«Trading Partners in the Interbank Lending Market Gara Afonso Anna Kovner Antoinette Schoar Staff Report No. 620 May 2013 This paper presents ...»
Federal Reserve Bank of New York
Trading Partners in the
Interbank Lending Market
Staff Report No. 620
This paper presents preliminary findings and is being distributed to economists
and other interested readers solely to stimulate discussion and elicit comments.
The views expressed in this paper are those of the authors and are not necessarily
reflective of views at the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.
Trading Partners in the Interbank Lending Market Gara Afonso, Anna Kovner, and Antoinette Schoar Federal Reserve Bank of New York Staff Reports, no. 620 May 2013 JEL classification: D40, E59, G10, G21 Abstract There is substantial heterogeneity in the structure of trading relationships in the U.S. overnight interbank lending market: Some banks rely on spot transactions, while most form stable, concentrated borrowing relationships to hedge liquidity needs. As a result, borrowers pay lower prices and borrow more from their concentrated lenders. Exogenous shocks to liquidity supply (days with low GSE lending) lead to marketwide drops in liquidity and a rise in interest rates.
However, borrowers with concentrated lenders are almost completely insulated from the shocks, while liquidity transmission affects the rest of the market via higher interest rates and reduced borrowing volumes.
Key words: interbank lending, OTC markets _________________
Afonso, Kovner: Federal Reserve Bank of New York (e-mail: firstname.lastname@example.org, email@example.com). Schoar: Massachusetts Institute of Technology, Sloan School of Management (e-mail: firstname.lastname@example.org). The authors thank Andrew Howland and David Hou for outstanding research assistance. They are grateful to Pierre-Olivier Weill for an excellent discussion and to Adrian Verdelhan and participants at the 2010 Money and Payments Workshop at the Federal Reserve Bank of New York, at Darden, and at the 2013 AFA meetings for helpful comments. The views expressed in this paper are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System.
I. Introduction A large fraction of transactions in the economy are negotiated and settled in over-the-counter (OTC) markets. Mortgage-backed securities, derivatives, corporate bonds, and syndicated bank loans are only a few examples of large OTC markets. Despite their importance to the economy, surprisingly little empirical research has been done on the functioning of these markets, mainly due to the lack of available transactions data. In this paper we study a specific OTC market, the overnight interbank lending market, for which we can obtain detailed information on individual transactions. We analyze how trading relationships in this market are formed and how they affect the pricing and transmission of liquidity shocks across banks. We show that a majority of banks in the interbank market form long-term, stable lending relationships, which have a significant impact on how liquidity shocks are transmitted across the market.
A number of theory papers have proposed models of the OTC markets. For example, Duffie, Gârleanu and Pedersen (2005) are one of the first to analyze how trading frictions affect pricing and liquidity in OTC markets. Similarly, Vayanos and Weill (2008) and Afonso and Lagos (2012a) analyze the dynamics of the government-bond market and the federal funds market, respectively. This literature provides a theory of dynamic asset pricing that explicitly models prices and equilibrium allocations as a function of investors’ search ability, bargaining power, and risk aversion. Importantly, these models assume that counterparties in the OTC market engage in spot transactions and participants in the market have symmetric information about each other’s types. They, however, do not allow for the endogenous formation of relationships between counterparties. While we believe that these theories capture some of the fundamental economic forces in the interbank market, our results show that it is of importance to understand the nature of the relationships through which liquidity is provided and shocks spread through the market.
Unlike the OTC markets envisioned in most theory models in which counterparties are randomly matched for spot transactions, we document large and persistent heterogeneity in the extent to which some banks concentrate lending and borrowing across counterparties. First, we show that a significant fraction of banks rely on a small number of dedicated counterparties to fill most of their liquidity needs, while others access the spot market to transact with lenders on an ongoing basis. It appears that banks which have higher demand for hedging their liquidity needs are more likely to rely on concentrated credit relationships: Banks that borrow from a more concentrated and stable set of lenders (we will also call these banks that rely more on relationships) tend to be smaller, borrow smaller amounts and access the market less frequently. In addition, concentrated borrowers have a lower ratio of deposits to assets and more trading assets. After controlling for size and amount borrowed, standard measures for bank opacity (% loans, % opaque assets) were not associated with concentration. This might suggest that relationships are created to mitigate liquidity shocks between counterparties but not to reduce information asymmetry between banks, as might have been suggested by traditional relationship lending models, see for example Rajan (1992) or Boot and Thakor (1994). The liquidity hedging story also seems to be corroborated by
the pattern of counterparty matching between lenders and borrowers in the interbank market:
Holding constant geographic proximity, counterparties tend to be dissimilar in the timing of their liquidity needs: Counterparties are negatively correlated in customer payment patterns, and in the ratios of non-performing loans.
Second, we look at the role of counterparty concentration in determining borrowers’ credit terms.
While borrowers with more concentrated lenders tend to face slightly higher interest rates overall, they get the biggest loan amounts and the most favorable interest rates from their most important counterparties. This suggests that borrowers face an upward sloping supply curve and choose to get credit from lenders which charge them better interest rates. These findings are consistent with a model where some banks match with lenders whose liquidity needs are negatively correlated with their own and they can thus insure each other against liquidity shocks at favorable rates. Alternatively, the finding could potentially be explained by a model where more opaque borrowers need to form relationships with more informed lenders, which are willing to lend to them at better prices. However, given the prior result that counterparties do not match due to lower transparency, we believe that it is more likely that the observed concentration of relationships reflects the need for liquidity hedging between counterparties.
Third, to understand the role of relationships in the pricing of liquidity and the transmission of supply shocks we look at shocks to the aggregate supply of and idiosyncratic demand for liquidity. We first look at the impact of large unpredicted shocks to the supply of liquidity. Our proxy for supply shocks is days when Government Sponsored Enterprise (GSE) lending is unusually low.1 Specifically, we identify the ten percent of days in each calendar year where GSEs lend the least. We verify that these days are unrelated to macroeconomic or banking level indicators. According to market participants, incidences of low GSE lending are due to unpredicted changes in mortgage prepayments and other mortgage features. Controlling for borrower and lender fixed effects we find that the GSE supply shocks are transmitted throughout the market: Overall, on days when GSEs lending is unusually low, spreads increase by 2.4 basis points while total borrowing falls for the average borrower in the market. However, it is exactly the banks that borrow the most from GSEs but also have concentrated lenders, which are able to In 2005 through 2009, GSEs supplied about 40% of liquidity to the interbank market but they are typically only lenders (not borrowers) in the market.
expand the amount they borrow from their largest non-GSE lenders without facing significantly higher cost of credit. These results support the idea that lenders provide preferential access to liquidity or liquidity insurance to their concentrated borrowers. Surprisingly, these lenders do not seem to take advantage of their increased bargaining power and demand an interest rate premium for this liquidity insurance. Instead we find that banks which do not have concentrated lenders experience a drop in access to liquidity and an increase in the cost of borrowing on days with low supply of liquidity.
Our findings suggest that even if a liquidity shock affects only a subset of banks, it is transmitted to the rest of the banking market in ways which are affected by trading relationships. This is contrary to standard search models with random spot transactions where supply shocks have a symmetric effect on all banks in the market. While the results underscore the importance of understanding relationships in this OTC market, we cannot tell if the transmission of the liquidity shock to the periphery of banks is inefficient. Banks that face higher costs of liquidity shortfalls may endogenously build concentrated relationships to protect their access to liquidity. In contrast, banks that rely on spot transactions might be able to absorb liquidity shocks more easily and thus do not need to invest in relationships.
Finally, we want to see if we find similar patterns of transmission for idiosyncratic demand shocks (measured as days where borrowers have the highest 10% largest one day borrowing).
We do not find that spreads go up when borrowers have high demand for liquidity. Concentrated counterparties do not seem to take advantage of their increased bargaining power on high demand days. At the same time, borrowers are able to access more liquidity from their most concentrated lenders. This suggests that concentrated lenders provide insurance to banks with volatile liquidity needs. Interestingly, we also see that there is almost no transmission of these idiosyncratic liquidity shocks to other borrowers in the interbank market. Contrary to the findings for aggregate supply shocks, average spreads do not go up and we see no crowding out of liquidity from other borrowers, even for banks with more concentrated lenders. This lack of contagion suggests that borrowers use relationships to obtain liquidity insurance.
One benefit of the overnight interbank market relative to other OTC markets is that we can analyze transactions using estimates on counterparties, prices and amounts extracted from Federal Reserve payments data. Specifically, transactions used in this study are identified as overnight loans from the universe of all Fedwire Funds Service (Fedwire) transactions using an algorithm similar to the one proposed by Furfine (1999). However, a drawback of the data is that since interbank transactions are not disclosed directly by counterparties, we cannot be sure that some loans are not missed, that some loan terms are not misidentified or that some payments are not misclassified as loans. Historically, algorithms based on the work of Furfine have been used as a method of identifying overnight or term federal funds transactions. The Research Group of the Federal Reserve Bank of New York has recently concluded that the output of its algorithm based on the work of Furfine2 may not be a reliable method of identifying federal funds transactions.3 This paper therefore refers to the transactions that are identified using the Research Group’s algorithm as overnight or term loans made or intermediated by banks. Use of the term “overnight or term loans made or intermediated by banks” in this paper to describe the output of the Research Group’s algorithm is not intended to be and should not be understood to be a substitute for or to refer to federal funds transactions. For this reason, this paper focuses on It should be noted that for its calculation of the effective federal funds rate, the Federal Reserve Bank of New York relies on different sources of data, not on the algorithm output.
The output of the algorithm may include transactions that are not fed funds trades and may discard transactions that are fed funds trades. Some evidence suggests that these types of errors in identifying fed funds trades by some banks may be large.
interbank lending activity in general, rather than on the subset of interbank lending transactions generally used, under Regulation D, to refer to obligations that are exempt from reserve
Overall, our findings support a view that participants in the overnight interbank market concentrate trading partners, especially borrowers that otherwise might find it difficult to access the market, such as smaller banks. Interestingly, lenders provide preferential access to these borrowers and seem to insure them against liquidity shocks. As a result, supply shocks to a subset of borrowers are transmitted to ex ante unaffected parts of the interbank market. These relationships play an important role in pricing and access to liquidity in this market. It is possible that these concentrated relationships may explain some of the stability that we documented in this market after the collapse of Lehman Brothers (Afonso, Kovner and Schoar (2011)).