Share:


A new pricing approach for SME loans issued by commercial banks based on credit score mapping and Archimedean Copula simulation

    Chang Liu Affiliation
    ; Haoming Shi Affiliation
    ; Yujun Cai Affiliation
    ; Shu Shen Affiliation
    ; Dongtao Lin Affiliation

Abstract

The traditional loans pricing methods are usually based on risk measures of individual loan’s characteristics without considering the correlation between the defaults of different loans and the contribution of individual loans to the entire loan portfolio. In this study, using account-level loans data of 2010-2016 abstracted from 2 databases kindly provided by a Chinese commercial bank, the authors choose Archimedean Copula to fit the default relationship between loans, combined with the loss distribution function constructed to measure the economic capital of the loan portfolio, to propose a loan pricing method that is more suitable for measuring the unique risk characteristic of SMEs loans. Empirical evidence shows that compared with the traditional loan pricing model, this new proposed one, requiring lower loan interest rates from customers with higher credit rating, while higher loan interest rates from customers with lower credit rating, could thus be able to provide higher risk-adjusted returns, higher economic capital adequacy ratios, and ultimately stronger banks’ capabilities to tolerate risk events. Although there might still be some issues and limitations in the study, the method proposed in this study could be of interest not only to the banks’ management, but also to banking regulators as well.

Keyword : loan pricing, economic capital, Archimedean Copula, SMEs loans, internal rating model, RAROC, capital adequacy, risk tolerance

How to Cite
Liu, C., Shi, H., Cai, Y., Shen, S., & Lin, D. (2019). A new pricing approach for SME loans issued by commercial banks based on credit score mapping and Archimedean Copula simulation. Journal of Business Economics and Management, 20(4), 618-632. https://doi.org/10.3846/jbem.2019.9854
Published in Issue
May 13, 2019
Abstract Views
469
PDF Downloads
356
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Aas, K., & Puccetti, G. M. (2014). Bounds on total economic capital: the DNB case study. Extremes, 17(4), 693-715. Retrieved from https://doi.org/10.1007/s10687-014-0202-0

Abdelkafi, S. Z., Ghorbel, A., & Khoufi, W. M. (2018). Energy portfolio risk management using timevarying copula methods: application to bonds, interest rate and VIX. American Journal of Finance and Accounting, 5(4), 371-393. https://doi.org/10.1504/AJFA.2018.093633

Alie, & Jean, K. (2016). Assessment of business risk economic capital for South Africa banks: a response to pillar 2 of Basel ii (Doctoral dissertation). M.M. Thesis, University of the Witwatersrand, Johannesburg, The Republic of South Africa.

Allen, J., Chapman, J., Echenique, F., & Shum, M. M. (2016). Efficiency and bargaining power in the interbank loan market. International Economic Review, 57(2), 691-716. https://doi.org/10.1111/iere.12173

Baltussen, G., van Bekkum, S., & van der Grient, B. (2018). Unknown unknowns: uncertainty about risk and stock returns. Journal of Financial & Quantitative Analysis, 53(2018), 1615-1651. https://doi.org/10.1017/S0022109018000480

Cao, J. M. (2011). Calibration and main scale development for credit risk model of non-retail risk exposure. International Finance Research, 7, 81-103.

Demarta, S., & Mcneil, A. J. (2010). The t copula and related copulas. International Statistical Review, 73(1), 111-129. https://doi.org/10.1111/j.1751-5823.2005.tb00254.x

Deng, C., Ao, H., Hu, W., & Wang, X. M. (2010). Research on loan pricing for small business from big bank based on relationship loans. Economic Research, 2, 83-96.

Flood, M. D., & Korenko, G. (2013). Systematic scenario selection: stress testing and the nature of uncertainty (SSRN Scholarly Paper No. ID 1262896). Rochester, NY: Social Science Research Network. Retrieved from https://papers.ssrn.com/abstract=1262896

Ghosh, I., & Ray, S. M. (2016). Some alternative bivariate Kumaraswamy-type distributions via copula with application in risk management. Journal of Statistical Theory and Practice, 10(4), 693-706. https://doi.org/10.1080/15598608.2016.1215943

Giacomini, R., & Rossi, B. M. (2016). Model comparisons in unstable environments. International Economic Review, 57(2), 369-392. https://doi.org/10.1111/iere.12161

Gorelaya, N. M. (2016). Otsenka vliyaniya faktorov na formirovanie tseny kredita [Evaluation of the Impact of Factors on Loan Pricing]. Journal of Corporate Finance Research, 10(1), 59-76. Retrieved from https://ssrn.com/abstract=3073043

Gubareva, M., & Borges, M. R. M. (2018). Rethinking economic capital management through the integrated derivative-based treatment of interest rate and credit risk. Annals of Operations Research, 266(1-2), 71-100. Retrieved from http://hdl.handle.net/10400.21/6962

Hauptmann, C. M. (2017). Corporate sustainability performance and bank loan pricing: it pays to be good, but only when banks are too. Saïd Business School WP, 2017(20). Retrieved from https://ssrn.com/abstract=3067422

Karmakar, M., & Paul, S. M. (2018). Intraday portfolio risk management using VaR and CVaR: A CGARCH-EVT-Copula approach. International Journal of Forecasting, 35(2), 699-709. https://doi.org/10.1016/j.ijforecast.2018.01.010

Khan, F., Hashemi, S. J., Paltrinieri, N., Amyotte, P., Cozzani, V., & Reniers, G. M. (2016). Dynamic risk management: a contemporary approach to process safety management. Current opinion in chemical engineering, 14, 9-17. https://doi.org/10.1016/j.coche.2016.07.006

Kim, J., Shim, K., Cao, L., Lee, J., Lin, X., & Moon, Y. (2017, 23-26 May). Advances in knowledge discovery and data mining: 21st Pacific-Asia Conference, PAKDD 2017, Proceedings, Part I. Jeju, South Korea.

Liu, C., Guo, M., Mo, N., & Zhang, X. M. (2012). Building an effective measurement and early warning index system for credit risk of SMES loans in China’s commercial banks based on internal ratings approach of Basel Accord. Financial Regulatory Research, 7, 26-39.

Liu, C., Zhang, X., & Mo, N. M. (2013). Construction and empirical study on internal ratings model of SMEs loans in China’s commercial banks. Investment Research, 5, 3-16.

Oh, D. H., & Patton, A. J. M. (2018). Time-varying systemic risk: evidence from a dynamic copula model of CDS spreads. Journal of Business & Economic Statistics, 36(2), 181-195. https://doi.org/10.1080/07350015.2016.1177535

Petropoulos, A., Chatzis, S. P., & Xanthopoulos, S. M. (2016). A novel corporate credit rating system based on Student co hidden Markov models. Expert Systems with Applications, 53, 87-105. https://doi.org/10.1016/j.eswa.2016.01.015

Pfeifer, D., Tsatedem, H. A., Mändle, A., & Girschig, C. M. (2016). New copulas based on general partitions-of-unity and their applications to risk management. Dependence Modeling, 4(1), 123-140. https://doi.org/10.1515/demo-2016-0006

Scandizzo, S. B. (2016). Economic capital models. In S. Scandizzo (Ed.), The validation of risk models (pp. 193-204). London, UK: Palgrave Macmillan. https://doi.org/10.1057/9781137436962_13

Shim, F., & Lee, B. (2017). An estimation of VaR based on Archimedean Copula. Journal of Chongqing Institute of Technology, 22(8), 326-336.

Tang, X. (2015). Credit loss distribution and copula in risk management (MSc Thesis). Mathematical Institute Leiden University, Leiden, The Netherlands.

Walke, A. G., Fullerton Jr, T. M., & Tokle, R. J. M. (2018). Risk-based loan pricing consequences for credit unions. Journal of Empirical Finance, 47, 105-119. https://doi.org/10.1016/j.jempfin.2018.02.006

Wang, T., & Libaers, D. M. (2015). Nonmimetic knowledge and innovation performance: empirical evidence from developing countries. Journal of Product Innovation Management, 33(5), 570-588. https://doi.org/10.1111/jpim.12306