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The Cost of Liquidity in the FX Market
On July 3, 2013, the courts pronounced caveat emptor with respect to execution performance in the FX market. U.S. District Judge Denise Cote threw out a lawsuit, which accused JPMorgan Chase & Co. of breaching a fiduciary duty to custodial clients by charging “hidden and excessive mark-ups” on currency trades. Judge Lewis Kaplan dismissed a lawsuit directed at officials of Bank of New York Mellon, for ignoring “red flags” or knowing that trades were being processed at the worst or near-worst prices of the day.1
We have no opinion on such rulings or the legal actions which engendered them. But, what do buyers do when buyer beware is the way of a market? They look, or at least, they should. An examination of trading is more involved than Judge Cote believes, when she said, there was “nothing secret about the mark-ups”2 charged, because they are disclosed in public databases and on trade confirmations. We are still searching for those public data, and trade confirmations are hardly evidence of execution quality.
We develop measures of the cost of liquidity in the FX market. This is a necessary step in the construction of pre-trade tools and an essential input to the evaluation of transaction costs on a post-trade basis. We begin by quantifying that cost. The results of our methodology, based on tradable quotes from multiple sources, are applied in three ways. The first involves asking the question, what is the range of costs associated with equity-linked FX transactions? The second application is to judge the effectiveness of indicative quotes, with respect to both the level of the quote stream and how it varies over time. Finally, we illustrate the use of cost and volatility analytics by examining trading activity during the 2014 World Cup. Yes, we know that may sound strange, but within-game and post-game market behavior is illustrative of what may be achieved through pre-trade transparency. Besides, that event generated over 3.5 billion Facebook interactions, and four times the number of tweets as the last Olympics; there must be a few fans amongst this readership.
1 In the case of JPMorgan, allegations were rejected that the custodial agreement obligated the bank to process trades at “the best available market rate” or by any other measure. Judge Kaplan said it was improper to hold Bank of New York Mellon executives and directors responsible for alleged currency trading practices leading to the lawsuit. The lawsuit did not create “reasonable doubt that the board’s inaction was a valid exercise of business judgment.” Jonathan Stempel, “Judge dismisses lawsuit against Bank of NY Mellon executives,” Reuters, July 3, 2013.
2 Jonathan Stempel, “JPMorgan, Bank of NY Mellon win dismissal of forex lawsuits,” Reuters, July 3, 2013.
24 October 2014
AUTHOR
Ian Domowitz Managing DirectorHead of Analytics [email protected]
Milan BorkovecManaging DirectorHead of Financial [email protected]
James CochraneDirectorITG [email protected]
Christopher EscobarDirectorITG [email protected]
CONTACT
Asia Pacific +852.2846.3500
Canada+1.416.874.0900
EMEA+44.20.7670.4000
United States+1.212.588.4000
[email protected] www.itg.com
224 October 2014
SURVEY EVIDENCE AND A LITTLE HISTORY
Credible examination of FX transaction costs is difficult, but not impossible. Early studies by Russell Investment Group and Record Currency Management used data from sub advisors to measure the contribution of FX to overall portfolio transaction costs.3 The distribution of transactions in major currencies were highly skewed towards the worst rates of the day, with an average cost of nine basis points (bps). Record found costs to be between 10 and 12 bps; it was noted that “approximately one half of the audits conducted to date by Record revealed that clients received uncompetitive FX pricing on a routine basis.” A 2010 paper from Russell shows that most FX trades of investors, who rely on the executions of managers or custodians, are executed at prices inferior to the average rate of the day.4
In 2007, a survey of 17 transaction cost analysis (TCA) providers revealed that none offered foreign exchange TCA.5 Today, there are at least 9.
Some ascribe this growth to regulatory pressures. The concept of regulation is tenuous in the global FX market, but mandates for best execution are on the horizon. Legal cases provide some additional impetus. Institutional and corporate demand, focused on process improvement in pursuit of alpha preservation, is a driver. A survey of FX traders in 2007 suggests that 73 percent would look at reports if available, and 37 percent of respondents would want such reports daily.6
Demand for measurement grows with changes in market structure and their consequences. In 2013, ITG commissioned a special report from Greenwich Associates7 in order to assess this evolution, as seen by the global buy-side. FX volume executed electronically increased 55 percent from 2011 to 2012. Electronically-traded volume was 65 percent of the total in 2012, an increase from 57 percent the year before. Multi-dealer platforms accounted for the majority of volume, dwarfing the use of single-dealer systems, messaging systems, and dealing on the telephone. Algorithmic trading already was used by 23 percent of respondents. There also is nascent dark pool activity.8
The survey also touched on TCA for FX, with 44 percent of respondents reporting usage. Early equity TCA was focused on compliance; similarly, 50 percent of users in FX also report compliance requirements. Surprisingly, 92 percent of respondents cited investment process improvements as a driver of FX TCA; this type of impetus was slower to develop in equities. Another 33 percent reported clients’ requests for execution information as being important.
The FX world has moved on since then. A look at Greenwich Associates data9 for 2013, covering 1144 global users of foreign exchange services, reflects the changes. Spot volume traded electronically accounts for 77 percent of total volume. G-10 currency percentages skew the results somewhat, with 81 percent of volume traded via electronic means. Emerging markets currencies are catching up, at 58 percent and growing, year on year. Although only 11 percent of the overall universe engages in algorithmic trading, the figure grows to 42 percent if one selects a subsample of 44 large institutional investors.
3 Robert Collie, “It’s Time for More Choice in FX,” Russell Investment Group Viewpoint, December 2004; Record Currency Management, “Paying Heed Pays Off,” Record Research Summary #5, July 2003; Record Currency Management, “Report to Frank Russell on Currency Transaction Costs,” February 2005.
4 Shashank Kothare and Lloyd Raynor, “Are your FX fees too high? Ways to close the ‘responsibility gap’,” Russell Research, January 2010.5 Michael DuCharme, “First Steps in Foreign Exchange Transaction Cost Analysis,” Journal of Performance Measurement, Spring 2007.6 Tabb Group, “Just What is Best Execution in FX?” Tabb Group Perspective, July 2008.7 Greenwich Associates, “Foreign Exchange Market Trends”, 2013.8 See Foreign Exchange Trading Creeps into Dark Pools, Wall Street Journal, October 11, 2012.9 Greenwich Associates, “Foreign Exchange Market Trends”, 2013.
324 October 2014
The use of TCA in the larger Greenwich universe is found to be limited; 17 percent report usage of some kind, largely proprietary as opposed to broker-provided or third-party vendor. As with algorithmic trading usage, however, that figure jumps to 70 percent for large firms.
THE COST OF LIQUIDITY IN THE FX MARKET
Before looking at a single order from any individual trader, one goal of TCA is to quantify transaction costs prevalent in the aggregate market, for various deal sizes, times of day, and across market conditions. We give a flavor of such analysis for five major currency pairs and six minors.10 The time period is January 1, 2013 through March 31, 2013.
The key to the analysis is the construction of a consolidated limit order book for each currency pair, based on data from twelve banks and five electronic communications networks (ECNs).11 Tradable quotes are identified, and all statistics are based upon them; indicative quotes are examined in the next section. We limit ourselves to a discussion of spot rates. 12
An empirical order book permits the construction of size-adjusted spreads for any time of day. Intuitively, the spread should depend on the notional amount of liquidity available at any given price. The order book quantifies this notion, based on the cost of climbing the book for any given deal size.
Figure 1 illustrates depth of book for one major and one minor currency pair.
10 The majors are EUR/USD, GBP/USD, AUD/USD, USD/CAD, and USD/JPY. The minors are USD/PLN, EUR/PLN, USD/CZK, EUR/CZK, USD/TRY, and USD/ZAR.
11 This approach was discussed at length in work by Morgan Stanley, but not acted upon, due to lack of available data at the time. Instead, aggregate cost models were built based on options pricing formulae and an assumption of Poisson arrivals of orders. See, “A Guide To FX Transaction Cost Analysis, Parts I and II,” Morgan Stanley White Paper Series, October, 2009 and February, 2010.
12 Space constraints preclude discussion of forwards and swaps.
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FIGURE 1Depth of Book for GBP/USD and USD/TRY
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24 October 2014 4
The top panels illustrate depth at the best quote and cumulative depth of book for GBP/USD, and the bottom panels provide their counterparts for USD/TRY. The median number of price levels from which total depth is calculated stays remarkably constant across the day, ranging between 17 and 20 for GBP/USD and 10 to 12 for USD/TRY.
The basic patterns are similar across all currency pairs. Median depth at the best quotes is 1mm, rising at most to roughly 1.5mm for the EUR/USD. The 90th percentile exhibits a bit more fluctuation, but is still relatively constant for GBP/USD at 3mm; for the EUR/USD pair, the upper percentile range hovers around 5mm.
In comparison, cumulative liquidity across all price levels rises to 250-300mm, on average, for EUR/USD, and hovers between 180mm and 200mm for the Pound, depending on time of day. Median cumulative depth for USD/TRY is between 25 and 30 times that available at the best quotes. This minor pair is not completely representative, however. Book liquidity is sparse for all CZK pairs and for EUR/PLN. Median cumulative depth is below 5mm for these pairs. In contrast, USD/PLN exhibits liquidity on the order of 25mm to 30mm.
Based upon these book data, we construct a measure of cost, by currency pair, time and order size.13 Figure 2 contains the results of the exercise for two major and two minor pairs, which are representative of the eleven currency pairs studied.14
13 The measures to follow are based on five-minute intervals and adjusted for daylight saving time regimes. Costs are computed for six deal sizes: 0.1mm, 2.5mm, 7.5mm, 15mm, 35mm, 75mm, and 200mm; remaining data points are simply interpolated. Median values of the size-adjusted spreads are illustrated in the graphs.
14 Although extrapolation produces reasonable results for large deal sizes for the CZK pairs and EUR/PLN, the lack of substantial liquidity on those books precludes reliable estimates past the 5mm deal mark.
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FIGURE 2Size-Adjusted Spread Distributions by Order Size and Time
Source: ITG
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524 October 2014
Time-of-day effects are small during London trading hours; the 1000 GMT and 1600 GMT curves virtually lie on top of each other. Off-hours trading is substantially more costly, although such differences are minimized for Asia-Pacific pairs such as AUD/USD and USD/JPY.
Aggregate costs are far lower than previously reported estimates, such as those cited by Russell and Record Currency Management. In those cases, time is a factor, since results date back to 2003. Morgan Stanley reports more recent numbers based on their model-based methodology, which are multiples of those shown here.15 Our liquidity-based estimates for 50mm GBP, for example, are a tenth of the Morgan Stanley estimate of 4.35 bps. For USD/CAD, they report 4.40 bps on average, with minimum cost at 1.82 bps; in contrast, for the same 50mm deal size, Figure 2 suggests about 0.5 bps. In the case of USD/ZAR, Morgan Stanley’s estimate is seven times what we see in the aggregate data.
As is often the case, the truth is probably somewhere in between. Our method takes advantage of real liquidity provision from seventeen sources. The assumption underlying the derivation of size-adjusted spreads is heroic, however. We are assuming a trader’s ability to sweep the aggregate book, taking advantage of all liquidity for all deal sizes. While this is closer to reality in equity markets, fragmentation of data sources and the mixed nature of the dealer-ECN markets in FX suggest that our estimates constitute a lower bound, at least in view of current trading practice.
The consolidated book is not the only way to look at the cost of liquidity, however. At any time, and for any currency pair, one might also construct a synthetic book based on the best dealer quotes. This exercise is illustrated in Figure 3, for the USD/PLN pair at a single point in time.
15 See, for example, “How Much Does It Cost to Trade 50M?” Morgan Stanley Fixed Income and Trading white paper, June 2013.
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FIGURE 3:Effect of LOB Consolidation on Pre-Trade Cost EstimatesUSD/PLN, 12:00 GMT
624 October 2014
The cost of liquidity based upon the best dealer quotes at any given point in time lies just above the consolidated book. In order to make such a cost curve actionable, a stellar credit rating would be required. The next two curves in Figure 3 are constructed from limit order books based on the average dealer tradable quotes and the consolidated ECN book. Both of these cost estimates are actionable. In the first case, this is simply the average dealer outcome, which provides a reasonable benchmark for the cost of liquidity provision. In the second, it is possible to reach the ECN liquidity, using smart order routing systems.
There are two observations which can be made based on Figure 3. Although the scale of the plots makes it difficult to see clearly, ECN liquidity is cheaper than average dealer liquidity for small deal sizes. The tipping point is roughly at the $5 million mark for most currencies. Second, the cost estimates based on the average dealer and ECN books are now consistent with the Morgan Stanley estimates cited previously. At the $50 million mark in the Figure 3 example, the consolidated ECN cost of liquidity is roughly four times that of the consolidated book. Although the precise multiples vary, this is true for all currencies we have examined.
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FIGURE 4Tradable and Indicative Spreads Over Time
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724 October 2014
INDICATIVE QUOTES AND THEIR TRADABLE COUNTERPARTS
Indicative quotes are widely disseminated, and underlie most studies of the FX market.16 A comparison of tradable quotes (TQ) to indicative quotes (IQ) is therefore of interest. Although there are some quantitative differences in quote levels across currencies, patterns are similar enough that the relevant points can be illustrated using one major and one minor pair. The first comparison appears in Figure 4.
Spreads calculated from indicative quotes are significantly greater than the spreads implied by tradable quotes. The ratio of the two spreads ranges between five and ten, on average for all currencies. Although the indicative quotes are not updated as quickly as tradable quotes, they track each other fairly closely, albeit at different levels.17 In other words, the intraday patterns are essentially the same. Although it is not obvious from the USD/JPY example, the difference between tradable and indicative quotes narrows during London trading hours for most currency pairs.
Indicative quotes do not vary by size. A natural question concerns the deal size for which the indicative spread “correctly” prices liquidity relative to what is actually available in the market. Figure 5 contains representative plots, taken from the GBP/USD and USD/ZAR pairs.
For the major currency pairs, size-adjusted spreads cross the indicative quote at deal sizes between 80mm and 120mm. Indicative spreads overstate cost for all sizes below that range, illustrated in Figure 5 by the GBP/USD pair. For the minors, where deal sizes tend to be smaller, the crossing point is much lower, at about 50mm in the example above and at roughly 30mm for pairs such as EUR/PLN.
16 An early example is by Tim Bollerslev and Ian Domowitz, “Trading Patterns and Prices in the Interbank Foreign Exchange Market,” Journal of Finance 48, 1993.
17 IQ quotes tend to lag the tradable quotes consistently by a few seconds for major currency pairs.
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FIGURE 5Indicative and Size-Adjusted Spread Costs by Order Size
Source: ITGALL Cost Curve IQ Half Spread
824 October 2014
LINKING FX COSTS TO INSTITUTIONAL EQUITY DEMAND
There is a direct connection between the cost of liquidity, as illustrated in Figures 2 and 3, and one of the grander aspirations of TCA: provide the “all-in” cost of a transaction. Appropriate linking of orders is an ongoing issue even in equity TCA, depending on the work flow of any individual buy-side institution. The link between global equity trades and their corresponding FX costs eventually will require information from the buy-side, which enables the connection to be made.
We can provide an idea as to what might be expected. We select the ten most active equity trading firms from our TCA Peer database. All equity orders requiring a foreign exchange transaction are identified.18 At the end of each trading day, the size of the FX transaction is calculated based on the aggregated executed sizes of all equity transactions in a country. We contrast three outcomes. The first is immediate execution of the aggregated FX volume of all equity trades at the time the last equity order is completed. The second and third outcomes are when FX executions take place at prices which deliver the best and worst outcome of the day, excluding the period 21:30-22:30 GMT, during which prices are not representative.19 The results of this exercise are contained in Figure 6.
FX order sizes tend to be relatively small, on average, but have some sizable outliers for certain days. As expected, the magnitude of the ITG Peer client order sizes varies across currency pairs. Euro and Pound lead the pack with the largest order sizes of 550mm and 600mm, respectively. The average order sizes at 100mm and 170mm are also substantial for both pairs. Relating the results to costs in Figure 2, orders in the Canadian dollar reach 200mm (with an average around 60mm) and for the Polish Zloty, only about 50mm (9.5mm). The last is not representative of order sizes in all the minors, however. Equity-linked FX order size for the South African Rand is in the range of 30mm on average with a maximum around 90mm.
18 For reasons idiosyncratic to our own database, the analysis is restricted to USD pairs, e.g., AUD/USD and USD/TRY.19 Even excluding this period, the worst prices of the day still are temporally close to this interval.
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FIGURE 6:Equity-Linked FX Transaction Costs
924 October 2014
How much does it cost the average firm to implement the FX leg of an equity transaction? The answer from this sample is an annualized $13.8 million.
How much could it have cost, if executions were consistently at poor prices? The annualized figure per firm would be $40.8 million. Enough said.
PRE-TRADE INFORMATION AND THE 2014 FIFA WORLD CUP
The FIFA World Cup is arguably the largest sporting event on the planet. Market behavior during World Cup games has attracted attention at levels as high as the European Central Bank.20 Bank researchers used data from 15 stock exchanges to examine the effects of games during the 2010 FIFA Cup in South Africa, They uncovered two interesting facts. When the national team was playing, the number of trades in the home market dropped by 45 percent; volumes declined by 55 percent. More importantly, the correlation between national and global stock market returns decreased by 20 percent during Cup games, whereas no comparable uncoupling is observed, for example, during lunch breaks during a regular day. In essence, the price formation process changed.
Goldman Sachs also reviews regional and global market performance with respect to the Cup.21 Their work focuses on returns and winners, as opposed to trading statistics and games. In short, a Cup winner outperforms the global market by 3.5 percent over the first month, with any outperformance fading away after three months. The runner-up experiences a performance slump, with average underperformance of 1.4 percent in the first month, and continued underperformance for the next three months.
In the examples below, we use a few games from the 2014 FIFA World Cup to illustrate the pre-trade transparency made possible by the methodology described previously. Information on the cost of liquidity and depth of book is augmented by forward-looking volatility calculations; the three factors work together, as might be expected.
We begin with Mexico versus Cameroon on June 13. The relevant currency pair is USD/MXN.
In the bigger picture of the World Cup, this match did not cause much excitement. On the other hand, volatility movements during the game, and changes in the topography of the FX order book presage patterns observed during several other games. Figure 7 shows the evolution of the depth-of-book, in particular. Vertical lines are shown at the start of the game, at the beginning and end of half-time, and at the end of game.
20 Michael Ehrmann and David-Jan Jansen, “The Pitch Rather Than The Pit: Investor Inattention During FIFA World Cup Matches,” ECB working paper no. 1424, February 2012.
21 Goldman Sachs, “The World Cup and Economics 2014,“ working paper, May 2014. This reference is their fifth edition of the series.
1024 October 2014
Depth of book was relatively constant heading into the game, but there was still some pick-up at half-time. Just after the game, however, depth rose above the 95th percentile in historical terms. We suspect that participants who were not typically quoting USD/MXN significantly (or usually provided tradable quotes only for small size) increased their exposure for a considerable amount of time.
Volatility dropped below the 5th percentile just prior to half-time, rising sharply during the recess. It then fell below the 5th percentile for all of the second part of the second half. As the order book filled in following match end, volatility stabilized. This volatility pattern is commonplace during World Cup games, so we omit the USD/MXN picture, and move to Italy versus Uruguay, on June 24.
This was a tight match, decided in favor of Uruguay in the last nine minutes of play. Italy was eliminated. The volatility pattern is striking, pun intended, as illustrated in Figure 8.
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FIGURE 8Intraday Volatility
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FIGURE 7Total Depth of BookUSD/MXN 06/13/2014 15:00-19:00 GMT
1124 October 2014
The intraday volatility distribution depicts expected market reaction to an important game. Volatility ends its random walk pattern just before the match begins. It stays below the 5th percentile during the first half, as the trading community focuses on the match as opposed to the market. There is a small uptick at halftime, but it again drops as the second half is about to begin.
As the game moved into its final minutes, during which the score was decided, volatility declines sharply; someone is watching TV. The lull is short-lived, however. Shortly after match end, volatility spikes well above the 95th percentile; it is unlikely that any predictive model would catch this, since the probability of such an event is close to zero. While the match was progressing, the Eurodollar slowly pushed higher, and tested a level held earlier in the day. If this was a point of pain for traders, they ignored it during the second half. The third test of the price level, right after the match finished, initiated a short-squeeze that pushed the Eurodollar 25 bps higher.
While the price move was sudden, it was short, and not devastating. Nevertheless, a sharp change in the cost of liquidity was observed, illustrated in Figure 9.
As the short-squeeze unfolded, spreads widened from below the 5th percentile to above the 95th percentile. This behavior might be expected, since market-neutral traders were expected to remove liquidity as volatility rises, increasing the cost of liquidity. Prior to the run-up, spreads remained between the 5th and 20th percentiles, tightening up after the short squeeze played out.
On the same day and at the same time, we observe similar pre-trade parameters for the game between Costa Rica and England. This also was a tight match, ending in a draw, which eliminated England from the tournament. The volatility pattern for the GBP/USD pair is depicted in Figure 10.
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FIGURE 9Intradat Cost DistributionsEUR/USD 06/24/2014 15:00-19:00 GMT 50 MM BASE
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1224 October 2014
As in the previous match, a drop in volatility below the 5th percentile occurs during the first half. A sharp rise in volatility accompanies half-time, and continues into the second half. Volatility again falls in the closing minutes, as attention is directed away from trading screens to the futbol pitch.
Perhaps the game was not as engaging as the Italy/Uruguay contest. Nevertheless, the same extreme spike in volatility is observed immediately post game. Contrasting the two games, this suggests that the Euro was really the object of attention. While the Euro rose 25 bps, the Pound climbed 15 bps, without testing a higher level on the day. The movements imply a shift led by the Euro, with the Pound moving in sympathy, and short squeezes in both EUR/USD and GBP/USD.
The effect on the price of liquidity is the same as in the Italy/Uruguay example, albeit more muted. The picture is very much like that observed for Italy/Uruguay, and we omit it here.
Other examples exist. This is true even for “double currency” events, such as Germany versus USA, and Portugal versus Ghana, same time, same day, which is “double Euro.” Both European teams were heavily favored, and one might think that market effects would be mitigated somewhat under the circumstances. Nevertheless, EUR/USD volatility ran down sharply as the matches started, dropping below the 5th percentile towards the end of the first half, spiking during half-times, with a sharp increase again after the match is over. Here, the cost of liquidity rose without the concurrent noise of a short squeeze.
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Source: ITG
FIGURE 10Intraday Volatility
GBP/USD 06/24/2014 15:00-19:00 GMT
5th percentile 20th percentile 50th percentile 80th percentile 95th percentile days_value
1324 October 2014
BUY-SIDE DATA AND THE WAY FORWARD
The purpose of this article is to outline available evidence with respect to the cost of liquidity in the FX markets. There are caveats and opportunities associated with the exercise.
Our estimates are derived from liquidity information based on multiple data sources, all providing tradable quotes, and permitting the construction of an order book. It is no surprise that indicative quotes are generally useless in judging levels of cost, for which we provide evidence. In effect, however, we present a lower bound on costs. The reason for this is an assumption that a trader can sweep the book in a market fragmented not only by time and space, but also by the proliferation of dealers and ECNs. The difference between our aggregate estimates and realized cost represents the opportunity to save money. Hence a rationale for FX TCA, and motivation for changes in market structure and sell-side applications, which would permit such liquidity aggregation.
Nevertheless, our methodology also permits the construction of the cost of liquidity based on synthetic books representing ECN liquidity and liquidity provided by the average dealer at any point in time. The magnitude of cost by deal size in these cases is several multiples of its lower bound, and represents actionable cost calculations. In the near term, attention should be focused on these estimates, as opposed to our lower bound.
Our work on the FIFA World Cup can be interpreted in two ways. The first follows the European Central Bank study, which used the 2010 Cup as a natural experiment to analyze fluctuations in equity investor attention, and to identify market changes due to a behavioral shift. We find that the 2014 World Cup also affected attention in the FX market. The results included unusual volatility conditions, differences in liquidity provision as evidenced by the order book, extremes in the cost of liquidity, and even a seeming ignorance of a couple of short squeezes in the making during the game. The World Cup results are not definitive, rather illustrative. For example, the timing of volatility spikes after two of the games coincided with 2-year U.S. Treasury Bill auctions; the spike following the “double Euro” game may also have had something to do with the seven-year Treasury auction on the day. We note, however, that such spikes typically are not seen at these magnitudes during such auctions. Further, commentary on the two auctions in question suggests very light demand from the buy-side and low coverage from the sell-side.
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Source: ITG
FIGURE 11Intraday Volatility
EUR/USD 06/26/2014 15:00-19:00 GMT
5th percentile 20th percentile 50th percentile 80th percentile 95th percentile days_value
1424 October 2014
The second interpretation is much simpler: the methodology and data presented here provide a level of transparency outside of dealing rooms which is unprecedented and potentially very useful in gauging market conditions and the cost of liquidity provision. Even the statistical methods embedded in the visual display play a part in practice. Typically, one might compute surprises in depth, cost, and volatility relative to expectations on a case-by-case basis and somewhat “on the fly.” The data visualization here overlays recent historical percentiles on data of interest, saving time and effort in the trading process. Surprises stand out, regardless of whether one is looking at an event-day or not. This type of visualization would not be possible without the underlying order book methodology and statistics associated with the cost-of-liquidity calculations.
We examine only the cost of liquidity. Slippage and price impact work await a serious look at a cross-section of buy-side firms’ FX dealings, using data with good timestamps. A preliminary examination of buy-side trading in our own files suggests that process improvement can lower costs. Trading in EUR/USD, for example, appears to cost roughly three times what we would have predicted based on the order book. For the AUD/USD pair, the factor is four. For the ten firms for which we match equity transactions with their FX counterparts, the cost would be $3.5 million, per firm per quarter, based on our lower bound estimates of size-adjusted spreads. Poor execution multiplies this figure three-fold. These are serious numbers, which call for a serious attempt at measurement and analysis.
There is much more to do, and many more questions than answers at this stage. Forwards and swaps constitute part of our individual buy-side analyses, and similar aggregate information would be useful, especially for minor currencies. The impact of the common practice of netting currencies is certainly an important topic, especially since any residuals from that process are executed by a single dealer, as opposed to being exposed to the type of liquidity described here. The effects of volatility are not yet well understood. In preliminary work, we find that volatility, per se, may not be as strong an effect as commonly believed. Volatility surprises, deviations from expectations, constitute a powerful driver and can be quantified, not only for forensic analysis, but also as a pre-trade tool. Explicit links between pre-trade and post-trade analyses have been shown to reduce costs in equity markets. We believe the same to be true in FX.
Regardless of disparities in alternative estimates, FX trading costs, if not measured and managed correctly, constitute a significant drag on investment performance. Solutions now exist, which could be leveraged to achieve better performance. Investors and traders are beginning to expect counterparty accountability in terms of execution quality. Focus and measurement are the first necessary steps toward this goal.
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