Stochastic modelling specialists for your dedicated team.

Where stochastic modelling fits in a long-term product team.

Choosing the process is the substantive modelling decision. Geometric Brownian motion assumes continuous paths and lognormal returns; real markets jump and have heavier tails than that implies. The choice encodes a belief about how the world behaves, and it is where model risk originates.

Calibration to historical data carries the assumption that the future resembles the past. That assumption is reasonable in stable conditions and fails precisely during the events the model exists to quantify, which is why stress testing outside the calibration range matters more than fit quality inside it.

What your dedicated team does with stochastic modelling.

Models used for decisions of consequence need governance: documented assumptions, independent validation, and periodic review. In regulated sectors that is a requirement; everywhere else it is simply prudent.

Building that governance alongside the model is part of how quantitative work is scoped, as set out in how the monthly fee is built.

Common stochastic modelling use cases.

• Processes chosen for the actual behaviour Jumps and heavy tails represented where the data shows them.
• Stress testing beyond the calibration range Behaviour examined under conditions the history does not contain.
• Assumptions documented for validation A model an independent reviewer can assess as well as run.

Adding stochastic modelling skills to your team.

Quantitative modelling capacity is assigned in a dedicated data engineering team, with model governance treated as part of the deliverable.

You can add stochastic modelling capacity as one dedicated specialist or as part of a wider remote team, managed by a service delivery manager from our offices in Thailand. Companies hire dedicated developers this way under one agreement and one monthly fee.