Case study: Multi-billion dollar hedge fund discovers $55m of margin savings by using OpenGamma

A global fixed income hedge fund with multi-billion dollar of AUM trading relative value strategies, reduced its OTC cleared margin by 22% through better allocation of trades across clearing brokers, releasing capital to scale up profitable strategies. The saving was found within 2 weeks of using the OpenGamma platform, following a handful of straightforward position transfers.

About the client

The hedge fund runs common relative value strategies such as bond and swap spread basis across major US and European markets, with significant cleared OTC positions at LCH, posting a total of USD 250 million initial margin. The firm has 3 OTC clearing brokers, ensuring it diversifies its credit exposure and provide back up clearing lines in a default scenario.

Previously, the hedge fund followed a common and intuitive approach of allocating trades between brokers, where it ensured trades of the same currency were cleared with the same broker in order to maximise the position offset.

In pursuit of better returns, the hedge fund reached out to OpenGamma to understand how to optimise trading allocations to reduce margin.

The challenge

Over the last few years, it has become harder for hedge funds to drive material returns from common relative value strategies. This is because margin levels have increased as post-crisis derivatives regulations have taken effect, these include requiring additional margin to be posted on bilateral trades and increasing the margin requirements for both ETD and OTC derivatives.

The margin posted on derivatives positions regularly becomes a limiting factor on the size of the positions firms can trade a direct constraint on returns. This is to guarantee liquidity for investors by maintaining unencumbered cash (UEC) levels of 65-70%.

The latest generations of margin models are complex, requiring detailed knowledge and therefore making it hard to be capital efficient. Simple approaches to capital efficiency no longer work well, this means it is very easy to build up margin efficiencies that drag down returns.

The solution

All OTC clearing houses use VaR-based methodologies for calculating initial margin requirements. Organisations use a combination of a historical value-at-risk and a liquidity add-on component based on concentration of positions.

Maximising position offset

VaR is calculated by running a set of historical scenarios and calculating the loss with a certain level of confidence. Margin at the clearing house is calculated on the positions at each clearing broker, without any position offset across brokers. If a fund had 2 trades in the book one PAY and one REC on same tenor but against different clearing brokers it would pay margin to both. On the other hand, if it had these trades with the same broker, it would get the benefit of risk offset in the VaR calculation and not pay any margin. Finding the best offsets for a portfolio helps reduce the VaR component of the margin. To get maximum position offset, the optimal solution here is to have all the risk with the same broker.

However, efficiently managing position offsets is only half the problem solved…

Minimising liquidity add-on

Liquidity add-ons are applied by most clearing houses and take into account the build-up of risk concentration of relative the available market liquidity. The liquidity add-on requirement increases  faster as the risk rises. Add-ons depend on the positions the fund has with each clearing broker (as opposed to the net position across brokers) and therefore distributing positions across brokers provides opportunities to minimise the liquidity add-on.

For large positions, the liquidity add-on can double the margin requirement.  This ‘super-linear’ property of the liquidity add-on means that when risk in concentrated at the same broker (to maximise position offset), it also creates the maximum liquidity add-on.

As a result, minimising margin through rebalancing positions between brokers means finding the right balance between directly opposing drivers:

Opengamma case study

However, to be able to calculate minimum margin the hedge fund would have to run thousands of stimulations and have the correct models in place every day. As a result, the firm employed OpenGamma’s Margin analytics to identify the optimal risk and position transfers to minimise margin, factoring in the methodology set out above. The platform allowed the hedge fund manager to account for real-world constraints, such as maximum number of moves to minimise operational overhead or avoiding breaking up strategies.

Furthermore, daily recommendations were provided to each user to constantly minimise their margin and increase efficiency and ultimately cut costs. On top of this, the firm used OpenGamma’s ad-hoc simulations to quantify the impact of moving specific positions across brokers. This allowed the hedge fund to optimally rebalance the portfolio, as well as move far less trades therefore also reducing operational overhead.

The results

Original: $250 million margin



Theoretical minimum margin: $189 million (-24.4%)

An equal split of all positions across the 3 brokers would create the theoretical minimum margin, however this would have required around 4000 positions to be moved making this operationally impractical.

Practical minimum margin: $195 million (-22%)

Rebalancing the portfolio using OpenGamma allowed the hedge fund to get close to the optimal: using a combination of 5 trade moves, the total margin was reduced to USD 195mm, a reduction of 22%:



It is incredibly easy to build up major margin inefficiency within a cleared derivatives portfolio due to the complexity of the latest CCP margin models. Tracking the margin efficiency on a day-to-day basis followed by periodic rebalancing of positions between clearing brokers can remove the vast majority of margin inefficiency that otherwise slightly builds within a portfolio.


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