Abstract
Based on simulations of implied values for credit worthiness over a period of 5 years for 1000 consumers, the study shows robustness of the Semi-Markovian models in forecasting Probabilities of Default and Loss Given Default for a portfolio of consumer loans. The study models credit risk as a reliability problem on the basis of which we generate credit risk indicators and quantify prospective capital holding based on forecast delinquencies. Consumer ratings are based on Monte-Carlo simulation techniques and the initial probability transition matrix on the Merton model. Banks could espouse the study results to fulfill regulatory credit risk capital requirements for consumer loans. Key words: Semi-Markov models, credit risk, Central Bank of Kenya.
Highlights
This study seeks to respond to the need for better credit risk modeling for a portfolio of consumer loans in the Kenyan banking sector
More realistic credit spreads are obtained from reduced form models (RFM) or intensity-based models (Linda, 2004). This holds since; whereas structural models view default as the outcome of a gradual process of deterioration in asset values/behavioral value, intensity-based models view default as a sudden, unexpected event, thereby generating probability of default (PD) estimates that are more consistent with empirical observations (Linda, 2004)
With considerable progress having been made in the area of modeling consumer credit risk, the use of RFMs to model credit spreads has been acclaimed as more realistic to other models
Summary
This study seeks to respond to the need for better credit risk modeling for a portfolio of consumer loans in the Kenyan banking sector. CBK (2013) notes in its March, 2013 Credit Report Survey that credit risk is the single largest factor affecting the soundness of financial institutions and the financial system as a whole and lending is the principal business activity for most banks. With the newly issued risk guidelines, CBK (2013), the Central Bank of Kenya identifies internal rating models for banks as being key for effective credit risk management. The default state can be seen as a down state and an absorbing state It is within this framework that Semi-Markov credit risk models become handy. The study’s results are of paramount importance to commercial banks, whose main business is credit creation, the regulator, CBK, as well as other corporate lenders, for instance corporate bond issuers
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