By Sam Miller

Credit Metrics is a method of reigning in credit risk by modeling changes in credit ratings portfolio. This implies a propositional change in value of the holdings. Credit metrics tries to construct that is not readily observable, which is the volatility of value due to changing credit quality. This approach renders credit metrics more of an exercise in proposing models and which explain the changes in credit related instruments. More than often the models that best describe credit risk don’t rely on the assumption that returns distribution is imperative.

Credit metrics is basically a framework that helps to quantify credit risk on portfolio of everyday credit products. This includes loans, commitments to lend, and market -driven instruments which are vulnerable to counterparty defaults. The sound of knowledge of Credit metrics enables you get a transparent depiction of credit risk. Transparency and effective management share a direct proposition and usually goes hand in glove. The common crisis that has been plaguing the credit risk measurement is the absence of a common point reference. The multiple approaches to measure of credit risk render them practically incomparable.

Credit measure and Credit metrics are often misinterpreted to be the same. When we refer to a measure we are actually assigning a number to something. A metric on the other hand is how interpret that assigned number. A simple example would be that of calculating a person’s height. Let’s ay it measures to 5.1 inches, the inches is the measure of the person’s height and the, “height” is the metric.

Although credit metrics and risk metrics are similar in many ways they are not the same. The primary difference between the two is that risk metrics presents an loads of daily liquid pricing data which can be easily used to construct a model of conditional volatility. On the other hand credit metrics offers relatively less and sporadically priced data for constructing a model of unconditional volatility

The recovery of a claim remains unknown until an obligor defaults. Credit metrics on the other hand models recovery by using a beta distribution. A beta distribution is characterized by a mean and standard deviation. The recovery of the distribution is affected by changes in parameters as demonstrated by the beta distribution spreadsheet.

In credit metrics the changes in value is not only influenced by chancy default events but also by the upswings and downswings in credit quality. Credit risk also addresses the value-at-risk (VaR) which is basically the volatility of value and not just the expected losses. It makes sense to address the co-relation of credit quality fluctuation across obligors as it allows you directly calculate the potential over -concentration across the portfolio.

Modeling transitions for a single name is pretty simple. If one has an idea of the probability to each state, then he/she can approximately simulate a transition corresponding to each state by observing a random uniform variable. The transition can be made by basing on the outcome of the random uniform variable. The glitch is when there are multiple correlated names in the portfolio.

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