Statistical Default Models and Incentives

The likelihood that a bank loan will default is of interest to both regulators and investors. Under the Basel II regulatory guidelines, a bank must hold capital in proportion to the riskiness of its assets. The probability of default is a pri mary determinant of the riskiness of a loan. Investors, in turn, price a loan in the secondary market based on its expected cash flow, which again depends on the default probability. How should market participants assess the default probability on a pool of bank loans? It is natural to consider historical data on loan condi tions and default rates, and to estimate a statisti cal model that can be used to predict defaults going forward. Such statistical models have been widely used across the financial markets, to enhance market liquidity and impose capital requirements on financial institutions. The accuracy of predictions from statistical models was especially poor in the subprime mortgage market in the period from August 2007 onwards.1 We argue that one cause for this failure was that these models relied entirely on hard information variables and ignored changes in the incentives of lenders to collect soft infor