Analysing the relationships between liquidity, volatility and momentum

Our latest research article is an extension of our ongoing work investigating the three market variables that are key when planning an execution strategy: liquidity, volatility and momentum. This piece extends the previous research in that we analyse the relationships between these variables to attempt to identify any causal effects, i.e. does one factor drive another? Using Granger causality we find that liquidity is the key factor in the ecosystem, driving changes in both volatility and momentum.

Please email contact@bestx.co.uk if you are a BestX client and would like to receive a copy of the paper.

Volatility spikes and impacts on liquidity

Predicting events, such as spikes in volatility, in financial markets is notoriously difficult.

At BestX we have been researching the use of Hawkes processes to model events, and our latest paper is a follow up to our publication on this subject in August 2020.

We extend the framework to model the impact on liquidity, and therefore transaction costs, with the use of an unsupervised machine learning method to categorise the number of observed liquidity states for a security. This provides the potential for an ‘early warning system’, resulting from monitoring, for example the VIX index, to indicate if sudden changes in liquidity are expected.

If you are a BestX client and would like a copy of the paper please contact us at support@bestx.co.uk.

Balance and compromise within the Best Execution Process

There are no solutions; there are only trade-offs.
Thomas Sowell

Best execution as a concept continues to be the subject of much debate across the industry, with both liquidity providers and takers, and asset owners, having different opinions and definitions. This is no surprise given the relatively amorphous nature of the subject, it really does just depend on what you are trying to achieve, how you are trying to achieve it and why. Best execution means very different things to different types of market participants. For example, a passive index manager will typically have a best ex policy that differs considerably to that of a pure alpha driven macro hedge fund, or a corporate treasury. Clearly, it is not possible to define a ‘one size fits all’ definition for such a broad spectrum of market participants, and therefore any technology solution needs to be flexible and configurable.

As we have previously discussed here, best execution is a multi-faceted process, that is impossible to distil into a single metric or formula. Many decisions are made when conducting the execution of financial transactions, either by humans and/or by algorithms of varying sophistication, and invariably each decision has an element of ambiguity embedded in it. This ambiguity is the result of a world where there is no one ‘right answer’, but a spectrum of outcomes depending on what you are wanting and trying to achieve. Each of these results is often dependent on balancing conflicting outcomes in the decision making process, and in this article we will explore and summarise some of the key themes where compromises have to be made to achieve your definition of best execution.

Market structure and execution method

Fundamentally, the market structure within which you are executing can already provide an over-arching dilemma at the outset of the process. If you are executing in the Fixed Income cash markets, where the majority of flow is still governed by a quote driven structure, there may be few options but to trade in a principal fashion (although this is changing rapidly). However, in a market such as FX, you can choose to trade on a principal basis, or on an agency basis through the issuing of an order, or a mixture of the two. There is clearly a compromise to be resolved here. Trading on a risk transfer principal basis results in immediate certainty of execution, but does not provide opportunity to participate and benefit in the provision of liquidity. Trading as a pure agent passes the responsibility for your order to another party, thereby removing the execution certainty, but does allow you to benefit from spread earning through passive order placement. Participating in a hybrid fashion, blending principal and agent i.e. through the use of an algo, may offer the optimal compromise, although it all depends on what you are trying to achieve.

Counterparty panel size

For those markets that retain a significant proportion of quote driven transactions, i.e. Fixed Income and FX, the number of counterparties you ask for a quote is a key compromise to be rationalised. As we discussed in our research published in March 2019, there is a trade-off between achieving the ‘best’ price and tightest possible spread versus information leakage. There is a law of diminishing returns by simply asking more and more counterparties for a price as past a certain number, you will not generally achieve any better pricing but you do increase the risk of signalling to the market of your trading intentions. There is no simple answer, unfortunately. Annoyingly, the answer is ‘it depends’, and is governed by what you are actually trading, at what time of day and in what size.

Liquidity curation

A similar concept applies when deciding which execution venues to form part of your smart order router for those order driven transactions, a common conundrum in Equities, Futures and FX. Simply adding venues into the mix won’t necessarily result in the optimal execution outcome. Again, there is a trade off between accessing liquidity and achieving price discovery, versus signalling risk and information leakage. Are all of the venues that you wish to add to the liquidity pool contributing unique, additive sources of liquidity, or is there a risk of recycled or ‘phantom’ liquidity polluting the mix?

Spread vs impact vs opportunity

A classic. Sure, you can trade on the tightest price every time, but that won’t necessarily be guaranteeing you have achieved best execution depending on how you execute and what your objectives are. If you are breaking a block up into a number of clips and trading over the course of a few hours, a common practice in all asset classes, then hitting the tightest price for the first clip may result in a sub-optimal result when you look at the overall result for the block at the end of the day. Why could that possibly be, we hear large swathes of the market cry in anguish? Well, the counterparty with the tightest price may also be the one that creates the largest market impact through, for example, the lack of internalisation opportunities. This impact can result in price deterioration in the market for when you come to trade the subsequent clips, so when the overall price achieved for the block is analysed it is worse than if you had executed on slightly wider spreads with a counterparty who created less market impact. We discussed this in our first article in early 2016 using the analogy of buying a suit, an analogy which seems very dated now in these work from home pandemic times.

Executing via an order can further complicate this one by also including the probability of getting filled. If your order gets rejected, and the price moves against you whilst you are waiting for the order to be resubmitted and filled, then this represents an additional cost, often referred to as an opportunity cost. So, one venue may look like it is offering the tightest pricing, but if 50% of the time you try to hit these prices you get rejected, and also find that any subsequent price action to be moving against you, it may not necessarily be the optimum choice. Again, we explored this in our recent research that presented our ‘Total Cost’ framework, a model for bringing all of these different components together to help with quantifying and managing this 3-factor balancing act.

Speed of trading

Remaining focused on the subject of market impact, the speed that you, or for example, the algo you choose to use, execute is another trade-off. This may not be a problem if your trading objective is crystal clear and singular in nature, for example, buy 500m of USDJPY to get done as quickly as possible. In this case, you may not care at all if you create significant market impact. However, in our experience, we find that the trading objectives for the majority of buy-side transactions tend to be more nuanced and there is a desire to not create too much impact. At this point, decisions have to be made on how quickly to execute your order – too quickly and you increase the risk of impact, too slowly and you run the risk of the market moving away from you and once again creating an opportunity cost. As with constructing a counterparty panel, there is no one simple answer to how quickly you should trade. It depends.

Timing

Another potential conundrum arises with trade timing, at least for those that have discretion in their trading. There are a number of aspects to this, for example, the classic of whether to wait to net positions or not. Netting may produce overall less spread costs, but the waiting time for orders to flow through from the OMS to the EMS to allow netting creates our old friend, opportunity cost. If this potential opportunity cost is more than the spread saving, and you don’t have a strong market view, then it may not be worth waiting to net. Equally, the timing of trades to access better liquidity pools during the day, and hence lower spread costs, can be plagued with the same dilemmas. For example, a US asset manager who has a number of Asian NDF orders arrive on the desk late in the NY trading day may obtain a better outcome by waiting to execute the following morning in Asian trading hours to access better liquidity, but only if this spread saving outweighs the potential opportunity cost.

Conclusion

This article has sought to summarise some key themes within the best execution process that are all subject to balancing conflicting outcomes. Frustratingly, none of these have simple, solvable equations to provide a single result. We are not living and trading in the world of the Hitchhiker’s Guide to the Galaxy, where the answer to the Ultimate Question of Life, the Universe, and Everything is 42. So, how do you cope with these grey areas and complexity? The answer lies in the use of data and analytics, to help quantify the outcomes and thereby allow more informed decisions to be made that can be justified. At BestX we designed the product to deal with ‘shades of grey’ through the use of configurable BestX Factors and corresponding traffic lights to provide a qualitative summary of the quality of execution (although it all still looks like different shades of grey to the colour blind amongst us). Conscious of the potential complexity created by multiple execution factors, not all of which are explicitly obvious, and the sheer volume of data, we also provide automated outlier detection. Thus, once you have decided what is important to you, BestX will notify you of any transactions that breach your specified comfort zone for the value of a given execution factor.

Implementing a data-driven technology solution for best execution is only the start. Once you have this in place it allows you to conduct controlled experiments, perform scenario analysis, leverage trader experience and intuition and then analyse the outcomes rigorously with no bias. This provides the foundation for a best execution process that can be iterated, refined and improved as the knowledge bank and datasets expand and deepen.

A framework for aggregating total cost of execution in FX

Our latest research article introduces a theoretical framework for aggregating all key components of execution cost into one, holistic measure. Analysing spread cost alone, or the impact from information leakage in isolation, only provides a piece of the puzzle. In addition, it is key, where the data is available, to also compute and include the opportunity cost from unfilled orders. Managing costs and achieving best execution is a complex and delicate balance between spread, impact and opportunity cost. Aggregating all three components provides a total cost of execution, thereby allowing more meaningful comparisons and decisions to be made.

Please email contact@bestx.co.uk if you are a BestX client and would like to receive a copy of the paper.

High Frequency Volatility Measures: An Exploration

In our latest research we explore the arcane subject of comparing different methodologies for measuring high frequency volatility. This can be more art than science, but a robust intraday volatility measure is extremely useful for many aspects of modelling.

Within the BestX framework, we use such a metric to help estimate opportunity cost within our Pre-Trade module. Going forward we also plan to introduce a machine learning based model to help predict intraday volatility regimes, and this work serves as a building block for the more complex models to come.

Please email contact@bestx.co.uk if you are a BestX client and would like to receive a copy of the article.

Measuring the impact of the Turn in FX markets

“Turn and face the strange”
David Bowie

In our latest paper we continue our research into measuring the impact of the ‘turn’ within the FX markets. This somewhat strange phenomenon, which manifests itself around key dates throughout the year, is generally caused by supply and demand for funding by large financial institutions, which can create dislocations in the forward curves for certain currencies.

In this latest research we empirically measure the impact of the turn around a range of different dates, including year, quarter and month end, but also event days such as NFP, FOMC etc. In summary, we find that the impact is most significant for year and quarter ends, with, for example, an average magnitude at year-end of between 0.6 and 1.5 bps for EURUSD depending on the tenor of the transaction. The work has helped us prioritise where to adjust the forward curve interpolation to better estimate mid for broken dates.

To receive a copy of the paper please email us at contact@bestx.co.uk