Credit Risk Concentration and Its Effect on Savings and Load

OTC derivatives broker-dealers perform measurement and limitation of credit risk not only to private counterparties, but also based on geographic regions, industry groups, and sometimes other categorizations measures. For instance, the Derivatives Policy Group in the year 1995 recommended measurement and disclosure of credit exposure risk by geographic location, industry, and credit rating. Aside from one guesses that such classifications define groups that might act in concert to take benefit of their private information, it may not be obvious that discussion of loans to a privately informed borrower why one wishes to measure limit credit risk concentration by groups. Here again, however, adverse selection can play a role.

For instance, let say that different information have been given to different bank about default risk in real estate industry. Then each banks set rate naively, based on their own estimation of the expected rate of default loss. Several banks then will set lower default loss based on estimation that others bank will set interest rates lower than them. In such situation, borrowers then choose bank with the most appealing rates. Yet they have no information (privately) about their own credit quality. Banks which offered lowest default loss estimate will consequently end up bidding an uncommon big number of loans to real estate developers. In the end, these banks will therefore endure an anticipated loss on their real estate loans. But the condition could be reverse if interest rates are brought up to cancel this winner’s curse. This situation is one form of adverse selection.

Making the presumption that other banks are lucky or smart to have in the positions of higher estimates default risk, the former bank will attempt a disproportionate share of loans with expected default loss. In this circumstance, the point of adverse selection is that, the averages means does not offer any protection at all.

Banks can use both borrowing rates and limit credit risks concentration , by area of concentration to reduce the impact of the winner’s curse. A full analysis would involve modeling equilibrium in loan markets with correlated default among borrowers and with asymmetrically informed lenders. This is beyond our objectives here.

Credit risk concentrations is indeed need to be measure, this is to provide information for the coordination of lending business. Suppose that loans to a particular industry appear to be profitable. Monitoring the level of profitability against the aggregate credit risk is needed, this is to make assessments regarding the allocation of the firm’s capital to loans made to firms in that industry. Measures of credit risk concentration can also be useful for crisis direction. Let say, a major news release, say a natural catastrophe or war, may actuate concern over default losses in a some particular area of credit risk concentration.

For example, it has often been known that the U.S. savings and loan debacle of the 1980s was a consequence of giving savings and loan institutions access to extensive credit through federal deposit insurance, while simultaneously not enforcing sufficient limits on the riskiness of savings and loan investments. This furthered some savings and loan owners to take on highly levered and risky portfolios of long-term loans, mortgage-backed securities, and other risky assets. If these “big bets” turned out badly and as they ultimately did in several cases, and if a savings and loan institution failed as consequence, its owners disappeared. Whenever the big bets paid off, the owners of the savings and loan institution earned large gains.

It is apparent, that an obvious defense against the moral hazard brought on by offering large loans to risky borrowers is to limit access to credit. The same story applies, in effect, with OTC derivatives. So, it makes sense, when analyzing the probability distribution of credit exposure risk on an OTC derivative to use appraises that place special emphasis on the largest potential exposures, allowing some justification for high-percentile credit exposure risk measures.

Large borrowers in default are frequently in a better bargaining position and can thereby extract luckier terms for failure or restructuring than can little small borrowers. This could, on average, bring down the profitability of larger loans, putting away fixed costs for arranging loan arrangements. In bank circumstances, a derivatives broker-dealer may wish to fix the extent of credit exposure risk for special counterparties. Large of credit risk concentration, for example, in the 1980s with Latin American countries, might also induce coordinated responses by groups of defaulting borrowers, again cutting the bargaining power of the lender.