Synthetic financing trends
22 January 2019
Robert Levy of Hanweck explores the use of a new metric for looking at broad aggregates of securities over the last two years
Image: Shutterstock
Hanweck launched borrow intensity indicators in March last year to provide intraday transparency into US stock borrow/loan rates and inform people of trading and lending opportunities. The model is based on the concept of constant maturity synthetic lending terms rates from 30 to 360 days. Borrow intensity is expressed in the format of lending rebate rates and can be readily incorporated into a company鈥檚 valuation framework. On a day-to-day basis, most users naturally focus on single securities that are exhibiting significant changes in term levels or are persistently hard to borrow (high intrinsic). The focus here with the close of last year, is to explore the use of a new metric for looking at broad aggregates of securities over the last two years and to see if there are discernible patterns.
Analytic approach
In previous research, we have examined trends of borrow intensity levels, counts of hard-to-borrow (HTB) securities of different ranges of intrinsic value and also categories of general collateral. Data is generated from the exchange-traded options markets and incorporates predictive analytics, rather than based on bilateral transactions.
A relative measure is possible, with the help of underlying historical data in the option and equity markets. We used the data shown in table one to construct a synthetic fee measure for a given maturity of borrow intensity, in this case, 45 days. We then calculated a metric of dollar/day at the 45-day rate, across the universe of HTB securities that ranged from mild to high intrinsic value, and further breaking this group into quintiles, with the first quintile holding extremely HTB securities.
This synthetic fee measure gives a fairer and more representative view of trends in aggregates of securities across time rather than merely looking at rates. Unlike an average rate view, the fees are not thrown off by illiquid low volume securities that are extremely HTB. Conversely, milder HTB securities that trade in high volume can contribute significantly to the total.
The calculation of synthetic fee dollars is described below:
Data for synthetic fee-dollar index
Daily data aggregated from intraday measures across entire US equity option universe (Hanweck Historical Option Analytic data):
Option expires that bracket target maturity
Trading volume of nearest-to-the-money strikes for each observation
Interpolated OIS rates as a term risk-free rate
Hanweck Borrow Intensity data
Summary of analysis 2017 to 2018
The following results were generated from the data described in table one above. The series is volatile on a daily basis, and so are presented herein monthly or annual rollups to make any trends more visible.
We initially expected the results to be similar to fee trends in the overnight securities lending market since the behaviour of rebate rates and the distribution of overnight lending rates and term borrow intensity levels are similar.
The process of bringing in options volumes, however, appears to introduce a new dynamic beyond simply a basis for weighting contributions upon liquidity.
There is no side-effect from put-call ratios because both are treated equally for volume weighting. But the overall measure becomes sensitive to the correlation of option volumes and synthetic lending rates. This causes spikiness in higher frequency data and has visible effects on the monthly series as well. It鈥檚 a phenomenon that bears further investigation.
Break-out by quintiles
Fees from the first quintile strongly dominate the overall fee distribution. Note that the break between first and second quintiles occurs roughly at a borrow intensity of -3 percent. Figure one below shows the total fees by year for the quintiles, and figure two shows the behaviour of the individual series over two years. The first quintile peaks in January of 2018 where both its level and spread to other quintiles is widest. By year-end of 2018, both the fee levels and spreads declined.
Monthly fee trends
The synthetic fee measure is not intended as a value to be considered on an absolute basis. That is, it cannot directly be compared to lending fees reported in the physical market. It is a comparative measure of option market-based fee trends, as it overstates the volume for options selected by using the entire traded volume, and then understates by omitting other series that are nearly comparable (for example, a nearby weekly) but not included. The goal is to account for both liquidity and lending fee spreads and objectively base that liquidity on the options that would most likely be used in an actual conversion trade.
Figure one: SEQ Figure \* ARABIC 1 - Fee Quintiles
Figure two: Fees by HTB borrow intensity quintiles 2017 to 2018
Figure three shows a plot of synthetic fees aggregated across the full range of HTB securities based upon 45-day borrow intensity.
Last year appears more volatile with peaks that exceed 2017 levels. Additionally daily fee dollars exceed 2017 by about 20 percent in 2018. The measure here is sensitive to option volumes as well as implied rates, and these two factors both increase in January, August, and November of last year to generate peaks in fees.
This contrasts with modestly lower security lending fees in the US lending market for 2018 versus 2017. These are different segments also, with lending fees mostly based upon the overnight lending market, and this analysis strictly focused on a term segment.
Figure three: Synthetic fees by month
Periods preceding and during major market declines
Interestingly, January and November activity both preceded months of major equity market declines and increases, which implied volatility. January synthetic fee activity in particular spiked prior to the February volatility spike and liquidity distress.
Much of the year-over-year increase in 2018 fees were due to these three periods. It鈥檚 also notable that fees in months where there was a major sell-off including February, October, and December all showed fairly significant drops from prior months.
Volume changes are not consistent with this, so it appears that real selling that occurred in these periods brought additional collateral into the market, reducing lending spreads.
As an aside, this pattern in December was visible with single securities also including some exchange-traded funds (ETFs). For example, the ETF HYG as seen in figure three, shows a drop in fees during the December 2018 period where it experienced the most precipitous price decline of 2018.
Fees were actually at the highest level in November 2018, a period where the price decline in HYG had just started to gain momentum.
Figure four: Synthetic fees HYG (iShares HY ETF) 2017 to 2018
Perspective from borrow intensity and additional option-based data
Borrow intensity indicators are created from tick-level data in the Hanweck option analytics framework. In this note, we explored a new indicators created from combining borrow intensity with filtered options trading data from Hanweck historical data. Synthetic fee dollars add the dimensions of liquidity and volume. The most frequent applications of borrow intensity indicators are two-fold: inform overnight lending rates and provide signals for equity strategies.
In this article, we explored the behaviour of the set of equity names that had at least minor intrinsic value to the most extreme of hard-to-borrow鈥攅xcluding general collateral. Last year, fee dollars demonstrated more volatility than 2017 and modestly higher levels overall, and particularly noteworthy: time periods of major spikes in synthetic fees preceded periods of higher volatility and broad market sell-offs, including price declines in February and December of last year. It鈥檚 exciting to find new ways to relate information across the derivatives and cash markets, and Hanweck will look to publish periodic synthetic fees statistics in the future
Analytic approach
In previous research, we have examined trends of borrow intensity levels, counts of hard-to-borrow (HTB) securities of different ranges of intrinsic value and also categories of general collateral. Data is generated from the exchange-traded options markets and incorporates predictive analytics, rather than based on bilateral transactions.
A relative measure is possible, with the help of underlying historical data in the option and equity markets. We used the data shown in table one to construct a synthetic fee measure for a given maturity of borrow intensity, in this case, 45 days. We then calculated a metric of dollar/day at the 45-day rate, across the universe of HTB securities that ranged from mild to high intrinsic value, and further breaking this group into quintiles, with the first quintile holding extremely HTB securities.
This synthetic fee measure gives a fairer and more representative view of trends in aggregates of securities across time rather than merely looking at rates. Unlike an average rate view, the fees are not thrown off by illiquid low volume securities that are extremely HTB. Conversely, milder HTB securities that trade in high volume can contribute significantly to the total.
The calculation of synthetic fee dollars is described below:
Data for synthetic fee-dollar index
Daily data aggregated from intraday measures across entire US equity option universe (Hanweck Historical Option Analytic data):
Option expires that bracket target maturity
Trading volume of nearest-to-the-money strikes for each observation
Interpolated OIS rates as a term risk-free rate
Hanweck Borrow Intensity data
Summary of analysis 2017 to 2018
The following results were generated from the data described in table one above. The series is volatile on a daily basis, and so are presented herein monthly or annual rollups to make any trends more visible.
We initially expected the results to be similar to fee trends in the overnight securities lending market since the behaviour of rebate rates and the distribution of overnight lending rates and term borrow intensity levels are similar.
The process of bringing in options volumes, however, appears to introduce a new dynamic beyond simply a basis for weighting contributions upon liquidity.
There is no side-effect from put-call ratios because both are treated equally for volume weighting. But the overall measure becomes sensitive to the correlation of option volumes and synthetic lending rates. This causes spikiness in higher frequency data and has visible effects on the monthly series as well. It鈥檚 a phenomenon that bears further investigation.
Break-out by quintiles
Fees from the first quintile strongly dominate the overall fee distribution. Note that the break between first and second quintiles occurs roughly at a borrow intensity of -3 percent. Figure one below shows the total fees by year for the quintiles, and figure two shows the behaviour of the individual series over two years. The first quintile peaks in January of 2018 where both its level and spread to other quintiles is widest. By year-end of 2018, both the fee levels and spreads declined.
Monthly fee trends
The synthetic fee measure is not intended as a value to be considered on an absolute basis. That is, it cannot directly be compared to lending fees reported in the physical market. It is a comparative measure of option market-based fee trends, as it overstates the volume for options selected by using the entire traded volume, and then understates by omitting other series that are nearly comparable (for example, a nearby weekly) but not included. The goal is to account for both liquidity and lending fee spreads and objectively base that liquidity on the options that would most likely be used in an actual conversion trade.
Figure one: SEQ Figure \* ARABIC 1 - Fee Quintiles
Figure two: Fees by HTB borrow intensity quintiles 2017 to 2018
Figure three shows a plot of synthetic fees aggregated across the full range of HTB securities based upon 45-day borrow intensity.
Last year appears more volatile with peaks that exceed 2017 levels. Additionally daily fee dollars exceed 2017 by about 20 percent in 2018. The measure here is sensitive to option volumes as well as implied rates, and these two factors both increase in January, August, and November of last year to generate peaks in fees.
This contrasts with modestly lower security lending fees in the US lending market for 2018 versus 2017. These are different segments also, with lending fees mostly based upon the overnight lending market, and this analysis strictly focused on a term segment.
Figure three: Synthetic fees by month
Periods preceding and during major market declines
Interestingly, January and November activity both preceded months of major equity market declines and increases, which implied volatility. January synthetic fee activity in particular spiked prior to the February volatility spike and liquidity distress.
Much of the year-over-year increase in 2018 fees were due to these three periods. It鈥檚 also notable that fees in months where there was a major sell-off including February, October, and December all showed fairly significant drops from prior months.
Volume changes are not consistent with this, so it appears that real selling that occurred in these periods brought additional collateral into the market, reducing lending spreads.
As an aside, this pattern in December was visible with single securities also including some exchange-traded funds (ETFs). For example, the ETF HYG as seen in figure three, shows a drop in fees during the December 2018 period where it experienced the most precipitous price decline of 2018.
Fees were actually at the highest level in November 2018, a period where the price decline in HYG had just started to gain momentum.
Figure four: Synthetic fees HYG (iShares HY ETF) 2017 to 2018
Perspective from borrow intensity and additional option-based data
Borrow intensity indicators are created from tick-level data in the Hanweck option analytics framework. In this note, we explored a new indicators created from combining borrow intensity with filtered options trading data from Hanweck historical data. Synthetic fee dollars add the dimensions of liquidity and volume. The most frequent applications of borrow intensity indicators are two-fold: inform overnight lending rates and provide signals for equity strategies.
In this article, we explored the behaviour of the set of equity names that had at least minor intrinsic value to the most extreme of hard-to-borrow鈥攅xcluding general collateral. Last year, fee dollars demonstrated more volatility than 2017 and modestly higher levels overall, and particularly noteworthy: time periods of major spikes in synthetic fees preceded periods of higher volatility and broad market sell-offs, including price declines in February and December of last year. It鈥檚 exciting to find new ways to relate information across the derivatives and cash markets, and Hanweck will look to publish periodic synthetic fees statistics in the future
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