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Sterling Trading Tech release custom margin rates and enhanced VAR for real-time risk engine

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Sterling Trading Tech (STT), a leader in compliance, risk and infrastructure solutions for equity, options, and futures trading, has unveiled customisable margins and enhanced VAR calculations, a major set of enhancements for its risk analytics Risk as a Service (RaaS) solution, the Sterling Risk Engine (SRE).The Sterling Risk Engine provides advanced analytics in a RaaS solution utilising sophisticated quantitative and big data techniques to manage risk in real-time for hundreds of portfolios allowing brokerages and clearing firms to easily monitor market and credit risk. The SRE continuously calculates the OCC Portfolio margin and Risk based haircuts, for each account as well as having the capability to set specific firm specific house rules.

A new Horizon risk functionality was added, giving clients the ability to view risk and margin requirements on a look forward basis, including post-expiration of option positions. Additionally, Sterling Risk Monitor, the web-based GUI for SRE, added a new Portfolio Viewer, expanded Greeks, and improved navigations. Using STT’s Risk System, firms have the ability to instantly update custom margin rates, allowing them to react quickly to market moves.
“The SRE and the Risk Monitor continue to rapidly evolve based on market feedback, client requests and proprietary STT research and development,” states Farid Naib, Sterling Trading Tech’s CEO. “We have expanded our VAR calculations to include more sophisticated handling of new stock issues as well.”
“The system’s design and architecture allow us to add new functionality quickly,” states Ravi Jain, Sterling Trading Tech’s Director of Risk and Derivatives. “Upcoming releases will include additional clearing firm house policy support, expanded concentration risk features and various front-end improvement for easier viewing of portfolios.”
The Sterling Risk Engine was launched in the spring of 2017 as one of the first commercial risk tools to utilise sophisticated quantitative and big data techniques to manage risk in real-time with a cloud delivery model, allowing users to easily monitor market and credit risk with minimal hardware and software requirements.

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