Fast risk is meaningless if the marketdata behind it is slow.
Speed only matters when price and risk share the same moment in time.
Pricing, market data and risk analytics are often treated as separate domains. Different teams. Different systems. Different optimisation goals.
In reality, they operate on the same time axis.
A sophisticated pricing model cannot compensate for curves that update sporadically. And a powerful risk engine cannot produce meaningful results if it is fed with misaligned or stale data. When inputs drift in time, outputs drift with them.
Real-time risk does not start in the risk engine. It starts with data discipline.
If price and risk are not evaluated against the same market state, risk measures inevitably become approximate, reflecting a delayed view of the market rather than the conditions that produced the trade. Sensitivities drift. Hedges misalign. Decisions are taken on numbers that no longer correspond to the same curve or surface that generated the price.
That is why robust architectures treat pricing and risk as a single decision pipeline. Curves, surfaces and market states must be synchronised. Historical states must be preserved. And price and risk must be evaluated within the same temporal window.
When data discipline is strong, risk becomes actionable rather than retrospective.
And speed becomes meaningful, not just fast.