Black-Scholes Style Credit Models Won't Survive Basel III

Any bank stuck in an old-school Black-Scholes mode of credit risk modeling or relying on credit ratings had better change its ways before Basel III gets adopted in the U.S., according to David Hamilton, managing director of Quantitative Credit Research at Moody's Analytics. Outdated or inaccurate modeling could hinder a bank's efforts to set aside the right levels of capital reserves under the Basel recommendations. And credit ratings don't change that often and therefore are not the best measure of probability of default.

"The original expected default frequency model is an application of the Black-Scholes-Merton view of credit risk that's now three decades old," Hamilton notes. The expected default frequency model many banks use has been around for two decades and is a point-in-time model, Hamilton says. "Every day it's recalculating the probability of default based on the most recent data available, which often is equity market information," he says. "That point-in-time view takes into account everything that's relevant for evaluating a firm's probability of default. It includes the kitchen sink — not just the firm's own long-term credit risk, but macroeconomic factors, macro-credit factors, industry effects, and geographical effects."

But the Basel III accord calls for risk measures that consider cycles rather than a point in time, Hamilton explains. The new method of measurement lets banks get a more accurate signal about how their capital reserves should change over time and whether the environment is becoming more or less risky. "Regulators do not want banks to use probabilities of default inputs into their capital model that will result in procyclical capital," he says. "That means in a boom period you'll have relatively low capital. In a downturn, your provision for capital goes up as risk factors go up. That's a problem because in a downturn, capital also gets more expensive."

A new "through-the-cycle" expected default frequency model Moody's introduced this week uses a cyclical risk measure for the purpose of calculating capital. It covers more than 30,000 firms on a given day, whereas ratings systems cover around 6,000 firms.

The new model, like its predecessor, attempts to measure two risk factors. One is the financial risk of a firm, based on its financial statements, market leverage, capital structure, and outstanding debt. "The more highly leveraged a firm is, the higher its probability of default is going to be, all else being equal," Hamilton notes.

The model also tries to measure business risk by measuring the volatility of a firm's equity. "A firm in an industry like technology, for example, will be much more volatile than a public utility because the technology industry is more risky," Hamilton says.

Credits that can be assessed under the new model have to have a quoted market price of some kind. This includes the loans of companies on listed exchanges as well as corporate, sovereign and municipal debt for which there are credit default swap spreads or bond prices available to infer an implied rating or a probability of default. "The market is a processor of information that doesn't care about old news," Hamilton says. "It tends to be forward-looking. So even though the model takes into account today's data, implicit in that data is a forward looking view."

Medium-size banks are the most likely candidate for the new model, Hamilton estimates. "Presumably the largest, most sophisticated banks have tackled this on their own," he says. "They clearly have the resources to do it. It's the more medium size banks that would find this an attractive solution. They don't have the resources of lots of quants to read through the cycle and make conclusions about creditworthiness.

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