Money market microstructure is fundamental to studying bank behaviour, to evaluating monetary policy and to assessing the financial stability of the system. Given the lack of granular data on interbank loans, Furfine (1999) proposed an algorithm to estimate the microstructure using data from the payment system. We propose an econometric methodology to assess and improve the quality of the money market microstructure estimated by the Furfine algorithm in the presence of zero and negative rates, exploiting information coming from market regularities. We first extend the standard Furfine algorithm to include negative rates and verify the presence of significant noise at a specific rate. Secondly, we propose an inferential procedure that enriches and corrects the standard algorithm based on the economic likelihood of loans. Market regularities observed in this decentralized market are used to increase the reliability of the estimated interbank network. Thirdly, the methodology is applied to TARGET2, the European wholesale payment system. The main impacts of recent monetary policy decisions on key interest rates are studied, comparing the standard algorithm with the new econometric procedure.
Published in 2019 in: Quantitative Finance, v. 20, 2, pp. 207-234
