Key takeaways
- H2O learns where a rules-based bond pricer goes wrong and corrects it, so the desk quotes clients with more confidence.
- Matrix pricing from a rules engine fills the gap for bonds that have not traded in days, and it drifts on days when the market moves fast.
- A wide or stale bond quote costs the desk the trade and the client's next call.
AI bond pricing uses machine learning to predict fair reference prices and spreads for bonds that trade rarely or in volatile markets. H2O learns where your rules-based pricer goes wrong and corrects it, so your desk quotes clients with more confidence.
Your desk quotes two-way prices on thousands of bonds, and many of them have not traded in days. A rules engine fills the gap with matrix pricing, and it drifts on days when the market moves fast. A wide or stale quote costs you the trade and the client's next call.