RDP 2016-11: Identifying Interbank Loans from Payments Data Appendix A: Literature Review

The first part of our algorithm is based on the work of Furfine (1999) and other papers that have followed a similar procedure. A number of papers have assessed the accuracy of Furfine-type algorithms, with varying results.

Access to transaction-level data where interbank loans are flagged appears to be rare. Armantier and Copeland (2015) use data from two US banks that unilaterally insist on marking these transactions as loans, and find that the algorithm performs poorly: on average, 81 per cent of transactions identified as loans were not actually loans (false positives or Type I errors), while the algorithm failed to pick up 23 per cent of loan-related transactions (false negatives or Type II errors).

Guggenheim, Kraenzlin and Schumacher (2011) and Arciero et al (2016) use transaction-level data, from Switzerland and the euro area, respectively, to assess the accuracy of their algorithm. Although these studies are based on different markets, and are therefore not directly comparable, both report lower Type II error rates than Armantier and Copeland – 10 per cent and 1–9 per cent, respectively. However, while neither European study assesses Type I error rates at the transaction level, the euro area algorithm identifies well over 100 per cent of the daily overnight trading reported by EONIA panel banks (inter-quartile range of 120–160 per cent), suggesting that Type I errors may be a problem.

More commonly, papers use aggregated filings by banks at the end of the day (or end of the quarter) to assess the accuracy of their algorithms. These data are typically aggregated gross or net lending reported to the central bank for regulatory purposes or for data construction (akin to the RBA's IBOC Survey). For example, Akram and Christophersen (2013) find that their algorithm accurately identifies aggregate daily lending for the majority of the banks in their Norwegian sample (similar to our results), but that large discrepancies for a few banks cause an average absolute daily deviation (aggregating across all banks) of 20 per cent (ours is 13 per cent).

Interestingly, Kovner and Skeie (2013) compare this method of accuracy determination to that of Armantier and Copeland (2015), and find that they do not square – using quarterly aggregated regulatory filings suggests a higher degree of accuracy than transaction-level comparisons. There are many reasons why this may occur. For example, Armantier and Copeland only look at the accuracy of loans involving two banks, whereas Kovner and Skeie look at the entire federal funds system. Moreover, some banks may transact on behalf of other client banks, so some of the false positives found by Armantier and Copeland may actually be loans but are assigned to the wrong bank (the banking system aggregates would be unaffected).

A different approach was taken by Millard and Polenghi (2004), who assess the accuracy of a Furfine-type algorithm by comparing the daily average of implied interest rates across all identified loans with the daily SONIA rate. Based on this metric, their algorithm performs very well (a daily correlation of 97 per cent). However, while this suggests that false positives are unlikely to be a problem, this method cannot elucidate the volume of false negatives (e.g. these excluded loans may have the same average interest rate as the captured loans).

In Australia, Sokolov et al (2012) use a week (in February 2007) of RITS data and a Furfine-type algorithm to identify interbank loans. With only a week of data, their algorithm only allows for overnight and very short-term loans.[46] Given the high share of rollovers during this period, not accounting for rollovers leads to a high rate of false negatives (65 per cent of the IBOC loans identified by our algorithm).

By allowing non-rounded transactions to be loans, Sokolov et al may also be capturing secured overnight loans. This may be why, even with a high false negative rate, their identified overnight loans represent 126 per cent of the IBOC market.


While they do not discuss the possibility of rollovers, short-term loans would be indistinguishable from rollovers in their algorithm. [46]