Migration Analysis of Credit Risk in Tunisian Banking Sector

Abstract

In this paper, credit migration matrices are built to measuretransition probabilitiesat Tunisian credit institutions, allowing a comparison of credit risk quality shiftsfor public banks, private banks and leasing companies. We proposeto apply estimating Markov transition matrices using proportions data in order to be adapted to the scarcity of individual dataonloan quality transitions. We employ annual classification of assets issued in theregistration documents and annual financial reports during 2003-2014 period.It’s found from the analysis that the risk grade 2 has the greater tendancy to be downgraded than to be upgraded in public banks and in leasing companies.For the other risk grade 3, the upgradation in the category is higher than the downgradation in all cases. The resultsindicate that the public banks are the riskiest credit institution in Tunisia and there is a lack of rigor in loan classification inpublic and private banks. The findings are useful and critical for supervisory purposes and foroptimizing bank credit risk management.