Continuous-time
risk analytics that
adapt when markets break.
Stokhos Labs builds Neural Stochastic Differential Equation models that forecast the U.S. Treasury yield curve in real time, cutting forecast error by 64.3% during the COVID-19 crisis versus industry-standard models.
Neural SDEs: a model
built for crisis conditions.
Instead of daily or weekly updates, the model evolves through a differential equation that responds instantaneously to new information, with no lag during fast-moving crises.
The diffusion network learns state-dependent volatility directly from data. When the market enters a crisis regime, uncertainty estimates widen automatically, with no manual recalibration.
Trained on 2015–2019 FRED data, stress-tested on COVID-19 March 2020. ARX-GARCH MSE: 1.943. Neural SDE MSE: 0.692. A 64.3% reduction — with zero post-crisis retraining.
Everything you need
to understand the platform.
Why ARX-GARCH and discrete-time models fail precisely when risk management matters most.
Read more →Drift networks, diffusion networks, and Euler–Maruyama integration: how the Neural SDE works.
Read more →Yield curve forecast vs. actual, cumulative error, and regime classification: the live product interface.
View dashboard →Built by Cole Amaya and Filip Maleev, researchers who understand both the mathematics and the market.
Meet the team →