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IHS Markit and OpenGamma join forces on UMR compliance

Collaboration between IHS Markit and OpenGamma aims to provide end-to-end support to clients for UMR compliance.

Margin optimisation specialist OpenGamma and analytics provider IHS Markit have confirmed a new partnership to support compliance for mutual clients with margin rules.  

The deal will see OpenGamma’s pre-trade margin analytics combined with IHS Markit’s post-trade derivatives calculation service, to provide end-to-end support for a range of entities which both firms mutual clients will have access to.

“Our forward-looking solution, powered by highly-accurate margin analytics and calculations, can effectively streamline margin workflows and OTC derivatives trading to enable cost mitigation,” claimed Hiroshi Tanase, executive director at IHS Markit.

The partnership was formed following a one-year delay to phases five and six of the uncleared margin rules (UMR). The final two UMR phases will now go live in September 2021 and September 2022. Both firms agreed that as numerous institutional asset managers will come into scope of the rules as they are introduced, demand for tools that help reduce the cost of posting margin will increase.

“Asset managers are currently working out how to best use the time afforded to them by the UMR delay,” said Peter Rippon, CEO of OpenGamma. “IHS Markit is one of the very few firms that has the proven pedigree in this area. Together, our combined solution offers full coverage for both cleared and bilateral derivatives.”

Blog, Insights

How to use option value to optimise your margin in current market conditions

We have had a number of clients contacting us lately asking for help in understanding option value (or net liquidating value) and what impact it has on their margin. What has become clear is that the volatility being caused by the coronavirus pandemic is causing large changes in option value, and consequently sudden changes in margin requirements. Market participants know that margin is affected by the parameter changes that the CCPs are making on an almost daily basis, but the impact of option value is less well understood.


What’s the issue?

Any portfolio that includes option positions may have option value that can be used to reduce the margin payable. The more of this credit that is used the greater the margin efficiency that can be achieved.

The way in which this margin offsetting works isn’t well understood. And the increased market volatility means that options that were previously out of the money will become in the money (and hence have option value) and vice versa.

This change in option value could also mean that a lot of firms are missing out on what is basically ‘margin free’ risk: the availability of a credit option value could  mean that additional positions can be opened without paying any additional margin.


What is meant by option value, how does it impact margin and how can its use be optimised?

For premium paid up front options, the option value is included in the overall margin calculation. credit option value can be used to offset initial margin, whereas debit option value must be covered by collateral in the same way as initial margin.

Before looking at how option value in your portfolio can impact margin, and how the use of it can be optimised, it’s probably best to define what we’re talking about. There are a few different terms that you may have heard:

  • Option Value
  • Equity Value
  • Net Liquidating Value
  • Premium Value.

These are all just different names for the same thing: the current value of any options where premium is paid up front in your portfolio. What is important here is that it isn’t all options. Some options are what is called “futures style”, with daily profit and loss being paid on them, and these do not have option value. As an example ICE Financial options are all futures style.

The calculation of the option value, or Net Liquidating Value (NLV) as it is called by the majority of exchanges, is a simple one:

NLV = position * contract size * price

Where position is the number of lots of the option held, with position being positive for a long position and negative for a short position. So, if you have a long position you will have credit NLV and if you have a short position you will have debit NLV.

Not all options are premium paid up front. A number of options are futures style, with Realised Variation Margin being paid on a daily basis. The options that fall into each category at the major CCPs are as follows:

CME – all premium paid up front (CME, CBOT, COMEX, NYMEX)
ICE Clear Europe
Financials (ex LIFFE) – futures style 
Equities (ex LTOM) – premium paid up front 
Commodities (ex LIFFE Commodities/FOX) – futures style 
Energy (ex IPE) – futures style for majority, but premium paid up front for those which are copies of CME contracts 
Fixed Income (PFI01 Liquidation Group Split) – futures style 
Equities (PEQ01) – premium paid up front 
Premium paid up front
JSCC (for OSE)
JGB – futures style
Equity and Equity Index- premium paid up front
In general, US options are premium paid up front. Other countries tend to follow the rule that equity and equity index options are premium paid up front, whilst all other options are futures style.


What is the impact on margin?

When most people talk about margin they are thinking of initial margin. This is the calculation that estimates potential future losses, using algorithms like SPAN or Prisma. But it is actually the net margin that has to be collateralised:

Net Margin = Initial Margin + NLV

It is important to get the signs correct in this calculation. Initial margin is always a debit, so if you have debit NLV then it will increase your net margin, but if you have credit NLV it will reduce your net margin.

There are limits on the initial margin that can be offset by credit NLV. Generally the use is restricted to the same group of products. So for example, if you have an excess credit on an equity position with a CCP you may find that you still have to pay the full margin on your fixed income position with the same CCP.

As can be seen above, if you have credit NLV then you can reduce your net margin. This means that the option value in your long option positions can be used to reduce the amount of margin that you pay. Not forgetting though that if you have a lot of short option positions they will increase your net margin.

And the value of the credit that you get from the NLV can be significant. In a portfolio consisting of a large proportion of long options, the credit NLV can often be larger than the initial margin, meaning it is completely offset, and your margin requirement will be zero.


How is the current market volatility changing this?

Many market participants will be holding option strategies where they expect the options to be out of the money. But with big swings in the underlying futures price, they may find that these options are suddenly in the money. And this means that they will now have option value that will impact the net margin.

If these option strategies have long option positions then this will be a plus, as the overall margin requirement will be reduced by this option value – maybe offsetting some of the margin increase resulting from recent parameter changes.

However, if the options are short then the net margin will increase by the option value. This can lead to a large increase in the margin requirement for the impacted firm, and may even cause liquidity issues.

Similarly, if a firm holds long option positions that were in the money, and offsetting their initial margin, they may find a sudden increase in their margin requirement if these options move out of the money.

Firms need to be aware of the impact that changes in option value can have on their margin requirement, especially in current market conditions where there can be significant jumps.


What about optimisation?

Unfortunately, you don’t benefit if your credit NLV more than offsets your initial margin. CCPs don’t give you any money back. It’s a case of ‘use it or lose it’. Consequently, you might like to consider trading some additional futures. The initial margin could be completely offset by the available credit NLV, meaning that your net margin could still be zero.

In order to benefit from this you need to understand the breakdown of your margin and the spare capacity you may have in your portfolio. But watch out – with the markets this volatile that option value could soon disappear.


So what have we learnt?

First of all, from the questions we have received, it is clear that the impact of option value on margin requirement is not generally understood. The current market volatility, which has led to big changes in option value has clearly highlighted this issue.

Once you understand how option value is used by CCPs, then it is easier to see how it is potentially another tool in optimising margin, and hence returns. The only problem is that, in the current market, the option value that you have one day could easily all have disappeared by the next.


DRW adopt OpenGamma’s analytics to drive efficiency and support treasury function

OpenGamma, a financial technology company, today announced that DRW, a major global principal trading firm, will use OpenGamma’s analytics in its treasury function to manage derivatives margin.

DRW was attracted to OpenGamma’s leading software-as-a-service (SaaS) solution as it seeks to expand its treasury capabilities in response to regulations such as Uncleared Margin Rules (UMR). 

UMR regulation was set in motion at the 2009 G20 meeting following the global financial crisis and requires firms using over-the-counter derivatives to post margin on those transactions. These regulations require any trading strategy that uses leverage to optimize and be efficient with its use of collateral. OpenGamma’s solution will enable DRW to improve treasury processes by regularly evaluating alternative ways to put on new trades across both clearing houses and bilateral counterparties.

“We’ve been looking at technology solutions that can help us to adapt the processes we use for financing and cash management,” says Mark Wendland, Global Head of Treasury at DRW. “OpenGamma’s solution gives us unique coverage for the products we trade, and it will add efficiency to our trading operations.”

OpenGamma CEO Peter Rippon added, “We are incredibly proud to have been selected by DRW, a company with a visionary approach to driving efficiency in their operations.”


It’s 4.15 somewhere

The late 1980s saw a huge change in financial markets, with the ‘Big Bang’, the deregulation and restructuring of financial markets, leading to the electronification of trading and arguably to ‘Black Monday’ (October 19th 1987), a global markets, single day, fall of in excess of 20% (note: This event is routinely used as a staple in bank stress testing). Understandably, the decade saw a significant increase in volatility and market risk becoming a critical concern for major trading banks. One response to this, from the largest banks, was a heightened need to know the market risk position as soon as possible following the close of the trading day. One such institution was JP Morgan, whose chairman, Dennis Weatherstone, wanted a report, and metric, that captured the complexity of the market whilst providing a market risk view for the following day. This report was required by 4.15pm and became known as the 4.15 report.


The 4.15 report became, in fact, the first Value at Risk (VaR) report and was a sensitivity-based calculation designed to take into account correlations and offsets and give a final number that was ‘the maximum loss that can be expected over a specified time frame (1 day, for example), with a specified confidence level (99% for example). Over the following years, the VaR methods were improved, to include Historic and Monte Carlo flavors, and the measure was adopted enthusiastically by regulators and banking bodies such as the Basel Committee on Banking Supervision (BCBS). This adoption has effectively ensured that VaR is the central plank for market risk management globally.


Two decades after Black Wednesday, the world’s financial markets faced the 2007/8 Credit Crisis and ‘Great Recession’. Essentially, market risk had transformed into credit risk across the market as a whole. The regulatory response to this was to (over) collateralize the system through central clearing, significant changes to bilateral trading agreements and BCBS driven bank capitalization guidelines. Governing bodies throughout the world put new stress testing and capital adequacy rules into place to adhere to these guidelines, the final phases of which are still coming on-stream, such as VaR based margining for futures and Uncleared Margin Rules (UMR) phases rolling through.


Now, in 2020, the financial markets as a whole are looking at the intended and unintended consequences of 40 years of change, crisis, response and repercussion (credit risk been transformed into liquidity risk) and we can make reasonable predictions as to where risk management and mitigation sits. One clear area for focused attention is Liquidity Risk.


The new margin rules mean that firms from the buy side now have a greater than ever responsibility to collateralize the system, through Initial Margin (IM), the collateral called to cover the ‘risk’ of the derivative (the amount that could be lost between failure to pay variation margin and the exchange being able to flatten the position) and Variation Margin (VM) the collateral called to cover the daily change in value of the derivative (essentially the daily P&L). These calls can be a considerable drag on liquidity for asset managers, who have to make sure that funds needed are available to cover them as they become due to post. To do this, funds have to be able to accurately forecast what that margin is likely to be the day before it is called.


With this need in mind, we should ask whether the 4.15 report for the 2020s is a liquidity report, which allows treasurers and cash desks to ensure that funding is in place, in the most cost effective way, to post as and when the margin calls are made the following day. To see what this is likely to entail, we can take our cues from four decades of evolving market regulation that has led us to this current need.  


Know tomorrow’s cash requirement today – predict accurately and early

Given that we know our market position, and the current state of the market, we should have the tools we need to accurately predict what tomorrows IM and VM are likely to be. To do this, we can value the portfolio as is, using that market data, to get to the VM, and we can apply a T-1 set of risk parameters to generate the IM. This should be sufficient, given that the idea of IM is not to be driven by single day moves in the market, but more to reflect the riskiness of the market over time.


A nuance of the margin process is single currency margining, where funds can pay multiple currency calls in their local currency. This can be a costly way to avoid the complexity of meeting multiple calls in different currencies. One of the chief benefits of forecasting ahead is that the forecast itself can be reported by currency, providing total control of how margins are posted. The opportunity to actively deal with multiple currency margin posting becomes an important bonus of margin forecasting.


Know how much cash buffer is needed – Don’t be caught off guard by normal market volatility

In a similar way that banks understand the market risk to their balance sheet value, we should be able to estimate the reasonable amount of liquidity that we would need to meet margin calls, outside of extreme situations, going forward. A kind of Margin at Risk (MaR) number would give us a high level of confidence in our short-term liquidity capabilities.


Know how bad the margin call could get? – Be prepared for market extremes

Systemically Important Financial Institutions (SIFIs) are asked to run stress tests to understand how their liquidity positions could be impacted in various pre-defined stressed scenarios. They are then asked to create Liquidity Contingency Plans to show how those liquidity impacts would be dealt with. Buy side funds need to understand, in the same way, what a stressed margin call might look like, and how they would deal with it. Running defined stress tests has to be a feature of Active Margin Management.


In summary, we can look at time between the 1980s to the 2020s from many angles, but one has to be deregulation and financial market stimulus leading to bank held market risk transforming, over that time, into buy side liquidity risk, largely through regulation. The result is, though, that the 4.15 report remains as important as ever, but now as the key risk report for buy side, rather than sell side, treasury.

Insights, Research and reports

Interactive survey: Broker scorecarding in 2019

Comparing your brokers and their execution strategies can be incredibly insightful – and of immeasurable value to your business.

We reached out to our buy-side clients to learn more about the broker review landscape in 2019. Over two months we carried out face-to-face interviews with professionals from 22 buy-side firms, with a particular focus on why firms carry out broker reviews, current processes, challenges faced and plans for enhancement.

Our latest report highlights the key results and trends from our surveys – to give you a better gauge of where you fit into the market, and what else you can be doing to benefit your business that you may not have already considered.

What you can learn:

  • Understand the drivers behind broker reviews.
  • Learn how often other firms carry out reviews.
  • Understand how – and with who – firms perform their reviews.
  • Gain insight into what goes into reviews and how they work.
  • Find out what the future holds for reviews: how many firms plan on enhancing and how.

Download our interactive guide by filling in the form on the right >>

Insights, Research and reports

Asset Managers could save millions through clearing ahead of UMR

Our latest research shows asset managers pulled into phases IV and V of the Uncleared Margin Rules (UMR) will be able to save up to 53% in initial margin when clearing compared to uncleared margining. 

UMR which, among other requirements, mandates margining rules for trades that are not cleared by a central counterparty (CCP) has caused great collateral inefficiencies for many in-scope firms. For asset managers with portfolios above €750bn in notional, clearing a greater volume of OTC trades frees up potentially millions of dollars worth of assets to put to use elsewhere. 


Our latest research shows asset managers pulled into phases IV and V of the Uncleared Margin Rules (UMR) will be able to save up to 53% in initial margin when clearing compared to uncleared margining.  Click To Tweet


The findings come as the industry continues to prepare itself to post an eye watering $2 trillion more margin as a result of the rules, the final phase of which has been pushed out until 2021. This means that thousands of asset managers, that have previously never had to post margin, will have much needed additional time to get their operational houses in order. 

In response to the research our CEO Peter Rippon said “The overarching goal of UMR is to strongly incentivise asset managers to stop trading bilateral uncleared derivatives, and shift towards central clearing.”


“Unfortunately, it’s not as simple as just deciding to clear, firms then need to decide where to clear. A derivative may be eligible to clear at numerous venues, but an asset manager then needs to factor in liquidity and whether they have an existing position, not to mention any pricing discrepancies between the clearing houses.” 


He concluded: “The trouble is, at a time when investors are putting fund performance under the spotlight following Neil Woodford’s woes, the last thing asset managers need is to be restricted from delivering strong returns. This problem can be solved, which is why we are seeing more firms carefully considering the differences in the margin calculated, and level of margin that will be required for UMR.” 

Interested in how this saving can be achieved in greater depth? Take a look at our blog on how leveraging other a variety of optimisation techniques can save you up to 80% on the cost of margin

Blog, Insights

Swaption risk in SIMM: variability of inputs

Following OpenGamma’s SIMM webinar (which you can watch here), I wanted to add a little bit more colour regarding the Initial Margin (IM) of swaptions in the SIMM(R) framework. 

In SIMM, the ‘S’ means standard. However, many people asked “how can this be for a liquid instrument like a swaption, when the numbers can be very different between counterparties?”

Yes, the ‘S’ in SIMM means Standard, but the standardisation is related to the computation of the IM based on sensitivities (Delta/Vega), not on the computation of the sensitivities. The sensitivities in SIMM are considered the inputs and not part of the methodology.

This variability of inputs is true for all models and all products. This is the case even for plain vanilla swaps where the sensitivities (bucketed PV01 or key rate durations) are strongly dependent on the interpolation mechanism. The high correlation between nodes in the risk weights/correlations approach selected for SIMM certainly lessens the impact on the IM, but it would already be present there.

So, I have decided to present the results for swaptions for several reasons. 

  1. A personal reason: I’m an interest rate quant and I found my inspiration in that risk class when, almost 15 years ago, I wrote a paper titled ‘Swaptions: 1 Price, 10 Deltas, and… 6 1/2 Gammas’ (Henrard (2005)) which was describing the same phenomenon that is at play here. 
  2. A technical reason: The range of models used for swaption pricing is maybe larger than for other asset classes. With rates potentially going (or being) negative, the Black/log-normal model is not the only starting base. The models used for swaption pricing and risk management range from Black (1976) to Bachelier (1900) going through the very important SABR (Hagan et al. 2002). I could have added the Hull and White (1990) model, but in terms of delta the figures would have been almost equal to the Bachelier model.

SIMM and models – delta

How does SIMM work when the users adopt different models for the valuation of their derivatives? 

For the delta, the inputs are sensitivities to the different vertices of the relevant yield curves. The sensitivities are the partial derivatives with respect to the market rates of outright swaps multiplied by one basis point (SIMM methodology V2.1, C.2.20). The practical meaning of partial derivatives is how the present value changes when the underlying changes. This is exactly what the model does; it explains how the present value of the derivative is impacted by the change of the underlying price. It is not surprising that the delta will be strongly dependent on the model. 

The models I will use in this blog are the Bachelier, the Black and the SABR model. For SABR, there are a lot of potential parameterizations and I restricted myself to one with beta (the elasticity coefficient) equal to 0 and one with beta equal to 0.5. From historical data analysis presented in Henrard (2005), beta=0 is a reasonable choice in terms of delta hedging and beta=0.5 has been a popular choice in banks. With beta=0, the local volatility part of the SABR is similar to the Bachelier one and with beta=1 it is similar to the Black one.

For all the tests presented the following methodology was used: A realistic data set for USD curves and USD swaption prices, including smile, is used as a starting point. The SABR models are calibrated (with alpha, rho and nu parameters) at best to that set of data for each expiry and tenor considered. This is typically how the model would be used in practice. For each swaption considered, the implied Black volatility and implied Bachelier volatility are computed. We don’t use a grid of implied Black/Bachelier volatilities and interpolate in the grid but we recompute the implied volatility on the fly for each trade. There is no interpolation approximation for the comparison between models. The present value of each swaption considered is the same for all models. This is equivalent to the ‘one price’ part of the above mentioned article.

How are the deltas different in practice? 

I will start with the example of long 1Yx5Y USD payer swaptions (standard conventions) for a notional of 1 million. I looked at two moneyness, one ATM and one OTM with a simple moneyness of +100 bps. 

I will illustrate this using a couple of graphs first and will present more extensive tables later. 

The total deltas (sum of bucketed deltas) are represented in Figure 1. Obviously with the delta larger for ATM than for OTM options and the left side of the figure as a different scale with larger numbers.

What is important for this analysis is that the deltas are significantly different between the models. The highest number (Black) is 23% higher than the lower (SABR 0). The relative difference is even higher for the out-of-the money option; the higher number is 77% higher than the lower one.

SIMM and models – vega

The issue of the diversity of the model is a little bit more complex for the vega. For SIMM purposes, the vega is defined as the partial derivative of the present value with respect to the implied volatility of the model. The SIMM language (paragraph C.3.30) indicates that for interest rate, the implied volatility can be “the normal volatility or log-normal volatility, or similar”. In all cases, the volatility changed is the ATM volatility “while keeping other inputs, including skew and smile constant”. 

Our interpretation is that for Bachelier and Black we apply a shift of the implied volatility and for the SABR model, we apply a shift to the alpha parameter. Obviously, those different shifts will produce very different vegas, but the SIMM vega inputs computation does not stop there. The vega so computed is then multiplied by the implied volatility used (paragraph 8.10 (c) ). The vega is rescaled by the value of the number (volatility) on which it is based. This somewhat brings the numbers roughly in line; at least they are of the same dimension. 

For the same options as in the previous section, we have computed those numbers in Figure 2. For the ATM example, all the rescaled vega are within 1% of each other. For the OTM case, the difference reaches almost 25%.

SIMM and models – IM results

From an IM perspective, the important thing is not the inputs but the output, i.e. the margin itself. The margin is composed of 3 parts, the delta IM, the vega IM and the curvature IM. The curvature IM is computed from the vega input using the relationship between gamma and vega in the Black model.

Once more we have used the same two examples to compute the IM. The results are presented in Figure 3. In the ATM example, the total IM is up to 15% higher in the most costly model (Black) than in the least costly one (SABR 0). In the OTM example, the total IM is up to 46% higher in the most costly model (Black) than in the least costly one (SABR 0). 

IM tables for other expiries, tenors and moneyness

The tables below highlight IM numbers for different expiries, tenors and moneyness. There are three tables, one with 1Yx5Y swaptions (as in the previous examples), one with 5Yx5Y swaptions and one with 10Yx10Y swaptions. For each of them we have listed five levels of moneyness, from -100bps to +100bps in 50bps steps.

Each table is divided into 4 parts; one for the Bachelier model, one for the Black model, one for SABR with CEV coefficient 0, and one for SABR with CEV coefficient 0.5. All the figures represent the IM computed with SIMM. The IM is divided, as per the SIMM methodology, between delta, vega and curvature. 

If we look at the last table, for the 10Yx10Y swaptions, we see large discrepancies. For example for the swaption with a moneyness of 100 bps out of the money (above ATM), the delta IM varies between 5.25K and 20.25K, a ratio of almost 400%. The total IM is not showing such a ratio but still exhibits a ratio of 200%. This means that with the same market data and the same trade description, the IM can double dependent on the option model. 


When calculating Initial Margin (IM) in relation to Uncleared Margin Rules (UMR), the most used methodology is the Standard Initial Margin Methodology (SIMM). The method is standard is the computation of IM from the trade sensitivities but the computation of the sensitivities is done by each counterparty according to its own methodology and not standardised.

In this note, using the example of vanilla swaptions and a set of standard models, we show that the resulting sensitivities and resulting IM can be very different, even for a single trade. There is no market wide standard that tells the market participants which one is better or which one should be used.

The SIMM figure reconciliation obviously require a back-office-like reconciliation of the trade population but also a quant-like reconciliation of the way the sensitivities are computed. It is almost impossible to understand the difference in IM figures between participants without an in-depth understanding of which method or model was used by each counterparty to generate the sensitivity. This excludes de facto any black-box approach or a process where the details of the model and its implementation are not available.

In our examples on swaptions, the IM numbers are in some cases more than doubled by changing the model used to compute sensitivities. The impact of those discrepancies are large enough to create cases where the posting counterparty believe that he is under the 50-million threshold but collecting party computations figures are above the threshold.


Bachelier, Louis (1900), Théorie de la Spéculation, PhD thesis from the Ecole Normale Supérieure.

Black, F. (1976). The pricing of commodity contracts. Journal of Financial Economics, 3(1-2):167€“-179.

P. Hagan, D. Kumar, A. Lesniewski, and D. Woodward. (2002) Managing smile risk. Wilmott Magazine, Sep:84-108, 2002.

Henrard, Marc (2005) Swaptions: One Price, 10 Deltas, and… 6 1/2 Gammas. Wilmott Magazine, pp. 48-57, November 2005

Infographics, Insights

Infographic: Broker Scorecarding in 2019

Broker scorecarding is still a fairly new practice, and many businesses are still unsure how often to perform it, why it’s worth doing, and what results it can bring.

We reached out to our buy-side clients to learn more about the broker review landscape this year, to put together our interactive report: ‘Broker scorecarding in 2019’.

Our infographic highlights the key takeaways of that report, so that you can better see where your firm fits in within the review landscape.

Insights, Research and reports

2019 UMR Impact Analysis Research Report

This research report summarises the findings from OpenGamma’s ‘Uncleared Margin Rules (UMR) impact analysis’ that we performed on behalf of 15 clients over the last 6 months.

The results are anonymised and aggregated by firm type, but help to quantify the margin impact of SIMM on the industry.

The report also outlines the steps firms can take to minimise the impact of SIMM, such as voluntary clearing and other optimisation approaches.

Download the full report to read:

  • Full UMR impact analysis for 15 firms, including 6 hedge funds, 6 asset managers and 3 pensions funds.
  • How SIMM and Cleared margin requirements impact funding costs.
  • How to optimise margin under UMR, including:
    • The regulatory schedule approach
    • ‘Cherry-picked’ backloading
    • Synthetic risk moves
    • Remaining below the $50m threshold.

We hope you find the research useful. If you have any questions or want to find out more contact us here >

Insights, Research and reports

Poor broker performance reviews leaving fund managers in the dark

Originally published at CityAM


Fund managers may not know how much money they are spending with brokers thanks to a failure to properly review the relationships, a new study has found.

Despite the majority having procedures to review broker relationships in place, only 11 per cent of fund managers actually assess how all their brokers are performing, according to a report by analytics fintech OpenGamma.

The vast majority of the 22 investment management firms surveyed (86 per cent) had a broker review process in place, but there was no industry consensus around how often the reviews should be carried out.

Almost a third (31.8 per cent) of firms said they held reviews at “mixed/multiple” during the year, while just over a fifth (22.8 per cent) held them “semi-annually”.

The majority of the are reliant on manual processes to review broker relationships, with only 21 per cent using live data in the review process.

The biggest challenge when conducting reviews was gathering data and calculating revenue, the report found. Over half – 53 per cent – of firms said they found carrying out broker reviews “very time consuming”

 “Having a process for assessing how brokers are performing is without question very valuable, but only when carried out,” said Opengamma chief operating officer Maxime Jeanniard du Dot.

“While regulations will be a big driver in reviewing broker performance, fund managers also have a strict fiduciary responsibility to investors,” he continued.

“On top of this, as the geopolitical landscape begins to take shape over the coming months, it is clear that fund managers will need to gain a new level of insight to understand the best brokers to do business with.”

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