Banks worldwide are implementing new approaches to measuring risk associated with the Environmental, Social and Governance features of their loan exposures. ESG adds an additional dimension of risk to the drivers that banks have traditionally considered: credit, market and operational.
This paper develops a new approach to modelling ESG and credit risk within a common framework. The technique involves modelling ESG and credit ratings as correlated Markov chains, expanding the classic Ordered Probit approach to credit portfolio analysis by including an additional metric of issuer ESG status.
The models proposed are implemented statistically using historical data on Refinitiv (ESG) and Moody’s (credit) ratings. The parameter estimations are performed using Maximum Likelihood techniques. The model allows for correlation between common factors driving credit and ESG ratings and for correlation between issuer-level idiosyncratic shocks.
Individual issuer ratings exhibit relatively low correlations (lower than those assumed in the Basel Internal Ratings Based Approach risk weights, for example). But a high and statistically significant correlation is evident between the common factors driving, respectively, credit and ESG ratings.
This suggests that, in a diversified bank portfolio, ESG and credit factors will jointly boost overall risk through their positively correlated common movements.
As a final exercise, we repeat the analysis but using E, S and G ratings (which we construct from the Refinitiv pillar scores) rather than the official Refinitiv ESG rating. This permits us to examine which aspects of the ESG ratings are correlated with credit ratings. In this, we find that the factor correlations are strongest between credit and Governance and lowest with Environment.
Research report available here.
You might also be interested in our strategy note on ESG Strategy for Banks: Tackling the Data Problem.
