RDP 2021-01: The Role of Collateral in Borrowing 1. Introduction

Throughout history financial crises have been accompanied by credit crunches (Schularick and Taylor 2012). Credit market supply-side factors are important determinants (Bernanke and Lown 1991), but during crisis times, there is also a flight to quality – lenders become more reactive to borrower risk. As banks generally shift towards safe assets (Caballero, Farhi and Gourinchas 2017), lenders request better and more collateral (Bernanke, Gertler and Gilchrist 1996; Gorton and Ordoñez 2014; Benmelech, Kumar and Rajan 2020a, 2020b), and borrowers without collateral suffer (Rampini and Viswanathan 2010).

In this paper, we empirically analyse the effect of collateral on borrowing during crises. For identification, we focus on interbank loans during the 2008 crisis episode, as loans to banks are prone to the information problems that afflict credit more broadly (Morgan 2002; Dang et al 2017), and wholesale liquidity markets played a central role during this period.

We exploit transaction-level data on the Australian interbank markets for both collateralised and uncollateralised loans around a large exogenous shock – the Lehman Brothers failure. Interbank markets present an ideal laboratory for identification because collateralised and uncollateralised lending take place between the same borrower-lender pairs at the same time. Importantly, the collateral we analyse is liquid and homogeneous across borrowers, whereas in other markets such as real estate lending, collateral and borrower characteristics can be correlated in opaque ways. Australia presents an ideal setting because, in addition to data availability and one-way causality with the Lehman Brothers shock, collateralised lending (i.e. repo) is not guaranteed by a central counterparty – as in as in Europe, for example – and is not segmented from uncollateralised lending (i.e. unsecured) – as in the United States, for example.

Our main result is to robustly show that after the shock, ex ante riskier borrowers with sufficient high-quality collateral substitute from unsecured to repo borrowing. In the unsecured market, borrowing becomes negatively related to banks' measures of ex ante risk, and in the repo market, the largest pick-up is by the riskier banks with more high-quality collateral. We also present evidence of broad-based excess demand for high-quality collateral, as the repo market collateralised against second-best collateral (i.e. semis) expands, while interest rates against first-best collateral (i.e. Australian Government securities (AGS)) fall noticeably – over 100 basis points in our sample. This demand to hold liquid assets presents another means through which collateralisation can lift credit supply, separate from mitigating counterparty risk and information asymmetries, as banks lend more to acquire the best collateral, particularly to riskier borrowers substituting into the collateralised market. We find additional evidence consistent with this channel in the heterogeneity of behaviour across lenders and collateral types. Overall, our estimated effects are statistically and quantitatively significant, and do not appear in placebo regressions using 2006 data (i.e. normal times).

Our key contribution is to detail the role of collateral in borrowing using a robust identification strategy. The large credit channel literature has identified characteristics of credit crunches (e.g. Ivashina and Scharfstein 2010), but there are limitations in comparing collateralised and uncollateralised lending due to identification problems. There is some evidence that safer borrowers need less collateral when borrowing (e.g. Lian and Ma forthcoming) and that more conservative lenders prefer borrowers with less risk and better collateral (e.g. Jiménez et al 2014). However, identifying these effects is difficult because the collateral analysed tends to vary across borrowers, be highly illiquid, and be subject to asymmetric information problems (e.g. Benmelech and Bergman 2011). Moreover, in the literature on interbank and other wholesale markets, uncollateralised and collateralised lending are typically analysed in isolation from each other (e.g. for unsecured lending, see Afonso, Kovner and Schoar (2011); for repo lending, see Krishnamurthy, Nagel and Orlov (2014)). The two markets are analysed together by di Filippo, Ranaldo and Wrampelmeyer (2018); however, the collateralised market is intermediated by a central counterparty, which fundamentally alters the role that borrower collateral plays.

The rest of this section comprises a preview of the paper, followed by a review of the literature and how this paper contributes to it.

1.1 Preview of the Paper

Our empirical analysis centres around reactions to the failure of Lehman Brothers in mid September 2008, which triggered a global financial crisis with disruptions to wholesale funding liquidity around the world. The Australian interbank markets present an ideal platform for identifying these reactions. There exist supervisory data on the positions that each borrower had with each lender each day in both the repo and unsecured markets, including the collateral type for repos, and how much collateral was held by each counterparty. Importantly, the Australian repo and unsecured markets were both operating through the same form of infrastructure, and overlapped substantially in bank participation. These characteristics permit a ceteris paribus analysis of the implications of collateral. In addition, the global financial crisis was largely exogenous to the Australian economy and its interbank market. The real estate market did not crash, and the economic slowdown was modest compared to other advanced economies.

With these data, we answer the following questions. How does the presence of collateral affect credit markets' responses to system-wide stress? Does availability of high-quality collateral matter? Do banks substitute between collateralised and uncollateralised markets? With data at the lender-borrower-day-market level, we can identify the impact of the Lehman shock conditional on ex ante borrower risk and ex ante collateral holdings, while controlling for lender behaviour. Controlling for lender behaviour is important because matching between borrowers and lenders is endogenous. The theoretical literature shows that lenders may choose their borrowers in similar (for monitoring) or in different (for risk diversification) businesses and geographical areas (Rochet and Tirole 1996; Freixas and Rochet 2008; Allen and Gale 2000, 2009). We find evidence of endogenous matching in our sample, and, consistent with the credit channel literature, control for it by saturating the regressions with fixed effects (e.g. Khwaja and Mian 2008; Jiménez et al 2017), though given the quality of the data, we can take further steps. For example, when analysing substitution between the repo and unsecured markets, we can use borrower*lender*day fixed effects, which removes influences that are common to both markets at any level, such as borrower-lender relationships that evolve. We also assess how these controls for unobserveables – and thus the channels they shut off – affect the estimates, to understand different drivers. Finally, we run placebo tests on data from 2006, a year without financial distress, and find no effects.

We find the following robust results. At the market level, after the Lehman Brothers default, the repo market expands substantially alongside the rise in the US TED spread, while the unsecured market remains flat.[1] Reactions to financial stress also differ across markets at the borrower (conditional on lender) level. In the unsecured market, loan volume reactions are negatively related to borrowers' ex ante balance sheet weakness (i.e. borrower risk). This occurs mostly in the intensive margin, that is, in the quantities borrowed rather than in the number of counterparties borrowed from. In the repo market, however, loan volume reactions are positively related to borrowers' ex ante high-quality collateral holdings. Importantly, the positive effect of ex ante collateral holdings is increasing in the degree of borrower risk. In the repo market, moreover, there is more activity in the extensive margin (i.e. number of counterparties borrowed from), consistent with collateral alleviating information asymmetries and facilitating faster changes to the borrower-lender network.

Our central specification combines data from both markets and formally tests whether collateralisation affects reactions to stress. The results confirm substitution behaviour in line with the abovementioned results on individual markets. After the Lehman shock, ex ante riskier borrowers shift from the unsecured to the repo market. This effect has a significant interaction with ex ante high-quality collateral holdings, such that the strongest shifts are by risky borrowers with large holdings of high-quality collateral. These patterns occur in the intensive and extensive margins. Overall, our results indicate that riskier borrowers are rationed from uncollateralised borrowing, while those with sufficient collateral can switch to collateralised borrowing.

We also analyse patterns across lenders and across collateral types. We do not find evidence of lending contractions related to cash hoarding. Rather, there is evidence of lending expansions related to heightened demand for liquid (and safe) assets. Heightened demand for high-quality collateral is evident from the interest rate differential on collateralised loans across collateral types – rates for first-best collateral fall market-wide by over 100 basis points relative to second-best (but still high quality) collateral. This encourages the riskier banks substituting to collateralised borrowing to borrow against first-best collateral, if they hold it ex ante. At the same time, there is an increase in collateralised lending by some banks with low ex ante collateral lending. Therefore, when collateral is high quality, it can facilitate the flow of liquidity beyond the role that it plays in mitigating counterparty risk.

1.2 Contribution to the Literature

Our contribution is to identify the effect of collateral on borrowing, by analysing a large exogenous shock in a setting where the effect of collateralisation can be isolated from confounding factors. We contribute to the large literature on credit, and in particular, the fields related to credit crunches and collateral. The setting we analyse is interbank markets – uncollateralised and collateralised side by side – around the Lehman Brothers failure. We also make an important contribution to the literature on interbank markets, where, to date, most papers analyse collateralised or uncollateralised interbank markets in isolation. In the few studies that cover both markets (see below), there are key risk-related differences between the two markets aside from the presence of collateral; namely, intermediation by a central counterparty, which obscures the analysis of collateral in market transactions.

The credit channel literature shows that borrower and loan characteristics are important determinants of credit allocation during credit crunches (e.g. Goldstein and Pauzner 2005; Chava and Roberts 2008; Ivashina and Scharfstein 2010; Chava and Purnanandam 2011; Chodorow-Reich 2014; Ivashina, Laeven and Moral-Benito 2020). Borrowers' access to collateral is key among these, as demonstrated by the effects of collateral constraints being tightened or loosened (e.g. Kiyotaki and Moore 1997; Chaney, Sraer and Thesmar 2012; Adelino, Schoar and Severino 2015; Campello and Larrain 2016). Several papers have also shown that lenders become more willing to lend collateralised during downturns (Lian and Ma forthcoming; Liberti and Sturgess 2014; Benmelech et al 2020a, 2020b; De Jonghe et al 2020). However, it is difficult to analyse the effect of collateralisation on borrowers' access to credit, because available data typically do not permit controlling for endogenous factors. In particular, the studies mentioned tend to analyse illiquid collateral, which can be deeply related to borrower characteristics, for multiple reasons. First, it is idiosyncratic in opaque dimensions, which opens up omitted variable bias problems. Second, the higher liquidation costs mean that its purpose is often to signal borrower quality rather than reduce lenders' losses given default (Berger, Frame and Ioannidou 2016). Third, illiquid collateral can have fire-sale externalities on other borrowers that use similar types of collateral (Benmelech and Bergman 2011).

Interbank markets present a laboratory in which the role of collateral can be separated from borrower characteristics, because the collateral is liquid, transparent and homogeneous across borrowers. Interbank markets also play a central role in monetary policy and the banking system, with a separate devoted literature, particularly on the effects of financial system stress. In unsecured interbank markets after the Lehman Brothers failure, riskier borrowers experience a reduction in credit or worse terms on their credit (Afonso et al 2011; Angelini, Nobili and Picillo 2011). For repo markets, detailed data are less available (Adrian et al 2014). Existing studies of repo markets under stress show an expansion of credit against liquid collateral (Krishnamurthy et al 2014), and a contraction of credit against collateral whose underlying markets are disrupted (Gorton and Metrick 2012). Our study provides granular evidence behind these patterns, with the first loan-level analysis of both markets side by side where the two markets operate through the same infrastructure. This reveals new results on the role of collateral. In particular, collateral can allow riskier borrowers to substitute markets, and can generate ‘reverse cash hoarding’, with funding conditions eased for riskier borrowers due to heightened demand for the collateral they hold.

The few studies that combine repo- and unsecured-market data during stressed periods analyse over-the-counter (OTC) unsecured markets alongside centrally cleared repo markets, and without loan-level data that include bank identities. These studies present evidence of substitution from unsecured to repo markets under stress (Mancini, Ranaldo and Wrampelmeyer 2016; di Filippo et al 2018; Piquard and Salakhova 2019). Mancini et al (2016) conclude that the outcomes are driven more by the central counterparty (CCP) infrastructure than by the presence of collateral, consistent with interbank studies emphasising the importance of market infrastructure during crises (Martin, Skeie and von Thadden 2014). Mancini et al write ‘the CCP-based segment represents the sole resilient part of the euro repo market. This suggests that anonymous CCP-based trading is key for repo market resilience’ (p 1773). We contribute by isolating the role of collateral, by analysing repo and unsecured markets that both operate bilaterally. Several other papers theoretically model the two types of interbank markets (e.g. Freixas and Holthausen 2005; De Fiore, Hoerova and Uhlig 2018).

Our paper also contributes to the safe asset literature. We show that the safest collateral in Australia remained information insensitive throughout the peak stress in 2008, in line with the theory of Gorton and Ordoñez (2014) and Dang, Gorton and Holmström (2015). We also document a spike in demand for safe assets relative to supply, with rates for borrowing against the safest collateral falling heavily (i.e. prices increasing), consistent with studies that postulate a safe-assets shortage that was exacerbated by that period (Caballero, Farhi and Gourinchas 2016, 2017).


The TED spread is between the 3-month LIBOR based on USD and the 3-month US Treasury bill rate. [1]