This article was initially featured in SFT’s Collateral Annual 2024.

Including collateral optimization tools in the buyside’s operational framework is no longer an option—it is a necessity, says Thomas Griffiths, Head of Product at Cassini Systems. The ability to navigate the impact of market volatility and meet regulatory hurdles on margin requirements while generating maximum returns from asset inventory all hinge on the effectiveness of collateral management strategies. Intelligent collateral analytics empower the buyside to achieve all these objectives while providing a solid foundation for a truly collateral-resilient framework.

In recent years, regulatory requirements, extreme market shocks, and rate rises have created a perfect storm for funds, driving up margin calls and significantly affecting fund’s liquidity and collateral positions. In the face of this, a robust liquidity and collateral operational framework has emerged as a frontline necessity to enhance returns and mitigate liquidity risk.

For buyside players navigating this complex terrain, collateral optimization tools have emerged as an indispensable part of this framework, providing a competitive edge and safeguarding against the challenges posed by market fluctuations and increasing funding costs.

The regulatory shifts reshaping the financial landscape, such as the Uncleared Margin Rules (UMR) and the European Market Infrastructure Regulation (EMIR), have forced market participants to collateralize derivatives trades more rigorously. This has meant the buyside is now required to make larger collateral pools available for meeting margin requirements and to consider more accurately the value of the assets in their funds for more intelligent collateral allocation.

Stage one of navigating this regulatory change has been ensuring compliance- now primarily in place across the buyside. The focus is now shifting beyond calculating and analyzing the margin and collateral requirements these new regulations have imposed on firms and onto ways to reduce the impact of these regulations by operating more optimally across their collateral processes.

Market volatility is an ever-present reality, and times of stress directly impact margin requirements. Sudden market fluctuations in asset values can trigger margin calls whilst often at the same time depreciating the assets that could have been used as collateral to meet these increased requirements. This impacts collateral buffers often held by buyside firms against extreme market events and can cause them to need to top up long box allocations at triparty agents or raise expensive liquidity during stressed markets to meet unexpected margin calls.

Sophisticated forecasting and stress testing analytics allow the buyside to simulate margin requirements and collateral values under different market scenarios.

In an increasingly competitive market, buyside firms are pressured to simultaneously enhance returns and operational efficiency to increase alpha and reduce costs within individual funds and across their firm.

Automating collateral selection tools, rather than relying purely on an individual’s expertise within collateral and treasury functions, provides an initial step to address these needs. However, using more advanced optimization algorithms enables firms to free up previously encumbered collateral assets to maximize investment opportunities and generate additional returns to fund performance. Providing these analytics as part of the daily collateral workflow further ensures these optimal results are achievable consistently without specialist intervention.

At its core, collateral optimization is the art of efficiently deploying collateral resources. It has long been believed that by defining a priority order of assets based on eligibility criteria in legal agreements and haircut rules, a collateral manager can manage this complexity. These so-called “waterfall” models of managing collateral assets have been commonplace and were considered fit for purpose when funding costs were low, collateral was cheap, and margin calls had a significantly lower impact on a fund’s bottom line.

However, in an era where regulatory changes are redefining margin requirements and market stresses can trigger unexpected margin calls, the buyside faces a twofold challenge: meeting increased and often unexpected margin calls while minimizing the impact of higher collateral costs on returns.

Collateral optimization analytics designed for the buyside provide a lifeline in this intricate margin and collateral complexity maze. By optimizing collateral allocation across portfolios, these tools ensure that the optimal assets are allocated to each margin requirement, thereby minimizing the overall cost of collateral, which otherwise may cause a drag on portfolio performance.

Several factors must be considered to optimize collateral allocation truly, and the significance of these can vary from firm to firm but will generally include the following:

• Understanding the opportunity cost of different assets
• Modelling counterparty eligibility and haircut schedules
• Restricting assets from collateral posting for dividends and corporate actions
• Ensuring diversity of assets across counterparties (i.e., impose concentration limits)
• Allowing for collateral buffers to reduce the number of movements
• Optimizing substitution to free up assets when they become non-GC for lending

Buyside firms starting on their journey to optimization are realizing that having transparency into the above factors and an automated way to apply them in the daily collateral process can result in the following:

• Reduced drag on portfolio performance from collateral costs
• A robust collateral liquidity framework for both T+1 and future requirements
• Avoidance of forced asset sales or emergency funding to meet short-term margin demands.
• Transparency and control of the assets available within their funds and direction on the best way to deploy them

The consequence of this realization amongst leading buyside firms is that once a peripheral concern, collateral optimization has surged to the forefront, transforming into a strategic necessity for their business.

The journey to embracing collateral optimization tools and analytics can start as a simple replacement of existing waterfall methodologies, but to truly benefit from the efficiencies these tools provide, firms should evolve their processes into a full collateral optimization suite.

An advanced collateral optimization framework will evaluate market opportunities for each underlying asset and leverage intelligent optimization algorithms to identify the cheapest assets to post either into a long box or against a margin requirement.

These analytics should be available at all workflow stages to help check for collateral sufficiency as part of a pre-trade process, process individual margin calls, or regularly rebalance and substitute already pledged collateral.

Whilst some buyside firms are already well advanced in implementing this robust model and benefit from the increased revenue and reduced costs, many others are just starting. What is clear, however, is that as the derivatives margining ecosystem continues to evolve, buyside players who embrace collateral optimization tools position themselves to thrive, whilst those that do not are likely to be left behind.