Do you think all Rating Agencies are equal?

Apparently, they are not!

From this report (HT: Marginal Revolution):

An S&P ratings seeks to measure only the probability of default. Nothing else matters — not the time that the issuer is likely to remain in default, not the expected way in which the default will be resolved. Most importantly, S&P simply doesn’t care what the recovery value is — the amount of money that investors end up with after the issuer has defaulted.

Moody’s, by contrast, is interested not in default probability per se, but rather expected losses. Default probability is part of the total expected loss — but then you have to also take into account what’s likely to happen if and when a default occurs.

This is something interesting to notice!

Also, from the same report, the author claims:

(...)country which has been downgraded to AA is a worse bet than a country that has been upgraded to AA: the former is much more likely to get another downgrade than it is an upgrade, while the latter is on an upgrade path and is more likely to get another upgrade than a downgrade.

Since I am a bit skeptical about this kind of claims, I search for a paper which calculates the transition matrix for sovereign credit ratings. I found Hu et al (2002) The estimation of transition matrices for sovereign credit ratings, Journal of Banking & Finance.

Even though the time period covered in the paper is from 1981-1998, I try to calculate the transition probabilities.

From table 3, using the SP transition matrix mentioned on the paper (this estimates are based on relative frequency), I calculated that the probability of a country which has been downgraded to AA and suffer another downgrade to A in the next period is 1.1%. The probability that this country goes back to AAA, instead of going to A, is 0.6%.

If you change for the Ordered Probit Model Estimates, the probabilities become 2.6% and 7.7% , inverting the order mentioned on the news above!The conclusion do not change using other tables: whenever you use S&P methodology, the probability of a country which has been downgraded to AA suffer another downgrade is higher than it would go back to AAA, if you use any of the authors´ estimators the direction changes.

One question that naturally arises is if the results hold with more recent data. Does anyone has any suggestion about sit?

Another question is if the model is correctly specified (no autocorrelation of the residuals and no heteroskedasticity: this would lead to INCONSISTENCY!). A robust analysis would be interesting to see.

The third important question is also if the frequency of changes is a good estimate. It is a robust estimator, know, but I think you can do better using the extra information on the covariates.

Hence, the affirmative mentioned on the news is not that obvious and still, there is space to research about it (what about a semiparametric estimator or even a nonparametric one, taking into account the covariates?)