Improving overnight loan identification in payments systems

Information on the allocation and pricing of over-the-counter (OTC) markets is scarce. Furfine (1999) pioneered an algorithm that provides transaction-level data on the OTC interbank lending market. The veracity of the data identified, however, is not well established. Using permutation methods, I estimate an upper bound on the daily false positive rate of this algorithm to be slightly above 10%. I propose refinements that reduce the bound below 10% with negligible power loss. The results suggest that the inferred prices and quantities of overnight loans do provide viable estimates of interbank lending market activity.