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USE CASE

OTC Price Automation

 

Given that trading of OTC products (e.g. bonds, interest rate swap) are conducted bi-laterally (between two parties) rather than on a centralized exchange, receiving real-time pricing information poses a challenge. To a large extent for liquid instruments, the data is available and the pricing is fairly transparent and is largely influenced by the trader’s position in the underlying trading book. However, for illiquid instruments traders have to rely on relational prices and apply their experience to determine the quotable price which is executable and also provides a margin to cover the position risk. Having very few real-time prices available in the market for this illiquid OTC product, the trader will be forced to calculate a hypothetical price based off combining the prices of comparable products and some benchmark data points. These illiquid instruments can be a part of products like mortgage-backed securities, corporate bonds, government bonds, U.S. federal funds, emerging-market debt, bank loans, broken dated swaps and many other derivatives, private equity, and real estate, that tend to get traded in OTC market.

Using Cognitive Machine Learning, historical data for this illiquid OTC instrument, and real-time data available for comparable instruments, Synechron’s Cognitive Machine Learning solution for OTC Price Automation derives a theoretical OTC price. Over time the OTC Price Automation can yield a predictable real-time OTC price for traders to use in their models or use for auto-quoting. This serves as an important step towards comprehensive integrated valuation for illiquid OTC products, moving away from an environment where the price is estimated by the trader and toward a predictive model where the price is determined by various real-time & historical factors: benchmark swap curve, benchmark futures curve, benchmark economic indicators, comparable OTC products etc.

Key features and benefits include:

  • Enhanced valuation of illiquid OTC products resulting in improved trading revenues
  • Enhanced trade positioning, trade back loading and instrument pricing with more accurate real-time and estimated price validation
  • Bilateral margin calculations and validation to assist with new margin requirements and better manage collateral more predictably based on real-time price transparency for both variation margin and initial margin
  • Portfolio optimization including optimal positioning across CCPs, bilateral counterparties and what-if analysis as well as the ability to minimize losses and increase visibility of large no of derivatives
  • System integration with historical and near real-time attributes like news and events
  • Increased data integrity and translation for enhanced compliance reporting and adherence to regulatory requirements on data accuracy and reporting
  • Increase instrument coverage to be a preferred market maker

To learn more about our Artificial Intelligence solutions for Cognitive Machine Learning and the work we’re doing email us at finlabs@synechron.com