe
3 times per year
Languages
English
access type Open Access

Estimating Probability of Default on Peer to Peer Market – Survival Analysis Approach

Published Online: 22 May 2017
Volume & Issue: Volume 6 (2017) - Issue 2 (May 2017)
Page range: 149 - 167
Received: 18 Sep 2016
Accepted: 21 Jan 2017
Journal Details
License
Format
Journal
eISSN
2336-9205
First Published
11 Mar 2014
Publication timeframe
3 times per year
Languages
English
Abstract

Arguably a cornerstone of credit risk modelling is the probability of default. This article aims is to search for the evidence of relationship between loan characteristics and probability of default on peer-to-peer (P2P) market. In line with that, two loan characteristics are analysed: 1) loan term length and 2) loan purpose. The analysis is conducted using survival analysis approach within the vintage framework. Firstly, 12 months probability of default through the cycle is used to compare riskiness of analysed loan characteristics. Secondly, log-rank test is employed in order to compare complete survival period of cohorts. Findings of the paper suggest that there is clear evidence of relationship between analysed loan characteristics and probability of default. Longer term loans are more risky than the shorter term ones and the least risky loans are those used for credit card payoff.

Keywords

JEL Classification

1. Allen, L. N. and Rose, L. C. (2006). Financial survival analysis of defaulted debtors, Journal of Operational Research Society, 57, 630-636.10.1057/palgrave.jors.2602038Search in Google Scholar

2. Atz, U., Bholat, D., (2016) Peer-to-peer lending and financial innovation in the United Kingdom, Bank of England, Staff Working Paper No. 598.Search in Google Scholar

3. Baba, N. and Goko, H. (2006). Survival analysis of hedge funds, Bank of Japan, Working Papers Series No. 06Search in Google Scholar

4. Buis, M. L. (2006) An introduction to Survival Analysis, Working paper, Retrieved from http://maartenbuis.nl/wp/survival.pdfSearch in Google Scholar

5. Carling, K., Jacobson, T. and Roszbach, K. (1998). Duration of consumer loans and bank lending policy: dormancy versus default risk, Working Paper Series in Economics and Finance No. 280, Stockholm School of Economics.Search in Google Scholar

6. Chen, D., Han, C., (2012) A Comparative Study of online P2P Lending in the USA and China, Journal of Internet Banking and Commerce,. 17, 1-15Search in Google Scholar

7. De Roure, C., Pelizzon, L., Tasca, P. (2016) How does P2P lending fit into the consumer credit market?, Discussion Paper Deutsche Bundesbank, 30, Retrieved from https://www.bundesbank.de/Redaktion/EN/Downloads/Publications/Discussion_Paper_1/2016/2016_08_12_dkp_30.pdf?__blob=publicationFile10.2139/ssrn.2848043Search in Google Scholar

8. Duarte J., Siegel S., Young L. (2012) Trust and Credit: The Role of Appearance in Peer-to-peer Lending, Oxford University Press on behalf of The Society for Financial Studies, doi:10.1093/rfs/hhs07110.1093/rfs/hhs071Search in Google Scholar

9. Glennon, D. and Nigro, P. (2005). Measuring the default risk of small business loans: a survival analysis approach, Journal of Money, Credit, and Banking, 37, 923-947.10.1353/mcb.2005.0051Search in Google Scholar

10. Golubnicijs, D., (2012) Is Your Peer a Lemon? Relative Assessment of Risk Remuneration on the P2P Lending Market, (Master’s thesis). Retrieved from http://arc.hhs.se/download.aspx?MediumId=1529Search in Google Scholar

11. Käfer, B. (2016) Peer to Peer Lending – A (Financial Stability) Risk Perspective, Joint Discussion Paper Series in Economics by the Universities of Aachen · Gießen · Göttingen Kassel · Marburg · Siegen, Retrieved from http://www.uni-marburg.de/fb02/makro/forschung/magkspapers.Search in Google Scholar

12. Malik, M. and Thomas L. (2006). Modelling credit risk of portfolio of consumer loans, University of Southampton, School of Management Working Paper Series No. CORMSIS-07-12.Search in Google Scholar

13. Mason, R., Gunst, F.R. and Hess, J.L. Statistical Design and Analysis of Experiments with Application to Engineering and Science, Wiley-Interscience, 200310.1002/0471458503Search in Google Scholar

14. Milne, A., Parboteeah, P (2016) The Business Models and Economics of Peer-to-Peer Lending, European Credit Research Institute (ECRI), Research Report No 1710.2139/ssrn.2763682Search in Google Scholar

15. Narain, B. (1992): Survival Analysis and the Credit Granting Decision. In: Thomas, L. C. – Crook, J. N. – Edelman, D. B. (eds): Credit Scoring and Credit Control. Oxford, Oxford University Press, 1992, 109-122.Search in Google Scholar

16. Roszbach, K. (2003). Bank lending policy, credit scoring and the survival of loans, Sverriges Riksbank Working Paper Series No. 154Search in Google Scholar

17. Serrano-Cinca C, Gutiérrez-Nieto B, López-Palacios L (2015) Determinants of Default in P2P Lending. PLoS ONE 10(10): e0139427. doi:10.1371/journal.pone.013942710.1371/journal.pone.0139427Search in Google Scholar

18. Stevenson, M. (2009) An Introduction to Survival Analysis, Retrieved from http://www.massey.ac.nz/massey/fms/Colleges/College%20of%20Sciences/Epicenter/docs/ASVCS/Stevenson_survival_analysis_195_721.pdfSearch in Google Scholar

19. Xu, X. (2016) Estimating Lifetime Expected Credit Losses Under IFRS 9, Retrieved from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2758513Search in Google Scholar

Recommended articles from Trend MD

Plan your remote conference with Sciendo