Micro Loan Portfolio Equilibrium Mix: A Markov Chain approach.

Statistics

Credit risk (loan) management has been the priority of almost all Financial Institutions in recent years. The interest lies as to whether the financial institution will be able to meet the demands of their potential clients whereas clients are expected to meet their short term or long term loan obligation. In view of this an optimal loan allocation mix policy from the steady State distribution of loan disbursement process is presented in this study. The objectives of the study are to (i) obtain an optimal loan allocation mix policy (ii) to estimatethe transition matrix using time series data on loans .iii) To find out whether risk can change or is likely to change due to portfolio changes.[1] Monthly data on actual loan Disbursement of four loan types for a period of twenty-four monthsis analyzed. An estimated Transition probability matrix is analyzed using the Markov chain approach. It is from this that the transition probability matrix and the steady state distribution of loan disbursement process are obtained. The loan types disbursed by the financial institution include Agricultural, Susu, Small and Medium Enterprise (SME) and Salary loans. The estimated transition matrix showed that the probability of loan switching from Agricultural loan to Small and Medium Enterprise (SME) loan is the highest (0.54) while loan switching from Salary loan to Agricultural loan is the lowest (0.034). Probability of no loan switching for Susu loan is (0.380), Probability of no loan switching for SME loan is (0.52), and whiles that of Salary is (0.044). From the estimated probability transition matrix, the steady state distribution indicated that in the long run, SME loan constitutes 52.36% of the total funds allocated for loans. by Agricultural loan 38.17%, salary loan 4.95% and Susu loan 3.76%, of the total loan amount.

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