First launched in 2001, there are now around 1,000 Synthetic Exchange-Traded Funds (ETFs) adding up to approximately 11% of the total ETF market. Financial institutions often hedge their Synthetic ETF positions with ETF Issuers using Total Return Swaps (TRS) trades, an approach that brings with it far greater complexity in managing collateral compared to traditional interdealer (D2D) workflows. 

Complexity stems from the requirements that ETF Issuers put on collateral management. These stem from a broad range of regulation-related factors including the structure of the fund, custom index requirements, concentration limits, investment guidelines, and, increasingly, ESG constraints. Each one of these factors comes with restrictions that can make efficiency and effective collateral optimization a challenge.

Here’s an example. The Undertakings for Collective Investment in Transferable Securities (UCITS) Directive mandates that at least 90% of assets in UCITS funds must be liquid. UCITS must also comply with the “5/10/40 Rule” so that no one asset represents more than 10% of the fund’s total assets and holdings of more than 5% cannot together exceed 40% of the fund’s assets. These stipulations require banks to come up with a complicated algorithm to take account of all these restrictions every time they enter into a TRS on equities deal with a UCITS fund. And that’s just one of the many requirements that are tied into the overall ETF wrapper.  

To date, banks have had no choice but to manage this complexity through manual workarounds. By building their own algorithms and linking them to manually created spreadsheets they can cobble together a policy control layer to ensure that their collateral is in line with the various requirements at hand. 

However, this is a suboptimal approach. It’s time-consuming and costly and, more importantly, error-prone. If any requirements are missed that can leave end clients exposed to regulatory compliance breaches, an eventuality that would effectively end the bank’s relationship with that customer. Ensuring efficiency and effective collateral optimization for TRS-hedged Synthetic ETFs is therefore a vital consideration.

Fortunately, cloud-based automation technologies promise to significantly reduce the cost and complexity of collateral optimization for Synthetic ETFs. Wematch’s own approach to Synthetic ETFs is a case in point. Our simple, web-based service automates the process of applying rules to TRS hedges for Synthetic ETFs. Our platform enables clients to consolidate their collateral in a single pool, regardless of where a trade sits – with the bank, its client, or the fund. 

From there, clients can create fully automated collateral schedules that reflect the unique rules of the fund(s) in question, and all with the same intuitive interface controls that Wematch is famous for. We’ve estimated that our automated collateral optimization tools reduce the time taken for a dealer to complete the associated workflow from around 4 hours to just 15 minutes. What’s more, automated workflow minimizes the likelihood of errors creeping into the process, reducing operational risk for banks and compliance risk for their clients. 

As well as making it much easier to meet the requirements of issuers, our platform brings further benefits to banks through dynamic collateral management based on real-time information. With Wematch, traders are also able to communicate with counterparties in bulk, enabling them to process proposals much faster. Together, these benefits add up to much more efficient collateral optimization, which in turn leads to balance sheet and capital cost improvements.

As in other areas of the TRS market, digitized and automated workflows and processes are taming complexity, reducing risk, and driving efficiencies. By working with a one-stop-shop solution provider like Wematch, financial institutions can unlock these benefits across their dealing workflows, giving them a significant competitive advantage. 

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