Computing Project
Team-based quantitative project in C++ with Excel/VBA interfacing.
Mathematics of Finance at Paris-Saclay
A demanding quantitative finance program at the crossroads of mathematics, computation, and financial applications.
The Master 2 Quantitative Finance trains specialists able to model, simulate, calibrate, and analyze complex financial problems using mathematics, statistics, programming, and machine learning.
Degree
Master 2
Duration
1 year
Languages
English / French
Location
Evry
Capacity
30 students
About the Program
A program built to connect probability, numerical methods, machine learning, and financial markets.
The first semester is devoted to the foundations of the quantitative finance toolkit: stochastic calculus, numerical methods, programming, financial econometrics, actuarial finance, risk management, and statistical learning. The second semester moves toward advanced derivatives, regulation, high-frequency data, stochastic control, applied deep learning, and a professional internship.
The program explicitly seeks a balance between rigorous theoretical coursework and applied training, with several modules taught or co-supervised by professionals. This connection between academic depth and real-world practice is central to the identity of the degree.
Admissions
Expected background, application timeline, and supporting documents.
Prerequisites
The program is designed for applicants with a strong Master 1-level or equivalent background in probability, statistics, financial markets, and programming, especially in C and Python.
Application period
Inception platform
From January 30, 2026 to June 30, 2026.
Required documents
- Statement of purpose or motivation letter.
- Curriculum Vitae.
- All academic transcripts available since the baccalaureate.
Optional documents
- Proof of English proficiency.
- VAPP file for prior-learning recognition requests.
- Supporting documents for eligible displaced or protected applicants.
Fees and useful information
Fees and scholarship arrangements may vary depending on the student’s administrative situation. Current official information should always be checked directly on the Paris-Saclay program page.
Our Curriculum
A curriculum organized around a strong common foundation followed by advanced specialization.
The structure below reorganizes the full official program content into a clearer editorial format: overall progression, key courses by semester, and detailed course descriptions.
Semester 1
Core quantitative methods, numerical tools, statistics, and computing.
Required courses
Numerical Pricing Methods and Model Calibration
Monte Carlo, hedging, Greeks, discretization, and calibration.
Elective courses
Deep Learning
MLP, regularization, RNN, CNN, and applied deep learning methods.
Computer Programming
C++, object-oriented programming, dynamic memory, templates, and VBA.
Interest Rate Models
Vasicek, Hull-White, CIR, HJM, LMM, and calibration.
Financial Markets and Actuarial Finance
Swaps, forwards, futures, options, and structured products.
Machine Learning
Trees, random forests, boosting, clustering, and dimension reduction.
Stochastic Calculus
Brownian motion, Ito calculus, SDEs, Girsanov, and finance PDEs.
Financial Econometrics
Time series, GARCH, volatility estimation, and high-frequency data.
Insurance Finance
ALM, variable annuities, insurance products, and insurer risk.
Risk Management
Market risk, credit risk, liquidity, stress testing, and scoring.
Semester 2
Advanced applied topics, regulation, project work, and internship.
Required courses
Professional Internship
Extended immersion in industry or research.
TOEIC Preparation
Grammar, listening, reading, and intensive exam preparation.
Elective courses
Derivatives
Rates, FX, rough volatility, and structured products.
Particle Systems and Mean Field Games
McKean dynamics, propagation of chaos, and finance/ML applications.
Stochastic Control and Modeling in Finance and Insurance
Optimal control, HJB, BSDEs, ruin theory, and liquidity.
Cutting Edge Finance
Industry-facing team project in quantitative finance.
Sustainable Finance and Asset Management
Risk premia, ESG, portfolio optimization, and allocation.
Machine Learning Techniques in Finance
Pricing, calibration, and hedging using ML and deep learning.
High-Frequency Data and Order Books
Microstructure, Hawkes processes, market impact, and simulation.
XVA and Regulation
CVA, DVA, FVA, MVA, KVA, and balance-sheet optimization.
Approximation of Processes
SDE discretization, Greeks, exotics, and Monte Carlo error.
Course Details
Detailed course descriptions.
Computing Project
ECTS: 3. Semester: 1. Language: English.
Contact hours: 2h lecture, 10h tutorial.
Content: C++ project with Excel/VBA interface on quantitative finance topics.
Goal: Conduct a full quantitative programming project in pairs.
Numerical Pricing Methods and Model Calibration
ECTS: 6. Semester: 1.
Content: Review of stochastic analysis, Monte Carlo, variance reduction, advanced Greeks, exotic pricing, SDE discretization, and calibration.
Deep Learning
ECTS: 3. Semester: 1.
Content: Perceptrons, multilayer networks, backpropagation, activation functions, regularization, recurrent and convolutional networks.
Computer Programming
ECTS: 6. Semester: 1.
Content: Object-oriented C++, encapsulation, constructors, operator overloading, inheritance, dynamic allocation, templates, and introductory VBA.
Interest Rate Models
ECTS: 3. Semester: 1.
Content: Rate derivatives, short-rate models, Hull-White, CIR, HJM, Libor Market Model, and market calibration.
Financial Markets and Actuarial Finance
ECTS: 3. Semester: 1.
Content: Rate hedging, swaps, forwards, futures, vanilla options, CRR and Black-Scholes models, structured products, and investment strategies.
Machine Learning
ECTS: 6. Semester: 1.
Content: Parametric and non-parametric models, trees, bagging, random forests, boosting, PCA, Gaussian mixtures, spectral clustering, and ROC analysis.
Stochastic Calculus
ECTS: 6. Semester: 1.
Content: Brownian motion, Ito formula, stochastic differential equations, Feynman-Kac representation, Girsanov theorem, pricing and dynamic hedging.
Financial Econometrics
ECTS: 3. Semester: 1.
Content: Time series review, stylized facts, GARCH, volatility forecasting, VaR, duration models, and high-frequency estimation.
Insurance Finance
ECTS: 3. Semester: 1.
Content: Traditional insurance products, fixed annuities, CPPI, variable annuities, ALM, and insurer risk mitigation.
Risk Management
ECTS: 6. Semester: 1.
Content: Market, credit, counterparty, operational, liquidity, and model risk, as well as copulas, EVT, scenario analysis, and credit scoring.
Derivatives
ECTS: 4. Semester: 2.
Content: Yield-curve modeling, fixed-income management, structured rates products, FX derivatives, rough volatility, and structured products.
Particle Systems, Mean Field Games, and Applications
ECTS: 4. Semester: 2.
Content: Particle systems, propagation of chaos, McKean diffusions, mean field games, and applications to machine learning and finance.
Stochastic Control and Modeling in Finance and Insurance
ECTS: 4. Semester: 2.
Content: Optimal stopping, switching, singular control, duality, dynamic programming, HJB equations, viscosity solutions, quadratic BSDEs, capital structure, dividends, and ruin theory.
Cutting Edge Finance
ECTS: 4. Semester: 2.
Content: Group project on a topic defined by industry professionals, with academic co-supervision.
Sustainable Finance and Asset Management
ECTS: 2. Semester: 2.
Content: Risk-based investing, smart beta, ESG, large-scale optimization, robo-advisors, machine learning, and calibration.
Machine Learning Techniques in Finance
ECTS: 2. Semester: 2.
Content: Applications of machine learning to pricing, calibration, and hedging, with a focus on deep neural networks and Gaussian processes.
High-Frequency Data and Order Books
ECTS: 2. Semester: 2.
Content: High-frequency data, trade and quote streams, Poisson and Hawkes processes, limit order books, and empirical market impact.
XVA and Regulation
ECTS: 2. Semester: 2.
Content: CVA, DVA, FVA, MVA, KVA, conditional expectations, expected shortfall, nested Monte Carlo, and deep-learning methods.
Approximation of Processes
ECTS: 2. Semester: 2.
Content: Brownian SDE discretization, strong and weak errors, barrier/lookback/asian options, Greeks, and Monte Carlo concentration bounds.
Professional Internship and TOEIC Preparation
Internship: 15 ECTS in Semester 2, with immersion in industry or research.
TOEIC: 2 ECTS, including grammar, listening, reading, and intensive preparation.
Career Development
Career paths in financial markets, insurance, asset management, data science, and research.
The degree prepares students for roles such as quantitative analyst, R&D engineer, financial analyst, statistician, and data scientist. It also supports careers in market risk, credit risk, insurance, asset management, and advanced analytics.
- Quantitative analyst and model developer.
- Market, credit, liquidity, and model risk roles.
- Asset management and sustainable finance.
- Data science and artificial intelligence.
The academic depth of the program also supports doctoral continuation and research-oriented paths in applied mathematics, stochastic modeling, quantitative finance, and statistics.
- Doctoral studies.
- Research in applied mathematics and finance.
- Higher education and expert quantitative roles.
Alumni
An alumni community connecting graduates, students, and employers.
The program aims to build a strong alumni network across quantitative finance, risk, data science, insurance, and research. Graduates can remain involved through mentoring, internship referrals, recruiting conversations, and guest talks.
- Mentoring and career conversations with current students.
- Internship and full-time opportunity sharing.
- Industry panels, testimonials, and alumni spotlights.
This section can host alumni stories, employer introductions, and short profiles of graduates working in banks, insurance, consulting, fintech, and doctoral research.
- Quant roles in front-office and model validation.
- Data science and AI trajectories.
- Research and doctoral continuation paths.
Meet the Team and Learn More
Program contact information.
Program Lead
Prof. Ahmed Kebaier & Prof. Vathana Ly Vath
Teaching location: Evry
Universite Paris-Saclay
Head office: 3 rue Joliot Curie, Batiment Breguet, 91190 Gif-sur-Yvette
Public reception: 9 rue Joliot Curie, Batiment Bouygues, 91190 Gif-sur-Yvette
Phone: +33 1 69 15 67 50