The credit channel assigns banks a pivotal role in the transmission of monetary policy, which stems from the notion that financial markets are characterized by imperfections. Banks are special in extending credit to borrowers that cannot access other types of credit because of their expertise in mitigating financial frictions. If banks adjust their loan supply following a change in the stance of monetary policy, this has a bearing on real activity, since some borrowers have to rearrange their expenditure decisions.
As Bernanke and Gertler (1995) and Hubbard (1995) point out, the credit channel is working in addition to the interest rate channel, according to which monetary policy affects the level of investment and consumer spending by inducing changes in the cost of capital and yield on savings. Although, the credit channel and the interest rate channel diverge in assessing the relevance of financial considerations, they are deemed complementary, with the implication that monetary policy can be effective through these transmission channels simultaneously.
The probability approach to uncertainty and modeling is applied to default probability estimation. This issue has attracted attention as banks contemplate the requirements of Basel IIjs IRB rules. Nicholas M. Kiefer proposes the fomal introduction of expert information into quantitative analysis. An application treating the incorporation of expert information on the default probability is considered in detail.
Estimation of default probabilities (PD), loss given default (LGD, a fraction) and exposure at default (EAD) for portfolio segments containing reasonably homogeneous assets is essential to prudent risk management as well as for compliance with Basel II rules for banks using the IRB approach to determine capital requirementsBasel Committee on Banking Supervision (2004). Estimation of small probabilities is tricky, and I will focus on estimating PD. This problem has attracted considerable recent attention; see Basel Committee on Banking Supervision (2005), Balthazar (2004), BBA, LIBA, and ISDA (2005),and Pluto and Tasche (2005). This is an application in which data information is scarce while expert information is available. The focus of this paper is on estimation of the default probability for a risk bucket on the basis of historical information and expert knowledge. Section 2 argues for the probability approach to uncertainty measurement. The probability approach to default modeling is uncontroversial, although perhaps the extent of the constraints imposed by the simple independent Bernoulli model are under appreciated. This model is briefly described in Section 3.
This study examines the extent to which management’s intent to mislead investors affects the probability that CEOs and CFOs are terminated around restatement announcements. Our research is motivated by the fact that, although recent studies report that executive turnover rates around restatements are statistically significantly different from turnover rates in a control sample of non-restaters, some question whether the observed turnover rates are too low.
Abelson, for example, is one member of the press who comments that when managers are “manipulating a company’s financial numbers to mislead investors, the punishment is often anything but sharp and swift” 1996). Further, in a recent study on restatements, Collins, Reitenga, and Sanchez-Cuevas (2005) conclude that “half the sample appear to have taken little or no action to penalize management.”
