RDP 2018-09: Identifying Repo Market Microstructure from Securities Transactions Data 1. Introduction

Short-term interbank markets are at the core of most developed financial systems. They are the first resort for financial institutions (henceforth loosely termed ‘banks’) wishing to manage the day-today liquidity needs that arise from their business-related cash flows. Moreover, the interest rates banks charge each other in these markets have flow-on effects to other interest rates throughout the economy. This pivotal role is the reason central banks use these markets for enacting monetary policy. The Reserve Bank of Australia (RBA), like many other central banks, targets the rate in the unsecured interbank market for overnight loans, termed the ‘cash rate’.

Another key component of short-term interbank markets, besides the unsecured market, is the repo market. Unsecured loans involve movements of cash only, whereas repos (i.e. secured loans) involve simultaneous movement of cash and securities. Borrowers provide and receive back securities as collateral alongside their receipt and repayment of the cash that they borrow.^[1] The collateral reduces the risk to the lender – if a repo borrower defaults, the lender takes immediate ownership of the collateral, whereas if an unsecured borrower defaults, the lender joins other unsecured creditors with a claim on the borrower's assets. To minimise counterparty risk, the RBA uses repos when lending to private banks in open market operations (OMO).

Figure 1 plots available data on overnight interbank (i.e. non-RBA) loans in the Australian unsecured and repo markets. These data capture the market segments that are transacted through Australian infrastructure and exclude loans rolled over multiple nights. Although initially smaller, this repo market segment had grown to outsize the corresponding unsecured market segment by 2015.^[2] Similar patterns have occurred in other regions – between 2006 and 2015, unsecured turnover in the European money market declined from €14 trillion to €3 trillion, whereas secured turnover increased from €21 trillion to €29 trillion (ECB 2015).

Notwithstanding this, there is little work studying repo market data at the level of individual loans. Loan-level data are valuable because, for example, they capture information on the borrower and lender for each transaction, potentially revealing whether position changes are supply or demand driven. They also have a daily or higher frequency, permitting identification of market reactions to shocks. The lack of loan-level analysis is likely due to data availability. Adrian et al (2014) write:

One conclusion emerging from [our work] is the need to better understand the institutional arrangements in [repo and securities lending] markets.

To that end, we find that existing data sources are incomplete. More comprehensive data collection would both deepen our understanding of the repo and [securities] lending markets and facilitate monitoring firm-level and systemic risk in these markets. (p 132)^[3]

This paper provides an algorithm for extracting loan-level data on over-the-counter (OTC) repo markets from securities transactions data, which may improve the accessibility of loan-level repo data.

Loan-level data on unsecured interbank markets are commonly obtained by applying an algorithm pioneered by Furfine (1999) on US data that identifies which interbank cash transfers through central bank payments systems are interbank loans (the ‘Furfine algorithm’). The Furfine algorithm identifies pairs of payments that are consistent with a loan principal transferred in one direction, then a principal and interest repayment back the next day. Many subsequent studies have used it to analyse unsecured interbank markets at the loan level. Some notable examples are Ashcraft and Duffie (2007), analysing the intraday allocation of liquidity in the fed funds market, Afonso, Kovner and Schoar (2011), studying daily patterns in US unsecured interbank markets during the global financial crisis, and Acharya and Merrouche (2013), analysing UK unsecured interbank markets during the crisis.

Research on repo markets has tended to rely on datasets that are less detailed or of lower frequency. For example, Krishnamurthy, Nagel and Orlov (2014) study detailed data at the quarterly frequency, obtained from regulatory filings by a large proportion of US repo counterparties, and Gorton and Metrick (2012) analyse daily market-wide quotes from US dealers on interest rates and haircuts for various collateral types. Data are more readily available for market segments traded through centralised infrastructure, although these data have tended to be aggregated or anonymised before analysis. Further, OTC markets, which can be large, are omitted, limiting the conclusions that can be drawn. Copeland, Martin and Walker (2014) analyse daily data on collateral held against repos through tri-party infrastructure, collected by the Federal Reserve Bank of New York.^[4] Mancini, Ranaldo and Wrampelmeyer (2016) analyse European data with several loan-level details but without counterparty information. Fuhrer, Guggenheim and Schumacher (2016) is one of the few studies that has analysed loan-level repo data, focusing on the Swiss franc repo market.

This paper describes an algorithm for extracting loan-level repo data on OTC market segments from securities transactions data, and applies the algorithm to conduct a preliminary loan-level analysis of the Australian repo market (excluding repos with the RBA). Securities transactions data are typically stored by a central securities depository (CSD) that is responsible for maintaining securities ownership records. Most CSDs permit securities transactions to involve simultaneous movement of cash and securities in opposite directions, via a link to an interbank payments system. Accordingly, OTC repos are settled through CSDs alongside other transactions such as secondary market purchases (i.e. outright trades). This is comparable to how unsecured loans are transacted through centralised payments systems alongside non-loan interbank payments. Analogous to the Furfine algorithm, the objective of the algorithm I present (the ‘repo-detection algorithm’) is to separate repo-related transactions from securities transactions occurring for other purposes.

This research is related to the small literature following Furfine (1999) that assesses and constructs modifications of the Furfine algorithm (‘Furfine-type algorithms’). Armantier and Copeland (2012) and Kovner and Skeie (2013) compare data from the Furfine algorithm with internal data from two banks and with regulatory data, respectively. Kuo et al (2013) generalise the Furfine algorithm to detect term loans rather than overnight loans. Arciero et al (2016) calibrate, run and assess the algorithm using European payments data. Rempel (2016) estimates the Furfine algorithm's rates of false detections, proposing some modifications to improve performance. Brassil, Hughson and McManus (2016) appear to be the first to detect loans that comprise more than two transactions (‘multiple-transaction loans’), finding their augmentation to noticeably improve detections in the Australian unsecured market.

Like Furfine-type algorithms, the repo-detection algorithm identifies groups of cash movements that resemble a loan followed by a repayment with interest. However, Furfine-type algorithms rely on the market convention that unsecured loan principals are multiples of, for example, $100,000, which is not followed in the Australian repo market. On the other hand, securities transactions data contain more information than payments data – most notably the type and quantity of securities transferred. By requiring that the securities initially provided as collateral are the same type and quantity as those returned, the repo-detection algorithm essentially removes the need to require that loan principals are round numbers. In addition, it detects multiple-transaction repos, like Brassil et al (2016), although the difference in market conventions across repo and unsecured markets necessitates a different approach.

The repo-detection algorithm is described in more detail in Section 2. It is represented as a set of conditions that identify a group of securities transactions as a detected repo. I then describe the procedure for applying these conditions to the data. To detect multiple-transaction repos, I appropriately adapt the subset sums problem (a well-known exercise in computer science) to the requirement that all collateral provided in a repo is subsequently returned. The R code for the algorithm, with detailed comments, is available in the online supplementary information.

In Section 3, I run the algorithm on securities transactions data from Austraclear that cover several two-month windows of transactions from 2006 to 2015, and assess its performance. Multiple-transaction repos are common but occur with much lower frequency than two-transaction repos. Using placebo tests that can be interpreted as a special case of the approach of Rempel (2016), I estimate around 3 per cent of the algorithm detections to be false detections, although excluding multiple-transaction repos reduces this to around 1 per cent. To gauge the incidence of repos missed by the algorithm (but present in the transactions data), that is, false omissions, I relax some of the conditions assumed in Section 2 and find that very few additional repos are detected.

Readers more interested in the Australian repo market data than the algorithm itself can skip to Sections 4 and 5. Section 4 provides context for the analysis in Section 5 by comparing the algorithm data with aggregated repo data from the Australian Prudential Regulation Authority (APRA). The APRA data imply substantially larger repo positions. There are, however, reasons to expect differences. For example, some repo positions reported to APRA are likely transacted through international CSDs (ICSDs) located in Europe with offshore counterparties. Observations in the algorithm data and APRA data have a robust positive relationship with correlations of around 0.5.

Section 5 provides a preliminary description of the Australian short-term repo market, that is, of 14-day maturity or less, as informed by the algorithm data obtained in Section 3. In the 2015 window, the average total value of repos open each night is around $12 billion, compared to around $5 billion in 2006.^[5] The majority of repos are collateralised by Australian Government securities (AGS), although there is little market concentration in particular AGS. In 2006 repos open for one week had the largest market share, although by 2015 the market had largely shifted to overnight maturities. Repo rates display substantial cross-sectional variation across an interval of around 50 basis points, and drift upward between 2006 and 2015. For maturities up to 14 days, rates are not strongly related to maturity. Larger loans have higher rates.

In the two-month 2015 window, around half of the repo turnover in the market segment captured is between one foreign lender and one Australian borrower. Otherwise, most of the turnover is distributed across around 20 Australian and foreign banks and a few Austraclear accounts related to banks' clients. Repo counterparties tend to be either highly integrated within the market, dealing with around ten counterparties, or active only in the periphery, dealing only with one or two banks from the highly integrated segment. Repo haircuts are scattered around zero, not following any obvious patterns. On average, repo settlement activity subsides late in the day when unsecured market activity peaks. Longer maturity repos tend to be initiated earlier in the day than shorter maturity repos.

Footnotes

‘Repo’ is short for ‘repurchase agreement’. A repo is similar to a securities sale paired with a subsequent repurchase. [1]

This figure focuses on the overnight markets because the overnight unsecured market is the target of monetary policy, but it does not necessarily represent activity at other maturities. Section 5.1 shows that between 2006 and 2015, there was a substantial shift in repo market activity towards overnight maturity. [2]

Securities loans are sometimes referred to as special repos, as opposed to general collateral (GC) repos, and are driven by the collateral receiver's demand for the particular collateral received, for example, to cover a short position in those securities. Since they are often collateralised by cash, they can be difficult to distinguish from other repos. This paper treats securities loans as a subset of repos [3]

Copeland et al (2012) describe tri-party repo infrastructure in the United States. [4]

Discount securities (i.e. securities without coupon payments such as bank bills) issued by private entities are excluded from the data prior to analysis, so these figures do not include any repos collateralised by them. [5]