Benchmark your books
03 May 2018
With the increase in global non-cash collateral usage, Matt Ross of DataLend explains how agent lenders can optimise their books
Image: Shutterstock
Standardising performance measurement has always been a key area of focus for DataLend. With the successful launch and market-wide adoption of our new-and-improved client performance reporting (CPR) suite, our next step鈥攁fter extensive discussions with clients and an ongoing review of market trends鈥攚as to add non-cash collateral benchmarking to our performance reporting tools.
Non-cash collateral acceptance is on the rise and showing no signs of slowing down (see figure 1), while cash collateral acceptance has remained flat to down over the past four years.
With almost two-thirds of the market, or $1.6 trillion, on loan against non-cash collateral by the end of 2017, DataLend has made strides in providing transparency and accurate benchmarking into that area of the market.
Following this trend, agent lenders across the globe have indicated a need to benchmark their clients against other beneficial owners accepting similar types of collateral. As a result, DataLend has added a new benchmarking capability to CPR (for agent lenders) and DataLend Portfolio (for beneficial owners) to do just that.
In order to tackle this requirement, DataLend determined how best to classify a beneficial owner鈥檚 non-cash collateral acceptance. In conjunction with the agent lender community, we developed a risk tolerance scale that would allow the lending agents to bucket their beneficial owner clients into one of four categories depending on the client鈥檚 鈥渞isk appetite鈥�.
The first and most conservative bucket represents beneficial owners that only accept investment-grade sovereign debt (Sov1). The second accommodates those that are able to accept major index constituent equities (Equity1). The third bucket then introduced the acceptance of investment-grade corporate debt (Corp1) and lower-rated sovereign debt (Sov2). The fourth and final bucket includes beneficial owners willing to accept lower-rated corporate debt (Corp2), equities that are not part of a major index (Equity2) and all other types of collateral (Other).
Not surprisingly, the results of the collateral acceptance allocation exercise were heavily skewed toward the first two buckets: investment-grade sovereign debt and major index constituent equities.
With each beneficial owner鈥檚 non-cash collateral acceptance defined, DataLend gathered data on the effects of different non-cash collateral guidelines. Figure 3 outlines some of the more noticeable differences between fees for clients accepting only investment-grade sovereign debt versus those accepting some level of equities as collateral.
A sizable difference in fees is evident for those clients accepting Sov1 as collateral versus those accepting Equity1 as collateral. The average borrow cost for all securities was 26.42 basis points when investment-grade sovereign debt was pledged as collateral; however, when equities were pledged, the average fee increased by nearly seven basis points.
Upon drilling into global fixed income and eventually down to US Treasury, the spread between the two collateral groups continues to widen鈥攕o much so that lenders accepting equities as collateral were earning 11 basis points more on average than those who were just accepting other investment-grade sovereign debt.
With all of the recent financial regulations, this is evidence that borrowers are willing to pay up when pledging equities as collateral in order to source more high-quality liquid assets (HQLA). Non-cash collateral usage also appears to impact utilisation, which in conjunction with fees drives a lender鈥檚 return to lendable. Figure 4 gives a breakdown of utilisation by non-cash collateral acceptance.
Similar to the disparity in fees, there is a difference in utilisation when lending against sovereign debt versus equities. Across all assets, utilisation increased noticeably when clients were willing to accept equities as collateral. What this illustrates is that when agent lenders have the ability to accept different types of collateral, they can lend at a higher rate of utilisation and potentially generate a greater return to lendable.
With the increase in global non-cash collateral usage, and the disparities noted in both fees and utilisation for clients with different non-cash guidelines, DataLend鈥檚 non-cash collateral peer group benchmarking feature is a vital tool for agent lenders to optimise their books.
Non-cash collateral acceptance is on the rise and showing no signs of slowing down (see figure 1), while cash collateral acceptance has remained flat to down over the past four years.
With almost two-thirds of the market, or $1.6 trillion, on loan against non-cash collateral by the end of 2017, DataLend has made strides in providing transparency and accurate benchmarking into that area of the market.
Following this trend, agent lenders across the globe have indicated a need to benchmark their clients against other beneficial owners accepting similar types of collateral. As a result, DataLend has added a new benchmarking capability to CPR (for agent lenders) and DataLend Portfolio (for beneficial owners) to do just that.
In order to tackle this requirement, DataLend determined how best to classify a beneficial owner鈥檚 non-cash collateral acceptance. In conjunction with the agent lender community, we developed a risk tolerance scale that would allow the lending agents to bucket their beneficial owner clients into one of four categories depending on the client鈥檚 鈥渞isk appetite鈥�.
The first and most conservative bucket represents beneficial owners that only accept investment-grade sovereign debt (Sov1). The second accommodates those that are able to accept major index constituent equities (Equity1). The third bucket then introduced the acceptance of investment-grade corporate debt (Corp1) and lower-rated sovereign debt (Sov2). The fourth and final bucket includes beneficial owners willing to accept lower-rated corporate debt (Corp2), equities that are not part of a major index (Equity2) and all other types of collateral (Other).
Not surprisingly, the results of the collateral acceptance allocation exercise were heavily skewed toward the first two buckets: investment-grade sovereign debt and major index constituent equities.
With each beneficial owner鈥檚 non-cash collateral acceptance defined, DataLend gathered data on the effects of different non-cash collateral guidelines. Figure 3 outlines some of the more noticeable differences between fees for clients accepting only investment-grade sovereign debt versus those accepting some level of equities as collateral.
A sizable difference in fees is evident for those clients accepting Sov1 as collateral versus those accepting Equity1 as collateral. The average borrow cost for all securities was 26.42 basis points when investment-grade sovereign debt was pledged as collateral; however, when equities were pledged, the average fee increased by nearly seven basis points.
Upon drilling into global fixed income and eventually down to US Treasury, the spread between the two collateral groups continues to widen鈥攕o much so that lenders accepting equities as collateral were earning 11 basis points more on average than those who were just accepting other investment-grade sovereign debt.
With all of the recent financial regulations, this is evidence that borrowers are willing to pay up when pledging equities as collateral in order to source more high-quality liquid assets (HQLA). Non-cash collateral usage also appears to impact utilisation, which in conjunction with fees drives a lender鈥檚 return to lendable. Figure 4 gives a breakdown of utilisation by non-cash collateral acceptance.
Similar to the disparity in fees, there is a difference in utilisation when lending against sovereign debt versus equities. Across all assets, utilisation increased noticeably when clients were willing to accept equities as collateral. What this illustrates is that when agent lenders have the ability to accept different types of collateral, they can lend at a higher rate of utilisation and potentially generate a greater return to lendable.
With the increase in global non-cash collateral usage, and the disparities noted in both fees and utilisation for clients with different non-cash guidelines, DataLend鈥檚 non-cash collateral peer group benchmarking feature is a vital tool for agent lenders to optimise their books.
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