For bankers, analysts, investors and regulators (and to a lesser extent borrowers), loan losses at lending institutions are an all-important focus. Loan losses, with considerable volatility and uncertainty, hit the pre-tax profit line directly and are one of the most significant determinants of overall profitability.

Proper credit analysis, structuring and monitoring are clearly necessary parts of good management of the credit process, but is sufficient focus also being given to appreciating the return side of the risk-reward trade-off? How do we know whether the deals we book today will over time be seen as the right decisions from a pricing perspective? Will loan sales, securitizations or credit derivatives be the right portfolio management avenues to pursue, and if so, when?

The Office of the Comptroller of the Currency, in its annual survey of credit underwriting practices during the first quarter of 2005 reported increased easing of commercial credit underwriting standards. The survey includes the 71 largest national banks and covered the previous 12 month period. At the same time, interestingly, responders to the survey reported that credit risk had increased during the past year and a further increase in credit risk is expected during the next 12 months.

Specifically, there were eased standards for all of the commercial products except agriculture loans, with significant easing in real estate loans. Rapid appreciation of real estate assets and the vulnerability to interest rate increases should raise concern.

The reason most often cited for this easing was competition, with risk appetite and market strategy as other responses. The most common method of easing is pricing; adjusting of covenants increasing the amount of the credit line, lengthening maturity and changing collateral requirements were other methods quoted.

It is not surprising that lending standards are being eased as banks have enjoyed several quarters of healthy profits, and loan growth in 2Q05 was seen as the second highest quarterly total ever. But could we be forgetting that not long ago, the lending markets were full of red ink from problems in the telecom, tech and energy markets?

Proper management of risk requires that lenders pass on credits that are too thinly priced, as returns will not be commensurate with risk. This is more of an urgent matter in times of a favorable lending environment and narrowing credit spreads, as we are experiencing today. Recognizing the early warning signs and red flags on the quality of individual credits is a challenging enough task, yet the shifting impacts of the macro picture tends to compound the issue. With market demands to deliver shareholder returns and capital constraints, lenders must be choosy, and in light of their opportunity costs, perhaps consider where spreads have been and where they might be.

The OCC commentary refers to the old adage “the worst of loans are made in the best of times”. I say to anyone who wants to hear: “you can’t compete against bad banking”.

We may not be able to avoid loan losses, but understanding where we are in the credit cycle will help us to anticipate these changes and not only avoid wide swings and large provisions, but perhaps also improve our yields on an on-going basis

Special thanks to our credit SME, Henry Pullman, for contributing this issue's timely topic. Interested in having Henry deliver credit training to your group?

Figure 1: Normal Distribution vs. Leptokurtotic Distribution

Volatility Smile
If more price action occurs at these extreme levels than suggested by the model, the option trader will mark volatility higher to account for the increased probability of the underlying trading in the “fat tails”. The end result is that implied volatility will be higher for out-of-money (OTM) and in-the-money (ITM) options then at-the-money (ATM) options. If there is no bias in market expectations of the underlying price, then the picture of volatility is symmetrical around the at-the-money volatility. This is commonly referred to as the “volatility smile”. Notice in Figure 2 how the volatilities are symmetrical around the ATM forward.

Figure 2: Theoretical Volatility Smile – USD/CAD

Figure 3: Volatility Skew - USD / CAD Source: Superderivatives

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