Given continuing regulatory change around risk and the cost of trading, innovative solutions that can combine the ability to crunch intensive calculations while still maintaining confidence through a list of definitive inputs are like gold dust—hard to find, but worth the effort. With its SwapClear Margin Approximation Risk Tool (Smart), LCH.Clearnet has created just that, following on from its success in this year’s inaugural Sell-Side Technology Awards.
Smart allows users to predict how their initial margin (IM) requirements will be affected by simulated trades, on an individual or portfolio-wide basis. This “what-if” functionality allows for a strong, confident calculation, based not only on portfolio sensitivities, but also user-defined sensitivity profiles, if desired. Underpinned by the Portfolio Approach to Interest Rate Scenarios (Pairs) methodology, it draws on a wealth of historical data to produce potential IM requirement numbers, backed up with input from regulatory requirements and the firm’s own risk policies.
Risk and portfolio analytics are not new technology disciplines, but applying them to a current and pressing concern such as margin calculations has proven to be a prescient and intelligent move by LCH.Clearnet. In expanding its appeal to buy-side clients, the firm has released Smart on three different avenues to date, including desktop, code-library integration with a client’s own applications, and making it available directly through the Bloomberg Professional Service. In the process, the company has added hundreds of new users in the past year alone.
The ability to perform portfolio examinations of margin impact, while factoring in specific volatility profiles, is of major benefit to the buy side, given the continuing debate over IM requirements, the quality of collateral, and the possibility of mutualized risk through new regulatory regimes. More specifically, given recent talk of pro-cyclical liquidity risk arising from margin requirements, the simulation capabilities allow market participants to address these challenges on a pre-, post-, or at-trade basis, utilizing the data to provide results that are as accurate as is possible, given the idiosyncratic nature of projections. All of these factors contributed to LCH.Clearnet’s victory in what is a perennially sought-after category.
"LCH.Clearnet’s Smart allows users to predict how their initial margin requirements will be affected by simulated trades, on an individual or portfolio-wide basis."
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