Probability of Default Estimation as a Credit Risk Parameter: A Markov Chain Approach Applied in Real Data

Abstract

Probability of Default (PD) is a crucial credit risk parameter. International accords have motivated banks and credit institutions to adopt objective systems of evaluating and monitoring the PD. This study examines retail unsecured loans of a major Greek bank during the period of the financial crisis. It focusses on the stochastic behaviour of the financial states of the loans. It is tested whether a first-order Markov chain (MC) model describes sufficiently the transitions from one state to another. Moreover, Poisson regression models are estimated in order to calculate the limiting transition matrix, the limiting state probabilities and the PD. It is proved that the MC of the financial states of loans is non-homogeneous suggesting that the transition probabilities from one financial state to another are not constant across time. From the Poisson regression models, the transition probability matrix is estimated from one state to another in alternative time periods. From the limiting transition matrix, it is shown that if a loan is delayed then it is very likely to move towards the next worst case. The findings of this research could be useful for bank management.

Citation

Ouranos, V. and Livada, A. (2022), "Probability of Default Estimation as a Credit Risk Parameter: A Markov Chain Approach Applied in Real Data", Grima, S., Özen, E. and Boz, H. (Ed.) The New Digital Era: Other Emerging Risks and Opportunities (Contemporary Studies in Economic and Financial Analysis, Vol. 109B), Emerald Publishing Limited, Leeds, pp. 151-176. https://doi.org/10.1108/S1569-37592022000109B010