* General modeling procedure
* Credit risk models as a risk management tool
* Looking ahead
In the past few years, there have been several developments in the field of modeling the credit risk in banks' commercial loan portfolios. Credit risk is essentially the possibility that a bank's loan portfolio will lose value if its borrowers become unable to pay back their debts. Arguably, credit risk is the largest risk faced by commercial banks, since loans and other debt instruments constitute the bulk of their assets. In the U.S., loans made up over 60% of total banking assets at year-end 2000, and fixed-income securities made up an additional 14%. These credit risk models are becoming widely accepted by banks for various purposes; in fact, bank supervisors, including the Federal Reserve, have recently proposed new risk-based capital requirements based partly on such models. This Economic Letter provides a brief survey of how these models are constructed and used for credit risk measurement and management. General modeling procedure
Commercial banks have been using credit risk models for their mortgage and consumer lending for decades. These credit risk models, typically known as credit scoring models, were first developed for consumer lending because of the large number of borrowers and their detailed credit histories. In contrast, there are many fewer commercial borrowers, and it is only within the last few years that credit risk models for commercial loans have been successfully created, marketed, and integrated into banks' risk management procedures. Although a reasonable variety of such models exists, all of them are constructed generally on three standard procedural steps. The first step is to choose the type of credit risk to be modeled. "Default" models simply estimate the probability that a borrower will default; that is, the borrower will not make any more payments under the original lending agreement. In contrast, "multi-state" (or "mark-to-market") models estimate the probability that the borrower's credit quality will change, including a change to default status. For example, a multi-state model forecasts the probabilities of whether a B-rated borrower will remain B-rated, will become an A-rated or a C-rated borrower, or will default. Obviously, default models are a special case of multi-state models and are being used less frequently by banks. An important element of this choice is the horizon over which credit losses are measured. For example, a borrower's credit quality may change several times before a default, and a default model would not be able to capture these changes. Many options are available to the user, but common practice has settled on a one-year horizon, which is shorter than the maturity of many commercial loans. This relatively short horizon is due partly to modeling convenience and partly to the increasing liquidity of the secondary loan market and the credit derivatives market. Both of these markets permit banks to hedge (i.e., decrease) their credit exposure to a particular borrower or class of borrowers. The second step is to determine the probability of each credit state occurring and the value of a given loan in each of them. In default models, there are two credit states: the credit is simply paid off completely, or it is worth a recovery value in case of default. In multi-state models, the loan's value in each possible credit state is frequently assessed by referencing credit spreads derived from the corporate bond market. The state probabilities can be calculated in several ways, such as from simple historical experience in the corporate bond market or from models using data from the public debt and equity markets. The combination of the estimated values of a loan in the different states and the estimated probabilities of the states determine the credit loss distribution for that loan. A key element of these...