Case study by Global Evolution
Global Evolution is a specialist emerging and frontier markets investment manager. Our ESG sovereign screening and the Global Evolution ESG Research Programme is a closely integrated part of our investment process.
Why ESG dynamics matter for sovereign risk analysis
Our regression analysis shows that credit ratings explain 38% of the difference in spreads across sovereign bonds, but that when combined with ESG dynamics (the change in ESG ratings over time), the explanatory power rises five percentage points to 43% – a 13% increase that represents a clear business case for considering ESG dynamics as part of the investment process. Adding the static ESG ratings themselves, rather than the changes in the ratings over time, generates no statistically significant effect: it is specifically the improvement or deterioration in countries’ ESG performance that credit ratings are not sufficiently capturing.
How we measure ESG dynamics’ impact on sovereign risk analysis
We calculate how many notches the credit ratings would have to be adjusted by to appropriately account for ESG dynamics. We call this adjustment the ESG Risk Factor.
The ESG adjustment per country is estimated by an ordinary least squares (linear) multiple regression: s = α + β1CR + β2ESG, where s is the spread, CR is the credit rating, ESG is the ESG dynamics and and are regression coefficients. (Both explanatory variables are statistically significant at maximum 2% (p-value); a small variance inflation factor indicates no multicollinearity).
Isolating CR in the regression (CR = β2/β1ESG – (α + s)/â1), identifies the effect on credit ratings from ESG dynamics as β2/β1ESG. Multiplying β2 and ESG shows the effect of ESG dynamics on spreads.
About the data: spreads are measured over Libor; credit ratings are from S&P, Moody’s or Fitch; ESG dynamics are expressed as percentage changes in ESG ratings (from 2013 to 2014) and are based on a weighted average of five risk indices, split across environmental, social and governance factors.
Example: Emerging markets
The following chart shows the Global Evolution ESG Risk Factor for a set of emerging markets countries:
Taking Ghana’s GH23 bond as an example, the model shows its B- credit rating should be about half a notch higher to take into account ESG improvements. The model simulates that Ghana’s ESG rating improvement of approximately 1% over the 2013/14 period has caused the spread to drop 0.14 percentage points. This spread-compression is not accounted for by the credit rating – so taking ESG dynamics into account better accounts for spreads.
At the extremes of the scale are Bolivia’s bond BO23 and Kazakhstan’s bond KZ24. Bolivia’s BB bond should be rated more than two notches higher to account for ESG improvements in the 2013/14 period, and its spread has dropped 1.26%. Kazakhstan’s BBB bond should be rated more than two notches lower to account for ESG deteriorations, and the spread has widened 1.34%.
Further research will analyse: the effect of sovereign ESG dynamics on different asset classes; forward-looking ESG dynamics; which specific ESG factors correlate most closely with spreads; the effect of different weightings between ESG indices in creating the ESG ratings from which ESG dynamics are measured; more sophisticated modelling than simple ordinary least squares regressions.
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