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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.

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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

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Summer’s Over and it’s Back to Work

There are so many signs that summer is over; the weather suddenly feels autumnal, the commuter trains are full again and everyone is back in the office. So, it’s time to start thinking about all the things that financial firms need to be addressed over the next few months. And there are quite a few of them…


  • A number of CCPs have planned upgrades to their margin algorithms which need to be supported. 
  • UMR may have been delayed for some market participants, but there is still a lot to do even if your timescales have been extended. 
  • And these changes are happening at the same time as other events such as Brexit and Libor replacement


…. so there’s a lot to think about.


All this means that margin optimisation should now be considered a requirement if you want to keep making profits in a difficult market. And, with the continued implementation of UMR, suitable collateral to cover margin liabilities is going to be harder to source, so the lower the margin the better. 


Upgrades to Margin Algorithms

LCH beat the summer break with their upgrade to IRS margining, bringing the calculation of Unscaled VaR into line with the calculation of the Historical VaR. Now both are scaled from a 5 day VaR to a 7 day VaR for clients. This change had a significant impact on the margin for a number of firms.

CME have their own change to the VaR component of their IRS Margin algorithm coming soon. They are adding an unscaled component, using historical and hypothetical scenarios, to help avoid procyclicality. This will also have the added benefit of allowing them to discontinue pegging the start of their historic scenarios to the 1 September 2008, without losing the extreme moves of October 2008 which are driving the margin for a large proportion of portfolios. In addition, they are adding an Event Risk component which will allow them to cover the risk of specific known events, for example elections.

These changes at both LCH and CME don’t only result in the need for system upgrades. The change in the margin calculated potentially changes the balance for where it is cheapest to clear certain swaps something worth investigating given the possible savings.

Bigger still are the changes to the ETD margin algorithms that are coming soon at CME and ICE. Both will be moving from SPAN to VaR based algorithms. These are big changes, that will take a large amount of resources to implement. They will also significantly impact the level of margin calculated for a well hedged portfolio the margin should be lower, but for a directional portfolio it is likely to be higher. These algorithms are also less transparent than SPAN it will no longer be possible to use a provided Scanning Range to simply estimate the margin on a new trade for example. 

And CME and ICE aren’t the only CCPs looking to move away from SPAN. LME, for example, are well on the way to implementing their own VaR margin algorithm. Unlike today, when all the major ETDs (except for Eurex who already have their Prisma VaR based algorithm) are margined using SPAN, each CCP may implement its own algorithm. Firms need to decide how they are going to cope in this new, more complex, landscape.


What’s to be done for UMR?

Just before the ‘summer break’, changes to the timetable for the implementation of UMR Phase V were announced. Now effectively there is a Phase V for those with AANA over €50bn and Phase V1 for those over €8bn. Now it’s time for firms to put in place their plans for how they are going to meet these requirements. And there is a lot to do.

First there’s calculating your AANA is, and therefore confirming the phase you fall into. Once this is determined, you need to start looking at how you deal with the requirement to post margin:

  • Should you be considering clearing?
  • If you stay bilateral what algorithm are you going to use (SIMM or Grid)?
  • How can you optimise your trades to make best use of the €50m threshold with each of your counterparties?


What about current market events?

If you’re in the UK, all the talk is around Brexit. This is obviously causing some market volatility, but it also raises the question of where is the best place to clear? Now may be a good time to look at any potential savings from moving some of your exposure.

Taking advantage of the cross margin benefits provided by the major CCPs allows savings in margin for the same risk profile. There has already been some evidence of firms using this feature to optimise their margin. Moves in liquidity have been seen between the CCPs, be this additional swaps being cleared at CME and Eurex or an increase in the open interest in fixed income futures on Curve Global. 

Libor replacement is also having a big impact on the market. Each of the CCPs is coming up with their own process on how they are going to manage the change in reference rate, particularly the impact on existing swap portfolios. They are also introducing new Exchange Traded Derivatives to support these changes. Again, this could be a trigger for you to check whether it is possible for you to optimise the margin that you pay and therefore increase your return on capital.



So what does this all mean and what are the priorities?

First of all you need to decide what is relevant for your firm and what the impact is going to be:

  • Are you using any of the markets where the margin algorithms are changing?
  • Are you going to be caught by the next phases of UMR?
  • Are you trading products that are likely to be impacted by current market events, in particular fixed income derivatives?

And if you answered yes to any of these questions then you need to make sure that you have all the necessary supporting processes and systems in place, in particular have you got a solution that:

  • Supports the upgrades to the CCP IRS margin algorithms.
  • Can calculate the new VaR based ETD margin as well as SPAN.
  • Allows you to calculate and reconcile SIMM margin requirements.
  • Helps you to optimise your margin requirement by, for example, comparing cleared versus uncleared margin, or determining the cheapest CCP for new or existing trades.


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Christmas, Commodities and Margin

It’s nearly Christmas and we are all busy eating too much, buying presents and trying to fit in visiting friends and family. It’s also an expensive time of year, and how much this all costs is very dependent on the price of commodities, be it the cocoa used in all those Christmas chocolates, the gold and silver used in those special little gifts or the less expensive metal used in the puzzles preferred by my husband. And then there is the cost of the fuel required for all those visits.


But what has all this got to do with margin? Well, as well as the prices impacting the cost of Christmas, their volatility can also have a big impact on the margin you have to pay if you trade commodity derivatives.


Commodity prices follow the usual economic principles of supply and demand. The only difference is that supply can be severely impacted by external events, for example health scares surrounding cattle (such as foot and mouth), accidents causing mines to be closed, conflict impacting production, or weather ruining a harvest. And that same weather can impact demand; really cold and more fuel is needed for heating or really hot and more electricity is used to run air conditioning. 


The drone attack on the Saudi oil facilities in September is a prime example of how events can impact prices. The strike knocked out 5% of global supply.


This caused an initial intraday spike in Brent prices of 20%. By the end of the day, the move had dropped back to just under 15%, but this was still the biggest jump in 30 years.

So what is the impact on margin? Well, in the first instance any large price move will cause an intraday margin call based on the potential losses. In the case of the Brent move this would have been a call for all short position holders based on the 20% move. At the end of the day the actual profits or losses would be realised as variation margin. At this point the move was only 15%, so some traders may have found that some of the cash they provided intraday was returned, whereas others who had sufficient non-cash collateral intraday to cover the potential loss would find themselves having to replace this with cash to cover the variation margin.

The Brent move was large enough to instigate a margin review, with ICE for example increasing the SPAN scanning range by 20%. And because of the way that SPAN works, this would have led to a 20% initial margin increase for all position holders, not just those with short positions.


This is where VaR as a margin algorithm would have made a difference. The increased price volatility would have lead to general uplift in margins, but the big price move as a new historic scenario would only have impacted short position holders.


This highlights the big difference between SPAN and VaR; with VaR the margin is no longer symmetrical, with different amounts being calculated on long and short positions. And for commodities, where you often see a very directional distribution of price moves, this could lead to big differences in margins for different sides of the market.

Both ICE and SPAN are looking to introduce VaR margining for exchange traded derivatives in 2020 and are prioritising commodities. So for those who are potentially going to benefit from this move then maybe you can think of it as an early Christmas present!

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How liquidity add-ons can impact margin

I have previously written about how the majority of CCPs are using Filtered Historical VaR as the basis of their margin algorithms, usually alongside stress or unfiltered scenarios to avoid procyclicality. (You can read more here >)

But the algorithms of the main CCPs also have something else in common: liquidity add-on. This can be a significant component of the margin for large positions, so it’s important to understand how it is calculated – and how the methodologies compare – when considering your choice of CCP.

How is the Liquidity add-on calculated?

Let’s look at the liquidity add-ons of the four major swaps clearing houses: CME, Eurex, JSCC and LCH. At the core of each of these calculations is an attempt to estimate the additional cost beyond the standard market moves covered by the market risk component of the margin algorithm, of hedging or closing out a defaulting member position. And the larger the position the larger these costs are expected to be.

Each CCP will have its own way of determining ‘large’: looking at their own open interest and the depth of the market in general. They will also have different views of the likely spreads when trading the hedges.

Let’s look at each of the algorithms in a bit more detail:


A par rate delta ladder is calculated, bucketed by key tenor points. A hedge portfolio is then determined against these sensitivities.

The notional of this hedge relative to the market is then used to look up an implied spread.


A hedge portfolio is determined based on swaps with standard maturities across the different curves and currencies.

The sensitivities are multiplied by a bid/ask spread to cover the risk of spreads in the market when hedging a defaulting member portfolio. To cover additional market moves that may occur when hedging a large portfolio, the market risk (VaR) of the hedge portfolio is multiplied by a liquidity factor determined based on the relative size of the swap to the CCP view of market capacity.


First a hedge portfolio, expressed as a series of sensitivities, is determined based on standard curves and tenors.

For each hedge, a charge is applied based on the sensitivity above a threshold. A discount is then applied to this charge based on correlations between the tenors of each curve.


A delta ladder for the portfolio is produced based on basis, discount and forward curves. These sensitivities are then aggregated across the curves to obtain a total delta for the currency.

The total delta is then multiplied by a liquidity factor, the size of which is based on the level of the delta.

How do the numbers compare?

It’s ok to compare how the different CCPs calculate the liquidity, but the most important question is: what is the impact on margin? To compare them we need to consider a trade that can be cleared at all of the CCPS. And this will need to be a big trade to make sure that the liquidity charge kicks in. Let’s consider the following trades:

  • Trade 1 – 20 year EUR 300 Million Pay 6M Euribor v 2% fixed.
  • Trade 2 – 20 year EUR 3,000 Million Pay 6M Euribor v 2% fixed.

Margin comparison

The following table shows the liquidity charge for 3 CCPs:

It is interesting to see where the liquidity charge is very similar across CCPs (2 and 3 for the smaller trade), but also where it differs considerably. It also highlights how the liquidity charge is not linear as an example for CCP 3 there has been a 25 times increase in the liquidity charge for a 10 times increase in notional.

Overall, this analysis shows how the CCPs’ view of the cost of hedging and auctioning a defaulting member’s portfolio varies, and that unlike the VaR component of the margin there is no standard set of market indicators to refer to.

This is obviously a simple example. The liquidity charge will be dependent on the overall portfolio, but it shows how it can differ by CCP. And more importantly the impact it can have on the overall margin.

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Have you noticed a change to your Eurex margin?

On 31 May Eurex issued a notice circulating that, as part of their regular review, they were changing both the stress scenarios and the confidence level used in the Stress VaR component of Prisma their margin algorithm over the weekend of 8 – 9 June.

It would be interesting to know how many people read all the way to the bottom of this notice, including the attachment. Changing some parameters doesn’t sound too bad. And if you got as far as the PDF attachment, the first table showed various periods from which stress scenarios for the different product types were to be taken. Most of these looked familiar October 2008 for instance.

But it was in the second table that the real change was detailed. This contained the changes in the confidence level. Each Liquidity Group (set of similar products such as Equities, FX or Fixed Income) has its own set of parameters. For the majority of these the confidence level used in Stress VaR was actually slightly reduced. But for the Fixed Income liquidity group the level had changed from 92.67% to 94.28%. And this sort of change can make a big difference to the margin calculated.

The Fixed Income liquidity group level has changed from 92.67% to 94.28% Click To Tweet


So, what is the impact on margin?

As with any margin calculation especially one based on VaR it depends on your portfolio. For a simple portfolio in a single Euribor future we have actually seen a small decrease in margin, even though this margin is driven by the Stress VaR component. The change in the stress scenarios has slightly reduced the individual scenario losses, and also, because for this product the losses for each scenario are quite similar, changing the confidence level does not have a big impact.

However, for an equally simple OTC portfolio consisting of a couple of EUR swaps and a FRA, we have actually seen a near 40% increase in margin. Here, the margin was previously being driven by the Historic VaR, but now it is the Stress VaR that is predominant.

Interestingly, for an equivalent USD portfolio, the margin before and after the change is driven by the Stress VaR component, but here the increase in the confidence level, as well as a general increase in the scenario losses, has made a 20% difference to the number calculated.

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What makes your initial margin volatile?

If you run a ‘buy and hold’ strategy you don’t expect your initial margin requirement to change much. You would even hope that margin would progressively fall over time as the duration decreases. But, this isn’t always the case…sometimes you will see big step changes in margin that can be as much as 100% –  a lot of potential capital to find in a hurry.

Plus, you’ve also probably noticed that when there’s a big move in the market, there sometimes is a big move in your margin. But there sometimes isn’t. So, why do some moves seem to impact margin whereas others don’t?

The answer to why large market moves impact some products more than others lies in how the CCPs determine the scenarios they use in their margin calculation. It doesn’t matter whether it’s ETD or OTC, or whether the margin algorithm is SPAN or VaR, all in some way consider historic price moves:

  • The commonly used VaR algorithms are based on historical simulation.
  • CCPs will analyse historic price moves to set the SPAN scanning range the main input into the calculation.

However, the way that these historic price moves are applied to the current market depends on the product. Basically there are two scenario types:

  • Absolute
  • Relative

With absolute scenarios the actual price move is considered. This will be the type of shift used for interest rate products. This makes sense because interest rates tend to move by the same amount whatever the current level of rates. This type of scenario leads to margins that are only impacted by market volatility and not by the current market level.

Relative scenarios look at the percentage change in prices and then apply this as a shift to the current price. This type of scenario will be applied to equity products, where larger price changes tend to be seen when the underlying price is higher. This type of scenario means that margins will change as the market goes up and down.

If margin rates and scenarios are set based on absolute changes in prices then the margin calculated for a given position will be relatively stable. But, if relative scenarios are used then the underlying price can have a big impact on the margin requirement.

Consider the SPAN margin calculated on an index future based on a scanning range set at 5%. This means the CCP is expecting the level of the index to move by a maximum of 5% over the holding period (usually 2 days) and the margin calculated should cover this move.

If the current index level is 7,000 then the margin rate will be 350. But if the index goes up to 8,000 then the margin rate will go up to 400. However, the good news is that if there is a market crash, with the index going down to 6,000, then the margin rate would similarly go down to 300.

Sometimes you will see big step changes in margin that can be as much as 100%, that’s a lot of potential capital to find in a hurry! Click To Tweet

The same effect can also be seen where VaR is the margin algorithm, where for example a 5% change in price would result in a similar 5% change in margin.

Knowing whether relative or absolute scenarios are being used for a particular product can help you understand how your margin might be impacted by changes in price levels. For example, relative shifts will often be used for equity based products, where the amount that a price is likely to move is relative to price level. But, for interest rate products, absolute shifts will be used as price moves will be directly related to basis point shifts in the underlying curve.

Other factors to consider…

Contract Expiry

Meanwhile, the parameters or scenarios used for calculating the margin are configured differently for each upcoming expiry month for both VaR and SPAN methodologies.

When futures contracts expire, the ‘front’ (or first expiry) month will switch to the next available expiry. The same will be true for all other expiries in the series. The result: the margin calculated will change for every delivery month even without any change in position.

Usually this change is only small, but for some more volatile contracts the difference can mean over 100% change in the margin. But, this isn’t all bad news…depending on the type of contract the change can as easily be a reduction as an increase.  

For example, on a short term interest rate futures contract, the implied price moves in the margin scenarios used for the front end of the curve tend to be smaller than those used for later expiries  maybe 5bp at the front end compared with 10bp at the 5 year point. This would lead to a 50% reduction in margin over time, without any change in position.

However, for Brent Crude the margin may vary between 40 cents per barrel for near expiries compared with 30 cents per barrel for far dated expiries. So, in this case, the margin would increase by 25% over time.

Changes in liquidation charge, as described in my piece Why margin can double if you roll ETD contracts too early, can also impact the margin without a position changing. In general, there is more liquidity in close expiries, meaning that margin would tend to drop as contracts near expiry.

Market Impacting Events

The most obvious example of a market impacting event is an election, and in particular one where the result is in doubt. Others may have more impact on particular markets, for instance OPEC meetings and oil prices. Another example is the monthly release of nonfarm payrolls figures.

For any of these events, CCPs will need to make sure they are holding sufficient margin to cover any price moves. They will make predictions of the likely impact of the event and adjust their margins accordingly. These pre-event changes in margin rates can easily increase up to 79% according to our analysis.

Generally these jumps are only temporary. Once the dust has settled the margin will drop back to pre-event levels. Although, if the outcome is not expected the jumps can last up to 6 months.

Bank Holidays

A number of CCPs calculate specific bank holiday margin. This is needed where a CCP clears a contract that trades on a market that is open while they are closed for a local holiday. They will require additional margin to cover potential price moves while they are closed.

This additional charge is usually in the form of a multiplier which increases the theoretical holding period of the margin. For example if the margin is intended to cover 2 day price moves, then the Bank Holiday will mean it needs to cover 3 days instead. In this case the margin will multiplied by a factor of SQRT(3/2) or about a 22% increase. Following the bank holiday the margin will drop back to the usual level, but this temporary additional requirement needs to be funded.

To sum up…

This article highlights the variety of reasons why margin can be volatile. And this volatility often means that you need to provide additional funding to cover your margin just-in-case of these scenarios. However, if you understand the drivers of margin change you can minimise any over funding by knowing when the additional cover is needed– and that can lead to big savings in margin costs.

Blog, Insights

Gain visibility to verify your Initial Margin requirement

Within UK retail banks, the treasury function has been challenged by mandatory central clearing obligations for OTC derivatives.

In the context of corporate governance and fiduciary responsibility, treasury managers feel obligated to have robust control around independent verification of the Initial Margin (IM) call on their cleared OTC derivatives portfolio, as they bear potential credit risk to their clearing brokers.

We have been working with a UK retail bank to provide this increasingly critical function. Its team is now in production using OpenGamma’s margin calculation capabilities within our partner CloudMargin’s collateral management workflow solution. Best of all, the client was able to access this new functionality through a simple configuration option in CloudMargin, rather than the often onerous task of new software deployment or updates.

With full visibility into the Initial Margin requirements on their OTC derivatives portfolio, the bank is now able to validate every Clearing Broker IM call against the actual Clearing House IM requirement – ensuring that costs resulting from clearing mandates are precisely understood.

Using the cloud-based solution offered by OpenGamma and CloudMargin, we have helped this bank increase control and transparency, meaning they never have to blindly agree on a margin call again.

To find out more about how we can help you analyse and assess your margin requirements, please contact us for a demo.

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