Free Phase 1 with registration
Market Risk Modelling in 26 Weeks
Learn market risk modelling as a practitioner workflow: from trade data and market curves to valuation, P&L, risk measures, validation, stress testing and capital.
What makes it practical
- One canonical portfolio used across the course
- Lecture notes connected to Python labs
- Products before risk metrics
- Valuation, market data and P&L controls
- Emphasis on assumptions, signs, units and evidence
Who it is for
Quant analysts, risk analysts, model validators, trading-risk professionals and students preparing for quant finance roles.
How it is taught
Each phase combines lecture notes, runnable Python notebooks, guided labs and portfolio-based checks.
What you build
A controlled workflow around trade representation, market data, valuation, P&L, risk measures and validation evidence.
Why this course is different
Not just models in isolation: the full market-risk chain
Many finance courses teach pricing models, statistics, regulation or Python as separate topics. This course connects them into the workflow a market risk quant or model validator needs to understand: products, market inputs, valuation, P&L, risk factors, risk measures, controls and reporting.
| Common learning path | This course |
|---|---|
| Topics are often taught as separate models, formulas or exam chapters. | Topics are connected through one practitioner workflow. |
| Risk metrics may appear before students can explain the underlying products. | Products, data, valuation and P&L come before VaR, ES and capital. |
| Examples are often self-contained and do not build on each other. | A canonical multi-asset portfolio is reused across the course. |
| Python examples may stop at producing a number. | Labs require checks on signs, units, assumptions, market-factor dependencies and reconciliation. |
Seven-phase roadmap
From foundations to advanced modelling
Phase 1 outcome
Value and explain a small multi-asset portfolio
By the end of Phase 1, students can explain the products and market inputs in a canonical market-risk portfolio, value each trade at two dates, convert values to the reporting currency, calculate full-revaluation P&L, and reconcile trade-level and portfolio-level results. The goal is not only to obtain a number, but to explain where the number comes from and what assumptions sit behind it.
Start with Phase 1
Phase 1 is free with registration. Open the learning hub after signing in or creating an account.
Start Phase 1Already registered?
Go directly to the course hub and continue with the lecture notes, notebooks, and student package.
Go to course hubFAQ
Is Phase 1 free?
Yes. Phase 1 is free with registration.
Do I need Python?
Yes. The labs use Python notebooks, with the required setup explained in the student package.
Are notebooks included?
Yes. Phase 1 includes a student ZIP with notebooks, labs, data and setup files.
Are later phases available?
Later phases will be added as the course expands.
What makes it different from other courses?
The course connects products, market data, valuation, P&L, risk measures and controls into one practitioner workflow.