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Implied Ratings: How Treasury Can Manage Credit Risk

  • By David Blair
  • Published: 7/7/2015

Ratings from Standard & Poor’s, Moody’s and Fitch were once core to treasurers’ counterparty credit risk management. The global financial crisis showed us painfully that these ratings were not reliable. But it is neither practical nor desirable for treasurers to do their own ratings. Implied ratings provide a reasonable solution to this conundrum.

After the global financial crisis, large treasury functions started to do their own credit analysis. The volume of data to be analyzed is mind-numbing. When regulators, agencies, and most professional investors cannot understand banks, what realistic chance does a small treasury team have?

Some turned to credit default swap (CDS) spreads, which respond quickly to news impacting institutions’ creditworthiness. But CDS are also trading instruments—they exhibit a volatility that far exceeds the real world changes in credit quality. Their volatility makes them impractical for corporate treasury departments who need a measure of continuity for operational effectiveness.

Some large treasuries started to create indexes including things like leverage and other reported ratios, stock price, CDS spreads, etc. The goal is to derive a more stable value that is still more responsive than agency ratings.

But why reinvent the wheel? Such indexes already exist. They are called implied ratings. And treasurers with market data services already have access to them for free, or at no extra cost. Thomson Reuters includes the SmartRatios Credit Risk Model (SCR) in its basic terminal access package, and Bloomberg has an implied rating called CRAT. The rating agencies also have similar models—Moody’s bought KMV, and S&P uses slightly different math.

Implied ratings

Implied ratings combine diverse data to derive a credit score. I will illustrate this with the example of SmartRatios Credit Risk Model, and treasurers could obtain similar results using models from the other companies.

Implied ratings have a long academic and market history including metrics like KMV and z-scores. Modern technology and market bandwidth have enabled increasing richness in raw data and sophistication in the models themselves.

SCR builds on these antecedents to provide forward-looking estimates of credit risk that generally anticipate agency rating changes. Experience with the collapse of Lehman Brothers shows that SCR indicated problems two months before the rating agencies. In fact, Lehman Brothers was still rated “A” by the agencies when it collapsed.

(Source: Thomson Reuters)

SCR uses a third generation model to calculate risk using a wide array of accounting ratios that are predictive of credit risk. The SmartRatios Model uses several sets of metrics, some generic and some industry specific, clustered around the following components:

  • Profitability
  • Leverage
  • Coverage
  • Liquidity
  • Growth and stability

In practice, SCR and other implied ratings are available from the same sources as agency ratings. Most treasurers will get them from their market data provider. These can easily be integrated with a treasury management system (TMS) or even Excel, though I do not recommend Excel for operational processes.  

The events of the last decade have made it clear that we can no longer blindly trust agency ratings. Boards and audit committees are more and more worried about security of cash, and treasurers need a practical solution at reasonable cost. This need not be a huge problem when we avail ourselves of existing and well-proven methodologies such as implied ratings. Not only have implied ratings been shown to be more reliable than agency ratings, they can be free and are easily available on your market data service.  

David Blair, is managing director for Acarate Consulting and formerly served as vice-president treasury for Huawei and group treasurer for Nokia. He is based in Singapore.

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