Implications of alternative operational risk modeling techniques

Quantification of operational risk has received increased attention with the inclusion of an explicit capital charge for operational risk under the new Basle proposal. The proposal provides significant flexibility for banks to use internal models to estimate their operational risk, and the associated capital needed for unexpected losses. Most banks have used variants of value at risk models that estimate frequency, severity, and loss distributions. This paper examines the empirical regularities in operational loss data. Using loss data from six large internationally active banking institutions, we find that loss data by event types are quite similar across institutions. Furthermore, our results are consistent with economic capital numbers disclosed by some large banks, and also with the results of studies modeling losses using publicly available “external” loss data.Bank capital ; Risk management ; Basel capital accord

The potential impact of explicit Basel II operational risk capital charges on the competitive environment of processing banks in the United States

Basel II replaces Basel I’s implicit capital charge on operational risk with an explicit charge. Certain U.S. banks concentrated in processing-related business lines – which have significant operational risk – could thus face an increase in overall minimum regulatory capital requirements. Some have argued that, as a result, these so-called “processing banks” would be disadvantaged vis-à-vis competitors not subject to regulatory capital requirements for operational risk. This paper evaluates these concerns.Bank capital ; Risk management ; Basel capital accord

INFORMATION DYNAMICS IN FINANCIAL MARKETS

A noisy rational expectations model of asset trading is extended to incorporate costs of information acquisition and expectation formation. Because of the information costs, how much information to acquire becomes an important decision. Agents make this decision by choosing an expectations strategy about the future value of information. Because expectation formation is costly, agents often choose strategies that are simpler (and thus cheaper) than rational expectations. The model s dynamics can be expressed in terms of the market precision, which represents the amount of information acquired by the average agent. Under certain conditions, market precision follows an unstable and highly irregular time path. This irregularity directly affects observable market quantities. In particular, simulated time series for return volatility and trading volume display a copersistence similar to that found in actual financial data.