RDP 2016-11: Identifying Interbank Loans from Payments Data 1. Introduction

The average interest rate on unsecured overnight interbank loans – the cash rate – is the Reserve Bank of Australia's (RBA) operational target for monetary policy. Moreover, the market for these loans (known as the interbank overnight cash (IBOC) market) is the market most directly affected by the RBA's standing facilities, and the RBA has control over the supply of funds in this market.^[1] So this market represents the first step in the transmission of monetary policy to the rest of the economy, meaning its smooth operation is critical for the financial and economic system. Consequently, it is important for the RBA to have detailed knowledge about this market.

While central banks have a wealth of information on interbank payments, there has historically been no in-built ability to distinguish the payments that constitute loans from other types of payments (other payments include those between banks' customers and foreign exchange transactions).^[2] Moreover, other sources of information about these loans do not typically include the level of detail that would be available in a loan-level dataset. For example, the RBA's Interbank Overnight Cash Rate Survey (IBOC Survey) only provided the aggregate gross value of each survey participant's IBOC lending and borrowing during each trading session of the day, and the average interest rate at which these loans occurred.^[3]

To garner detailed information on IBOC loans, algorithms have been developed to identify the interbank payments that constitute these loans. These algorithms are typically based on the seminal work of Furfine (1999), but have since been extended along several dimensions (see Demiralp, Preslopsky and Whitesell (2004) and Hendry and Kamhi (2007), for example). Given the mixed success of these algorithms (see Appendix A for details), we build on this literature to design a new algorithm for use in Australia (detailed in Section 3).

The main innovation of our algorithm is the ability to identify ‘rollovers’. A rollover occurs when, instead of repaying a loan from the previous day, the lender and borrower agree to a new IBOC loan. The lack of next-day repayment makes rollovers difficult to separately identify from longer-term loans. As a result, even though previous international research has discussed the possibility of rollovers (e.g. Furfine 1999; Armantier and Copeland 2015; Arciero et al 2016), existing algorithms do not attempt to identify them.

Identifying rollovers introduces complications beyond the lack of daily repayments. For example, borrowers could pay interest on a daily basis even though the principal of the loan is rolled over, or all interest could be paid when the loan ceases to be rolled over (with interest not necessarily included in the same payment as the principal). Interest may also be calculated in different ways (i.e. simple or compound). Our algorithm allows for all of these possibilities.

Perhaps the biggest innovation of our algorithm is the ability to identify rollovers for which only part of the principal is rolled over, or for which the principal is increased. In this case, daily payments only reflect the change in principal, and the dollar value of daily interest varies over the rollover period (with the changes in principal). For reasons that will be explained in Section 3, identifying this type of rollover requires an algorithm that is fundamentally different to a Furfine-type algorithm.

By comparing aggregates of the IBOC loans identified by our algorithm to IBOC Survey data, we conclude that our algorithm successfully identifies the majority of IBOC loans. Between 2005 and 2015, the daily correlations between the IBOC Survey data and the algorithm output are greater than 90 per cent (increasing to 96 per cent for the four major Australian banks). As is discussed in Section 4, such accuracy is uncommon in the existing literature, which may be partially due to the inability of existing algorithms to identify rollovers (the daily correlations in our sample drop below 40 per cent if rollovers are excluded). So the novel features of our algorithm may also be useful for identifying overnight interbank loans in other countries.

The loan-level dataset produced by our algorithm contains information on counterparties, loan values, interest rates, time of settlement, time of repayment, rollover length, and repayment structures. With the most detailed IBOC market dataset ever constructed now at our disposal, we explore several features of the market that were previously unobservable. The aim is to both improve our understanding of how the market functions, and help explain several of the changes that have occurred over the past decade, for example (see Section 5 for details):

• The halving of IBOC market activity between 2009 and 2015 mainly involved an 87 per cent fall in the value of rollovers (rollovers accounted for almost half of the market before the fall), with the value of non-rolled loans being little changed.
• 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). Knowing the timing of loans is important for operational decisions made by the RBA (such as the timing of open market operations).
• Banks are currently conducting non-rolled lending during a briefer period (towards the end of the day) than has been the case historically. And, despite significant changes to the market resulting from the introduction of same-day settlement of direct entry obligations in November 2013, there is little evidence that these changes affected the timing of non-rolled IBOC loans.
• Simple interest was used to calculate the repayments of rollovers, even though daily interest typically remained outstanding for the life of the rollover.

The main advantage of this new loan-level database is the ability to conduct previously infeasible analyses of the IBOC market, such as forthcoming analysis of how the market changed during the global financial crisis.

Footnotes

Although some non-banks are able to participate in the IBOC market, the majority of participants are banks. For simplicity, this paper refers to all participants as ‘banks’. [1]

Section 2 describes Australia's high-value payments system (known as the Reserve Bank Information and Transfer System, or RITS) and the IBOC market. [2]

From May 2016, the RBA has required banks to identify IBOC-related transactions in RITS (for more information, see RBA (2016)). [3]