Clarus Financial Technology

Liquidity Variables in the Swaps Market

Following Up

As promised at the start of this month, we’re going to crunch some numbers related to the Staff Working Paper No.580 from the Bank of England. For some background, check out our first blog, which had these key take-aways:

Today, we take a look at the framework used to quantitatively assess liquidity in swaps markets.

Liquidity Variables

The Bank of England use three Liquidity Variables. There are two price-dispersion measures, which they use as a proxy for execution costs. The final one is a Price Impact measure. Handily, we can replicate all of these using Clarus data. As with the first blog, the main point will be to bring their figures up to date, instead of relying on a data-set from 2014.

Remember Tick Sizes?

Regular readers will recall one of our top blogs of last year – Is this the scariest chart in 2015 for swap markets? We found that the average tick size in USD swaps is increasing. The framework that the BoE use to assess liquidity is similar in concept to this measure as well.

The first equation that the BoE paper cites looks at the dispersion per day of the transaction price relative to the average price on that day. This is volume weighted, and uses the square of the difference to the average price – hence arriving at a price dispersion measure on a volume weighted basis:

\( \tag {1} DispVW_{i,t} = \sqrt{\sum\limits_{k=1}^{N_{i,t}}\frac{Vlm_{k,i,t}}{Vlm_{i,t}}(\frac {P_{k,i,t}-\bar{P_{i,t}}}{\bar{P_{i,t}}})^2}\)

where;
\(N_{i,t}\) is the total number of trades executed for contract i on day t, e.g. how many 5y trades occurred on the 8th of January 2016?
\(P_{k,i,t}\) is the execution price of transaction k, i.e. the price of a particular 5y trade on the 8th January 2016.
\( \bar{P_{i,t}}\) is the average execution price on contract i and day t, e.g. what was the average price of all 5y trades done on 8th January 2016.
\( Vlm_{k,i,t}\) is the volume of transaction k e.g. the size of the 5 year trade we are looking at on 8th January and
\(Vlm_{i,t}=\sum_{k}Vlm_{k,i,t}\) is the total volume for contract i on day t. e.g. the total volume of 5 years traded on the 8th January.

Using Clarus Data

So, I went off and calculated this measure using Clarus data for every trade in my sample. Remember that we can easily run queries by Tenor, which makes interrogating the SDR data much much easier for data analysis such as this. For example, from SDRView Researcher, we can see the distribution of price forming, spot starting USD trades in 2016:

USD Swap Market Tenor Analysis 2016

My sample data consisted of spot-starting, USD Swaps across the major tenors of 2Y, 5Y, and 10Y. And of course, we look at only price forming transactions – therefore excluding any trades from compression, roll activity or list trading. I looked at all trades done so far in 2016, and also compared those with the same period in 2015.

Clarus Results

Happily, our number crunching backs up the BoE staff report, as well as making a compelling case for transacting on-SEF in 2016.

On-SEF vs Off-SEF

In 2016, we see a marked difference in the Price Dispersion measures for trades transacted On-SEF versus those transacted Off-SEF. For example, in 10Y swaps, which were the most active in our sample period, we see the following time-series of daily Price Dispersion measures:

Volume weighted price dispersion from the average price in 10 year swaps. We compare on-SEF vs off-SEF trades during 2016.

Breaking down the calculations used for that chart, we;

The resulting chart is plotted on a logarithmic scale. This is because the differences in Price Dispersion are so great between each time-series. Roughly speaking, we see that off-SEF trades have a price dispersion approximately two orders of magnitude greater than on-SEF trades.

Similar charts are seen for both the 2Y and 5Y time-series of data.

It is important to note that the off-SEF data-set is smaller than that used for on-SEF trades. Therefore, any “off-market” trade has a greater impact on the average and a greater impact on price dispersion – particularly if it is a large trade. We also know from experience that off-SEF data is not quite as clean – I have cleaned some trades with obviously missing fees for example. But we keep this impact to a minimum in our Clarus data set by using only price-forming trades for this analysis.

What Does it Mean?

To my mind, this is an extremely important chart. Not only does it back up the BoE study, it is done with timely, up to date data.

Whilst the BoE staff stated that their Price Dispersion metrics reduced after the introduction of SEF trading, we can see that the sheer difference between the two venues under current market conditions is a telling case for transacting on-SEF right now.

We can also compare current market conditions to those back in 2015 for the same time period. For example, the time series of this analysis for 5y and 10y swaps is below:

Price Dispersion for USD Swaps traded on-SEF. Split by Tenor in 2015 and 2016

Showing;

This last point is an important one. The BoE staff report includes “a vector of market-wide controls” that allows them to make valid comparisons between two different points in time – potentially correcting for increased financial market volatility. But it also highlights that liquidity conditions continue to evolve and are far from static!

And What is Next?

This is just the first of three liquidity variables that the BoE calculated. Over time, we will also plough through the analysis of the remaining two equations:

\( \tag {2} DispJNS_{i,t} = \sqrt{\sum\limits_{k=1}^{N_{i,t}}\frac{Vlm_{k,i,t}}{Vlm_{i,t}}(\frac {P_{k,i,t}-m_{i,t}}{m_{i,t}})^2}\)

where;
\(m_{i,t}\) is the end-of-day mid-quote for contract i on day t,

and

\( \tag {3} Amihud_{i,t} = \frac {1}{T}\sum\limits_{j=0}^{T-1}\frac{\mid{R_{i,t-j}}\mid}{Vlm_{i,t-j}} \)

where;
\(Vlm_{i,t}\) is the total volume traded for contract i on day t, and
\(\mid{R_{i,t-j}}\mid\) is the daily return of contract i on day t.

But that will be the subject of future blogs.

In Summary

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