If maintaining operations through a global pandemic was not enough of a challenge, many buy-side institutions are grappling with fast-approaching compliance dates for an onslaught of new regulations issued by global regulators. Alongside the related suite of new acronyms entering our collective vocabulary (UMR, SFTR, CSDR, etc.), several regulations require institutions to materially change and enhance their operational processes and technology systems. It is a tall order in the best of times.

The financial resource consequences of some of these regulations cannot be ignored. The Uncleared Margin Rules (UMR), in particular, require institutions that have never been asked for a penny in initial margin (IM) to be operationally ready to pledge IM. In some cases, they must pledge a significant amount to their counterparties to cover risk of default. Understandably, many overwhelmed operations and project teams are focused primarily on compliance rather than strategic initiatives.

However, ticking off the regulatory boxes and moving on in this instance may prove to be unexpectedly costly. For many, now may be the right time to build analytics and optimization capabilities through the trade lifecycle and to more holistically manage funding and opportunity costs to generate additional benefits.

Our clients are in various stages of their journey toward optimization. Some are yet to begin. In this article, we explore the components of a front-to-back optimization framework, benefits of emerging alternative avenues to liquidity markets and some of the real-life successes clients have achieved by incorporating analytics into their workflows.

Optimization Goes Through the Lifecycle of a Trade

Collateral optimization has traditionally been considered an algorithm that runs in the back office with little need for involvement by, or transparency to, the front office. While fears of collateral shortages have not materialized to date, demand has climbed for high-quality liquid assets (HQLA) that can be used to satisfy margin requirements at central counter parties (CCPs) and in bilateral trades. Higher demand has, in turn, raised the funding and opportunity costs of delivering these assets as collateral. With the ensuing direct impact to the bottom line, front offices are taking note and, increasingly assuming control.

Pre-Trade – Where Optimization Begins

Unlike variation margin (VM), which covers the mark-to-market (MTM) of trades, IM is a modelled figure meant to cover the risk of market movement between the time of a counterparty default and the associated portfolio close-out at a high confidence level. While VM is based on realized market moves and generally considered a cost of doing business by managers, IM is a risk and portfolio-based calculation that may be optimized. Specifically, where you place your trade can affect your margin requirement.

Analytical tools to model the margin and collateral impacts of trades before placement can help you minimize your margin requirement, whether by taking advantage of risk offsets in portfolios, avoiding costly counterparty concentration and liquidity add-ons.

Pre-trade analytics may be of particular importance to asset managers with funds in scope for UMR. Asset owners are allocated a threshold of US$50 million (or jurisdictional equivalent) per counterparty, such that they only have to deliver collateral if calculated margin exceeds that threshold. Many managers seek to stay below or close to their thresholds to minimize or entirely avoid required collateral moves. Asset owners with multiple external managers generally will allocate threshold across their managers, potentially leaving each individual manager with little room for error to remain under its allocation. Pre-trade tools offer transparency and confidence that inadvertent breaches will not arise.

Margin Forecasting – Look Ahead and Align Your Resources

Market volatility in the beginning of the global pandemic drove a five-fold increase in cleared variation margin volumes, underscoring the need for managers to proactively assess their margin requirements. The ability to forecast margin requirements at a high confidence level allows institutions to right-size cash and liquidity buffers and to establish contingent funding lines. In the absence of a clear view of potential margin requirements, managers may hold outsized liquidity buffers and miss out on investment opportunities. Transparency ensures cash and liquidity are allocated and deployed efficiently.

Pension funds have historically been able to meet margin requirements from the large pools of bonds that they hold. However, clearing regulations require some of their derivative transactions to be margined with cash, which in turn has required funds to establish new capabilities to deliver cash in the right currency to the right account on a T+0 basis. Margin forecasting provides a line of sight into upcoming requirements allowing the process to be managed with confidence.

Trade Novation – Balance Your Portfolio and Remain Optimal

In the cleared space, porting trades between clearing brokers and CCP switches can reduce margin requirements post-trade. While more operationally intensive, the same benefits can be achieved for bilateral trades.

Where the frequency of derivative trades is high and timing is important, pre-trade checks to identify optimal placement may not always be feasible. Novating trades allows for margin optimization on T+1. Even when trades are placed optimally using pre-trade analytics, post-trade re-balancing can produce significant margin savings as portfolios and risk factors change. What-if capabilities to model portfolios and determine the ideal number of clearing brokers with which to contract can also help maximize the optimization impact of novation.

Collateral Optimization – Efficiently Use Your Inventory

Incorporating collateral optimization with margin optimization (as discussed above) becomes even more important as the value of eligible collateral rises. While margin optimization focuses on minimizing the collateral that must be posted, collateral optimization focuses on meeting requirements in the most cost-effective manner.

Effective collateral optimization extends beyond an algorithm. It includes all actions an institution takes to more efficiently use its portfolio of assets and it requires a thorough understanding of costs and uses of collateral assets and requirements. While institutions may have differing goals, the preservation of HQLA can reduce funding costs and may provide opportunities to generate incremental revenue through securities lending.

Collateral Transformation – Put Your Assets to Work

Collateral transformation, which can include repo, reverse repo, securities lending and securities borrowing transactions, is integral to an optimization framework. A client short of eligible collateral may upgrade other assets in its inventory to eligible collateral, allowing the client to hold long those assets that are best aligned with its investment strategy and still meet its collateral requirements. Similarly, a client long in-demand collateral may lend such assets and generate incremental fee income.

Typically, transformation trades are undertaken with a dealer or a bank. The dealer or the bank must use its balance sheet, the cost of which is passed on via pricing. We all know that banking regulations have made balance sheets more expensive and — for all but a bank’s most profitable clients — harder to access. Alternative avenues to funding markets, such as clearing and peer to peer, offer buy-side institutions a diversified set of options. It is possible that they may support more beneficial pricing too. Just as margin optimization can reduce the amount of collateral required and collateral optimization can preserve high quality assets, alternative avenues to markets may yield cost savings and incremental revenue.

Creating a Competitive Advantage

Recent regulations and market conditions have created challenges for buy-side institutions, increasing costs and squeezing revenues, but opportunities are also aplenty. Innovative technology and market solutions available to the buy-side have never been more abundant and, as illustrated above, can deliver tangible benefits. Instilling a holistic optimization framework may sound daunting. It will be a journey for most, for sure, and the end result will not look the same for all.

However, analytics, technology and a broader toolkit of execution options can be combined to generate alpha, something that has become increasingly elusive in public markets and — we think — that is worth a journey.