Abstract
The misestimation of rating transition probabilities may lead banks to lend money incoherently with borrowers’ default trajectory, causing both a deterioration in asset quality and higher system distress. Applying a Mover-Stayer model to determine the migration risk of small and medium enterprises, we find that banks are over-estimating their credit risk resulting in excessive regulatory capital. This has important macroeconomic implications due to the fact that holding a large capital buffer is costly for banks and this in turn influences their ability to lend in the wider economy. This conclusion is particularly true during economic downturns with the consequence of exacerbating the cyclicality in risk capital that therefore acts to aggravate economic conditions further. We also explain part of the misevaluation of borrowers and the actual relevant weight of non-performing loans within banking portfolios: some of the prudential requirements, at least as regards EMS credit portfolios, cannot be considered effective as envisaged by the regulators who developed the “new” regulation in response to the most recent crisis. The Mover-Stayers approach helps to reduce calculation inaccuracy when analyzing the historical movements of borrowers’ ratings and consequently, improves the efficacy of the resource allocation process and banking industry stability.
Highlights
Credit risk transition probabilities are the key to improving forward-looking risk management for investors and commercial banks
We find that the rating trajectory cannot be estimated with a pure Markov chain without incurring the risk of an absorbing state which stands for a bankruptcy
Banks are over-estimating their credit risk resulting in excessive regulatory capital
Summary
Credit risk transition probabilities are the key to improving forward-looking risk management for investors and commercial banks This is true for listed bonds whose risk is generally estimated by rating agencies (Lando and Skødeberg 2002; Gabbi and Sironi 2005; D’Amico et al 2016), and for loans that are more frequently analyzed through banks’ internal models. Our paper presents the originality of comparing all these factors simultaneously in order to assess how they can influence banks’ willingness to grant credit even in times of crisis To address these issues in depth, we focus our analysis on transition matrices applied to credit risk which show the pattern of changes for different borrowers over time from one rating notch to another. Our contribution differs from existing research because it estimates how the estimation of the transition matrices of SMEs in relatively stable periods can be perfected by banks by replacing the pure Markovian method with the movers-stayers approach.
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