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Adapting in real-time: the need for dynamic control frameworks
For more than a decade after the 2008 financial crisis, collateral optimisation in derivatives markets primarily focused on funding efficiency. The mandate was simple: minimise the cost of collateral by allocating the cheapest-to-deliver assets across margin obligations.
However, a series of acute market stress events, including the 2022 UK gilt crisis and the 2023 failure of Credit Suisse, exposed the fragility of static, cost-centric optimisation models, underscoring the need for dynamic control frameworks.
Today, the paradigm has shifted and collateral optimisation has evolved. Real-time control of collateral allocations rules, on top of a centralised and real-time inventory, are now critical for the market to simultaneously drive collateral resilience and funding savings.
Market shocks driving the change
The UK gilt crisis (2022)
The sudden spike in gilt yields during the UK liability-driven investment (LDI) crisis demonstrated how quickly collateral needs can escalate. Leveraged pension strategies faced aggressive margin calls as gilt prices collapsed, forcing urgent asset liquidations and emergency liquidity injections from the Bank of England. Trading desks and collateral teams found themselves competing for the same assets, just as prices were plunging.
The episode highlighted two painful realities:
- At the height of the volatility, gilts identified by optimisation systems as cheapest-to-deliver were systematically being pledged by collateral systems at the same time trading desks were fire selling the assets in market. This dislocation of business priority drove significant lags in desks being able to sell assets in market, and contributed directly to increased losses.
- For institutions lacking real-time visibility into inventory and holdings — and without automated substitution workflows that reflect real-time settlement status — risks and losses quickly escalated. Pledged gilts remained locked at counterparties, CCPs and exchanges for prolonged periods as teams struggled to track asset locations, recall positions and confirm whether in-transit collateral had actually settled.
With global national debt at record levels, and governments globally walking a tightrope of monetary policy and bond market confidence, the threat of another sovereign-driven liquidity event remains extremely high.
The Credit Suisse collapse (2023)
Credit Suisse's rapid deterioration and stressed acquisition by UBS underscored once again counterparty risk sensitivity in derivatives markets. As trust quickly evaporated, trading counterparties scrambled to understand their true exposure to Credit Suisse and to manage their risk effectively.
The crisis identified a number of modelling and process shortcomings:
- Cheapest-to-deliver approaches, designed to minimise funding costs at the moment of allocation, often failed to account for liquidity fragility, asset encumbrance risk and rapidly changing credit conditions. In some instances, static allocation approaches were actually increasing counterparty credit risk precisely when collateral processes were intended to reduce risk.
- Teams with static optimisation models had to override their processes and revert to manual asset selection to account for the dynamic risk environment. Yet this manual triage came at a cost, with slower agreement of margin calls, delayed settlements and elevated credit risk during a highly sensitive period.
Both crises illustrated a single unavoidable truth: collateral optimisation must be as dynamic and fast-moving as the markets it supports.
Control is the new alpha
Collateral optimisation has entered a new era. The priority has shifted from a siloed focus of reducing funding costs to a consolidated, cross-functional approach across collateral, liquidity and trading teams, driven by:
- Increased market volatility
- Strained liquidity
- Higher volatility in rates and credit spreads
- More complex regulatory and margining frameworks.
Firms need to adapt and be equipped to:
- Navigate stress and minimise trading losses
- Optimise liquidity and funding simultaneously
- Build resilience into operating models and reduce risk.
In this environment, optimisation is no longer simply about choosing the cheapest asset. It’s about preserving liquidity, protecting trading performance and reducing risk through real-time, accurate, automated control. In markets where liquidity shocks can cascade in hours, control is no longer optional, it's the core of a modern and well managed derivatives trading franchise.
