Fair value accounting has been a hotly debated topic during the recent financial crisis. Supporters argue that fair values are more relevant to investors, while detractors point to the measurement error in the estimation of the reported fair values to attack its reliability. This study examines how noise in reported fair values impacts bank capital adequacy ratios. If measurement error causes reported capital levels to deviate from fundamental levels, then regulators could misidentify a financially healthy bank as troubled (type I error) or a financially troubled bank as safe (type II error), leading to suboptimal resource allocations for banks, regulators, and investors. We use a Monte Carlo simulation to generate our data, and find that while noise leads to both type I and type II errors around key Federal Deposit Insurance Corporation (FDIC) capital adequacy benchmarks, the type I error dominates. Specifically, noise is associated with 2.58 (2.60) [1.092], 5.67 (6.44) [1.94], and 10.60 (26.83) [3.423] times more type I errors than type II errors around the Tier 1 (Total) [Leverage] well-capitalized, adequately capitalized, and significantly undercapitalized benchmarks, respectively. Economically, our results suggest that noise can lead to inefficient allocation of resources on the part of regulators (increased monitoring costs) and banks (increased compliance costs).
JEL Classifications: D52; M41; C15; G21.