RDP 2016-11: Identifying Interbank Loans from Payments Data 6. Conclusions and Future Research

The aim of this paper was to construct a loan-level database of IBOC loans to improve the information available to the RBA about how this pivotal market has historically functioned. To this end, we designed an algorithm to identify IBOC loans using a census of payments settled through Australia' high-value payments system.

While algorithms for this purpose already exist in the literature, we found that these existing algorithms failed to identify the majority of IBOC loans in Australia. This is because these algorithms do not attempt to identify rollovers.

Many rollovers were identified by extending existing algorithms to recognise the features of rollovers (such as principal repayments that do not occur the following day, and multiple days' worth of interest either paid in one transaction or over several days). The success of these extensions was aided by the low interest rate volatility in the Australian IBOC market, which allowed us to calibrate our algorithm with a smaller interest rate range than is typical in the literature (thereby minimising the frequency of false positives).

With existing algorithms only matching pairs of transactions (i.e. the original loan and the repayment), identification of rollovers that exhibit features more akin to a credit facility required the development of an algorithm that is fundamentally different to the Furfine-type algorithms that exist in the literature.

The combination of the extended Furfine-type algorithm and credit-facility algorithm was very successful in identifying Australian IBOC loans. Between 2005 and 2015, the daily correlations between the aggregated survey data and the algorithm output are greater than 90 per cent (increasing to 96 per cent for the major banks). Such accuracy is uncommon in the existing literature, which is potentially due to both the inability of existing algorithms to identify rollovers and the larger interest rate ranges required for algorithms used in other jurisdictions.

Using the loan-level database, we find that the halving of market activity between 2009 and 2015 mainly involved an 87 per cent fall in rollovers (rollovers accounted for almost half of the market before the fall). We also find that while both rollovers and non-rolled loans appear to be used to satisfy banks' late-day funding needs, they are not perfect substitutes, with rollovers conducted earlier in the day (when banks may be less certain of their late-day funding requirements).

Given the detail and accuracy of the algorithm output, previously infeasible research projects are now possible, such as forthcoming analysis of how the market changed during the global financial crisis. Moreover, the algorithm could be modified to capture secured or long-term interbank lending, providing the same level of detail about these markets as has been shown here for the IBOC market, and could also be adapted for use in other countries.