Accessible Version
The Dynamics of the U.S. Overnight Triparty Repo Market, Accessible Data
Figure 1. Overnight Triparty Repo Daily Volumes and Rates
Line chart from September 2015 to March 2021 with three variables charted on the plot with each variable extending the entirety of the range. The left vertical axis ranges from $0 to $2,000 in volume (billions). The two variables associated with the left vertical axis are Volume: All and Volume: Overnight. The right vertical axis ranges from 0 to 8 Interest Rate (percent). The first variable labeled Volume: All is designated by a black dotted line. The variable rises slowly over the time period starting at about $750 and ending at about $1,100. The second variable labeled Volume: Overnight is designated by a black solid line. The variable rises slowly over the time period starting at $650 and ending at about $900. The third variable labeled Interest Rate is designated by a thick blue line. The variable rises slowly starting at about 0 percent with a peak at about 5 percent around September 2019 and decreases to end at about 0%.
Note: This figure depicts the daily amount of traded volume in overall and overnight triparty repos (in billions of dollars) and the average dollar-weighted interest rate (in percent) in the overnight triparty repo market.
Source: Authors’ calculations, which use data provided by Bank of New York Mellon and the Federal Reserve Bank of New York.
Figure 2. Repo Daily Volumes and Interest Rates by Collateral Type
This figure depicts volumes and rates by collateral type. This figure shows two plots. The plot on the left shows volume (in billions) in the vertical axis and days in the horizontal axis (from September 2015 until March 2021). This plot shows that overnight funding has steadily been increasing, with Treasury and agency securities making up most transactions. The plot on the right shows rates (in percent) in the vertical axis and days in the horizontal axis (from September 2015 until March 2021). This plot shows that the difference between average interest rates (by collateral classes) and the federal funds target midpoint rate moves in relative lockstep. It also shows that spreads between different collateral classes narrowed in the years prior to the onset of COVID-19 to then widen again. Finally, this figure shows that average interest rates across collateral classes are generally steady, with occasional spikes, as highlighted by the events of September 2019.
Note: This figure depicts volumes and rates by collateral type. Plot (a) shows that overnight funding has steadily been increasing, with Treasury and agency securities making up most transactions. Plot (b) shows that the difference between the weighted average interest rate (by collateral classes) and the federal funds target midpoint rate moves in relative lockstep. We subtract the federal funds midpoint rate from triparty repo interest rates, as the Federal Reserve can influence repo rates through interest on excess reserves and its overnight reverse repo operations.
Source: Authors’ calculations, which use data provided by Bank of New York Mellon and the Federal Reserve Bank of New York.
Figure 3. Interest Rates and Haircuts by Collateral Type
This figure depicts the distribution of rates and haircuts by major collateral type (that is, Treasury, Agency, and Other). This figure shows two plots. The plot on the left shows the distribution of rates (in percent) in the vertical axis and major collateral classes in the horizontal axis. This plot highlights that collateral of greater credit quality and liquidity is associated with transactions with lower interest rates on average. The plot on the right shows the distribution of haircuts (in percent) in the vertical axis and major collateral classes in the horizontal axis. This plot shows that haircuts across collateral classes do vary, reflecting the importance of collateral’s credit quality and liquidity for financing costs. For example, Treasury haircuts are quite standard at 2 percent, with generally little variation, while transactions associated with major collateral class “Other” command much higher haircuts on average and exhibit higher variation.
Note: The credit quality and liquidity of collateral matter for repo pricing. This figure presents the distribution of rates and haircuts by collateral type (in percent). Plot (a) highlights that collateral of greater credit quality and liquidity is associated with transactions with lower interest rates. Notably, haircuts across collateral classes, presented in plot (b), do vary, reflecting the importance of collateral’s credit quality and liquidity for financing costs.
Source: Authors’ calculations, which use data provided by Bank of New York Mellon and the Federal Reserve Bank of New York.
Figure 4. Intraday Clearing Cycle
This figure shows that the overnight segment of the U.S. triparty repo market has a persistent daily clearing cycle. This figure shows two plots representing different views of the intraday clearing cycle. The plot on the left shows the probability associated to trades at a given hour in the vertical axis and hours in the horizontal axis. Thus, this plot presents the probability density function of funding at each hour of the day, where “6 AM ≥” in the horizontal axis captures the early morning activity as well as overnight lending negotiated days prior. The plot on the right shows a different view of the intraday clearing cycle by presenting the average portion of the market cleared throughout the day (which represents the average cumulative density function derived from the plot on the left). In particular, the plot on the right presents the mean (+/- 2 standard deviation bands) of the cumulative density function of funding at each hour of the day. This figure highlights a somewhat persistent clearing process, with overnight agreements typically taking place between 8 and 9 a.m., with a modest late day spike around 1 p.m.
Note: This figure shows that the overnight segment of the U.S. triparty repo market has a persistent daily clearing cycle. Plot (a) presents the probability density function of funding at each hour of the day, where “6 AM ≥” represents the early morning activity as well as overnight lending negotiated days prior. Plot (b) presents the mean (+/- 2 standard deviation bands) of the cumulative density function of funding at each hour of the day.
Source: Authors’ calculations, which use data provided by Bank of New York Mellon and the Federal Reserve Bank of New York.
Figure 5. Intraday Market Participation
This figure presents the hourly volumes of different types of cash lenders and borrowers. This figure shows two plots. The plot on the left shows the intraday distribution of volume (in billions) associated to each major cash lender, where volume is depicted in the vertical axis and hours are depicted in the horizontal axis. The figure on the right provides analogous information for cash borrowers. The figure on the left shows that, among cash lenders, GSEs and securities lenders tend to participate in the first half of the day, while commercial banks make up most of late day trades. The figure on the right shows that, among cash borrowers, non-primary dealers participate only in the first half of the day, while the Federal Reserve’s reverse repo facility has historically made up a large portion of the activity during the second half of the day (mostly at 1 p.m.).
Note: The business models of market participants influence why and when they choose to arrange overnight triparty repos. This figure presents the hourly volumes of different types of cash lenders and borrowers. In each plot, legends identify bars in order from bottom to top.
Source: Authors’ calculations, which use data provided by Bank of New York Mellon and the Federal Reserve Bank of New York.
Figure 6. Intraday Collateral Allocation
This figure presents hourly transaction volumes across major collateral classes. This figure shows two plots. The plot on the left shows the intraday distribution of volume (in billions) associated to each major collateral class, where volume is depicted in the vertical axis and hours are depicted in the horizontal axis. The figure on the right depicts the intraday probability density function per collateral group derived from the figure on the left. The figure on the right shows how different classes of collateral are allocated over the course of the day, emphasizing the idea that riskier collateral tends to be used earlier in the day.
The credit quality/liquidity of collateral play an important role in intraday trading behavior. This figure presents average hourly transaction volumes for overnight funding. Plot (a) presents the aggregate dollar volume (in billions of dollars) by type of collateral. Plot (b) presents the intraday probability density function per collateral group. In each plot, legends identify bars in order from bottom to top.
Source: Authors’ calculations, which use data provided by Bank of New York Mellon and the Federal Reserve Bank of New York.
Figure 7. Trading Network of Triparty Repos Collateralized with U.S. Treasury Securities
This figure presents the trading network of triparty repos collateralized with U.S. treasury securities. Although we do not observe master agreements, we do observe trades, which is how we uncover trading relationships among accounts for different types of collateral. In the figure, nodes represent accounts, and edges represent the historical set of trades collateralized by U.S. Treasury securities between accounts. Colors differentiate cash lenders (in red) from cash borrowers (in black). The size of nodes is selected to emphasize the importance of accounts associated with cash borrowers. Nodes associated with cash lenders are given a fixed size, whereas the size of cash borrowers’ nodes is proportional to their number of trading relationships. The figure shows that the market is somewhat segmented as not every lender is connected to every borrower. Additionally, many lenders exhibit few relationships while some borrowers are highly connected.
Note: Trading relationships can play an important role in determining the terms of triparty repos. Although we do not observe master agreements, we do observe trades, which is how we uncover trading relationships among accounts for different types of collateral.
Source: Authors’ calculations, which use data provided by Bank of New York Mellon and the Federal Reserve Bank of New York.