VWAP for futures and equities
Trade information in many futures and equities markets is published readily over market data feeds and it is straightforward to calculate an accurate VWAP price by taking a weighted average of eligible trades. As a result, VWAP benchmarking can be easily measured historically and in real-time, which is useful for asset managers in building models or calculating slippage versus expectation. As well as being easy to benchmark, VWAP trading, or trading proportional to market volume, has been shown to reduce market impact compared with many other execution styles. This makes VWAP an algo which is applicable when an interval average price and reduced market impact are acceptable outcomes.
A specific use-case in equity markets is when trading a market neutral basket of tens or hundreds of stocks, each with different liquidity attributes. VWAP execution is able to keep the basket reasonably balanced while reducing market impact. Another use-case is found in futures markets where some products, such as corn, crude oil or Eurodollar, use a VWAP price as the daily settlement price. Executing close to this price becomes important for funds tracking settlement prices.
In spot FX, the dominance of traditional styles of execution such as risk-transfer or fixing execution has resulted in arrival price or spread based metrics being utilised by asset managers.
The VWAP algo
There are two main types of algos which target VWAP, a participation VWAP and a schedule VWAP. This article will focus on schedule VWAP, but in brief the participation algo trades along with real-time volume in the market and looks to be a percentage of this activity until the trade has finished. The execution will usually be close to the VWAP price over the window it has traded.
A schedule based algo, targeting VWAP over some trading interval, is the more common execution style in spot FX. Typically the interval will be defined by the user or determined by the algo based off quantitative models. Then, to accurately trade VWAP, the algo needs to know the volume distribution over this interval. As this cannot be known in advance, a historical distribution is used to forecast expected activity. This means VWAP trading is more complex than other schedule based algos, such as Time Weighted Average Price (TWAP), which has a constant schedule.
The additional scheduling complexity of VWAP leads to greater variance in performance for the algo than that of a TWAP algo. The performance of the VWAP execution could be significantly better or significantly worse than the market VWAP if the trade schedule is a poor estimate of the actual market volume. For example, in Figure 1 we have two schedules, a schedule from a VWAP algo and the schedule corresponding to actual market volume. This example shows that the algo is trading too much at the beginning at a lower price and too little at the end at a higher price. Overall the algo has an execution lower than the market VWAP, and it will depend on whether the order is a buy or sell as to whether this is a good or bad outcome.
Volume distribution and VWAP schedules
The accuracy of the forecast for volume distribution over the trading interval directly affects the performance of a VWAP algo versus the VWAP benchmark. Although there is some benefit in the algo using micro-structure models to have discretion around the schedule, an accurate trade schedule is the most effective way to get consistently close to the VWAP benchmark.
- Frequency that the distribution is updated, e.g. weekly, monthly, etc.
- Granularity of the distribution e.g. 15 sec, 1 min, 5 min buckets.
- Dynamic granularity around important times of day such as London 4pm fixing.
- Number of trading days that make up the distribution. Usually more than one month but less than three months and can be variable by currency pair.
- Separate distributions for known structural differences such as: day of week, end of month, bank holidays, economic events, etc.
- Whether smoothing or raw data is used for the distribution.
- Dynamic updating of the distribution shape during unexpected volume increases. Note that it is only the shape of the distribution that impacts the schedule and not the absolute levels.
- Stability of volume distributions for less liquid currency pairs.
To consistently get close to the VWAP benchmark, an accurate forecast of volume distribution is needed. For liquid currency pairs, where activity is reasonably consistent, the volume distributions have a relatively small variation across typical days. However, there are many currencies which exhibit large variations in liquidity and the corresponding volume distributions are also highly variable. Traders using VWAP should note that using an algo based on highly variable historical distributions can result in having unnecessary market impact. A maximum participation parameter is one way to ensure impact costs are reduced when volume distributions are variable.
When is VWAP useful in FX?
In spot FX, the dominance of traditional styles of execution such as risk-transfer or fixing execution has resulted in arrival price or spread based metrics being utilised by asset managers. Although there are venues where FX trade data is published, the absence of a comprehensive trade feed results in VWAP benchmarking being less accurate than in equities and futures. Both of these points make it unsurprising that VWAP benchmarks are less popular in FX, although there are some natural situations where VWAP execution is beneficial. To list a few examples:
- A trade with the aim to execute where other market participants have traded.
- A large order from the Asia session into the London morning session.
- A trade in an emerging market currency with the aim to reduce market impact and capture spread.
Example 1 is the classic case for VWAP trading. Of course, more opportunistic execution styles might be an option here as well, but for reducing impact or trading over a long interval, VWAP is a good choice. In the second example, a VWAP algo will distribute more volume into the higher liquidity London morning than during the Asia session. This will reduce market impact compared to other algos such as TWAP. Similarly, in the third example there is reduced impact by scheduling volume during more active trading periods and opportunities to capture spread using smart micro-structure models. In this example, due to the variability of volume distributions, a VWAP with dynamic scheduling or a maximum participation rate constraint is preferred to an algo solely reliant on static scheduling.
These examples demonstrate when VWAP is a useful trading option, but in each scenario a trader will need to consider the benefits and risks of using a VWAP for execution. Any algo executing over a time horizon has risks of adverse price movements versus more immediate execution. Practitioners should also be aware of other considerations not discussed including choosing optimal trading intervals; automatic limit prices; participation rates; anti-gaming logic; algo schedule discretion; measuring VWAP performance; influencing VWAP; and tilted and dynamic schedules. However, when used correctly VWAP has value for asset managers seeking to execute along with the market, to reduce market impact and to save on spread costs.
