Semi-Markov credit risk modeling for a portfolio of consumer loans in the Kenyan banking industry

dc.creatorOthieno, Ferdinand
dc.creatorWagacha, Anthony
dc.date03/02/2015
dc.dateMon, 2 Mar 2015
dc.dateMon, 2 Mar 2015 14:33:43
dc.dateMon, 2 Mar 2015 14:33:43
dc.date.accessioned2015-03-18T11:29:16Z
dc.date.available2015-03-18T11:29:16Z
dc.descriptionPaper presented at the 11th African Finance Journal Conference, Durban, South Africa.
dc.descriptionBased on simulations of implied values for credit worthiness over a period of 5 years for 1000 consumers, we establish a case for the semi-markov models as a proxy for internal credit risk models for a portfolio of consumer loans. With ample calibration, we prove the robustness of the semi-markov models in forecasting probabilities of default and loss given default. With a view of credit risk as a reliability problem, we generate credit risk indicators as qualifications of adequacy of a loan portfolio. This informs prospective holding of capital based on forecast delinquencies as opposed to the current retrospective practice that relies on the trigger event of default. We use Monte-Carlo simulation techniques to generate consumer ratings and adopt this to the Merton model to derive the initial probability transition matrix. Initial consumer rating is in accordance with industry practice using a credit score sheet backed by the logit model. The banking credit function could espouse the study results to fulfill regulatory credit risk capital requirements for consumer loans in line with the Central Bank of Kenya Prudential Risk Guidelines or banks in other jurisdictions compliant with the Basel banking framework.
dc.description.abstractBased on simulations of implied values for credit worthiness over a period of 5 years for 1000 consumers, we establish a case for the semi-markov models as a proxy for internal credit risk models for a portfolio of consumer loans. With ample calibration, we prove the robustness of the semi-markov models in forecasting probabilities of default and loss given default. With a view of credit risk as a reliability problem, we generate credit risk indicators as qualifications of adequacy of a loan portfolio. This informs prospective holding of capital based on forecast delinquencies as opposed to the current retrospective practice that relies on the trigger event of default. We use Monte-Carlo simulation techniques to generate consumer ratings and adopt this to the Merton model to derive the initial probability transition matrix. Initial consumer rating is in accordance with industry practice using a credit score sheet backed by the logit model. The banking credit function could espouse the study results to fulfill regulatory credit risk capital requirements for consumer loans in line with the Central Bank of Kenya Prudential Risk Guidelines or banks in other jurisdictions compliant with the Basel banking framework.
dc.identifier.urihttp://hdl.handle.net/11071/3845
dc.languageeng
dc.rightsBy agreeing with and accepting this license, I (the author(s), copyright owner or nominated agent) agree to the conditions, as stated below, for deposit of the item (referred to as .the Work.) in the digital repository maintained by Strathmore University, or any other repository authorized for use by Strathmore University. Non-exclusive Rights Rights granted to the digital repository through this agreement are entirely non-exclusive. I understand that depositing the Work in the repository does not affect my rights to publish the Work elsewhere, either in present or future versions. I agree that Strathmore University may electronically store, copy or translate the Work to any approved medium or format for the purpose of future preservation and accessibility. Strathmore University is not under any obligation to reproduce or display the Work in the same formats or resolutions in which it was originally deposited. SU Digital Repository I understand that work deposited in the digital repository will be accessible to a wide variety of people and institutions, including automated agents and search engines via the World Wide Web. I understand that once the Work is deposited, metadata may be incorporated into public access catalogues. I agree as follows: 1.That I am the author or have the authority of the author/s to make this agreement and do hereby give Strathmore University the right to make the Work available in the way described above. 2.That I have exercised reasonable care to ensure that the Work is original, and to the best of my knowledge, does not breach any laws including those relating to defamation, libel and copyright. 3.That I have, in instances where the intellectual property of other authors or copyright holders is included in the Work, gained explicit permission for the inclusion of that material in the Work, and in the electronic form of the Work as accessed through the open access digital repository, or that I have identified that material for which adequate permission has not been obtained and which will be inaccessible via the digital repository. 4.That Strathmore University does not hold any obligation to take legal action on behalf of the Depositor, or other rights holders, in the event of a breach of intellectual property rights, or any other right, in the material deposited. 5.That if, as a result of my having knowingly or recklessly given a false statement at points 1, 2 or 3 above, the University suffers loss, I will make good that loss and indemnify Strathmore University for all action, suits, proceedings, claims, demands and costs occasioned by the University in consequence of my false statement.
dc.subjectSemi-Markov Models
dc.subjectCentral Bank of Kenya
dc.titleSemi-Markov credit risk modeling for a portfolio of consumer loans in the Kenyan banking industry
dc.typeConference Paper
Files