The Master’s in Quantitative Finance at Université Paris-Saclay trains specialists in financial modeling, risk management, and derivatives valuation. Combining theory and practice, the program emphasizes advanced numerical methods, as well as machine learning and deep learning applied to finance. Students acquire the skills to become quants, prepared for careers in financial markets, asset management, or academic research.
The Master’s in Quantitative Finance is a one-year program combining advanced theoretical courses, applied teaching, and practical projects. The first semester focuses on core topics in modeling, numerical methods, and machine learning. The second semester is dedicated to an internship in industry or a research lab, enabling students to apply their skills and prepare for careers in quantitative finance or for pursuing a PhD.
Information
Objectives
M2QF brings to high level scientific students an invaluable expertise in the field of quantitative finance, considered from the points of view of mathematics (probability and statistics, computational methods) and data science. Job opportunities after the master program: quantitative analyst, risk manager, IT quant, insurance, data scientist for finance, PhD thesis in quantitative finance,...
Capacity
Available Places
Target Audience and Entry Requirements
Good M1-level (or equivalent) competence in probability and statistics, market finance, programming (in C, Python)
Application Period(s)
From 30/01/2026 to 30/06/2026
Supporting documents
Compulsory supporting documents
Motivation letter.
All transcripts of the years / semesters validated since the high school diploma at the date of application.
Curriculum Vitae.
Additional supporting documents
Certificate of English level.
VAP file (obligatory for all persons requesting a valuation of the assets to enter the diploma).
Supporting documents :
- Residence permit stating the country of residence of the first country
- Or receipt of request stating the country of first asylum
- Or document from the UNHCR granting refugee status
- Or receipt of refugee status request delivered in France
- Or residence permit stating the refugee status delivered in France
- Or document stating subsidiary protection in France or abroad
- Or document stating temporary protection in France or abroad.
| Subjects | ECTS | Semester | Lecture | directed study | practical class | Lecture/directed study | Lecture/practical class | directed study/practical class | distance-learning course | Project | Supervised studies |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Derivative Instruments | 4 | Semester 2 | 47 | ||||||||
| Particle system, Mean field games and applications to machine learning and finance | 4 | Semester 2 | 21 | 21 | |||||||
| Stochastic control and corporate finance and insurance modeling | 4 | Semester 2 | 42 | ||||||||
| Cutting edge finance | 4 | Semestre 2 | 64 | ||||||||
| Sustainable finance and asset management | 2 | Semester 2 | 24 | ||||||||
| Machine learning techniques in finance | 2 | Semester 2 | 21 | ||||||||
| High frequency data and order books | 2 | Semester 2 | 24 | ||||||||
| XVAs and regulations | 2 | Semester 2 | 22.5 | ||||||||
| Approximation of processes | 2 | Semester 2 | 21 | ||||||||
| Subjects | ECTS | Semester | Lecture | directed study | practical class | Lecture/directed study | Lecture/practical class | directed study/practical class | distance-learning course | Project | Supervised studies |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Professional Internship | 15 | Semester 2 | |||||||||
| TOEIC preparation | 2 | Semester 2 | 25 | ||||||||
| Subjects | ECTS | Semester | Lecture | directed study | practical class | Lecture/directed study | Lecture/practical class | directed study/practical class | distance-learning course | Project | Supervised studies |
|---|---|---|---|---|---|---|---|---|---|---|---|
| IT Project | 3 | Semester 1 | 2 | 10 | |||||||
| Numerical finance and models calibration | 6 | Semester 1 | 42 | ||||||||
| Subjects | ECTS | Semester | Lecture | directed study | practical class | Lecture/directed study | Lecture/practical class | directed study/practical class | distance-learning course | Project | Supervised studies |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Deep learning | 3 | Semester 1 | 21 | ||||||||
| Programmation informatique | 6 | Semestre 1 | 18 | 24 | |||||||
| Interest rate modeling | 3 | Semester 1 | 21 | ||||||||
| Financial markets and actuarial finance | 3 | Semester 1 | 14 | 6.5 | |||||||
| Machine Learning | 6 | Semester 1 | 21 | 21 | |||||||
| Stochastic calculus | 6 | Semester 1 | 21 | 21 | |||||||
| Financial econometrics | 3 | Semester 1 | 15 | 10 | |||||||
| Finance of insurance | 3 | Semester 1 | 36 | ||||||||
| Risk management | 6 | Semester 1 | 36 | 15 | |||||||
Teaching Location(s)
Contact
-
Ahmed Kebaier
PR