Master 2 MMMEF - Curriculum

Students involved in the Master 2 MMMEF program have their academic year divided into two parts:
  • a first part (two trimesters) with courses: fundamental courses, specialization and advanced courses,
  • a second part during which students either do an internship or carry out academic research and write a Master's thesis.
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Each student is admitted to the Master's program to follow one of the four tracks:
  • Quantitative Finance,
  • Optimization and Control,
  • Mathematical Economics, Games and Decision,
  • Economics, Finance and Games.

As far as courses are concerned, students need to validate:
  • 20 ECTS in the list of fundamental courses (UE1),
  • 17.5 ECTS in the list of specialization and advanced courses (UE2) and 2.5 ECTS in the form of "language certification" or seminars.
Students have to choose a majority of courses in line with their track but they are free to diversify their choices. The choice of courses eventually need to be approved by the supervisor of the student's track.

List of courses

Each course represents 2.5 ECTS unless otherwise indicated.

Fundamental courses (UE1)

  • Stochastic calculus 1 (5 ECTS)
  • Arbitrage theory
  • Market risk measure
  • Introduction to finance
  • Financial time series analysis
  • Python and advanced Python for optimization and Finance​
  • Combinatorial optimization
  • Numerical methods in optimization (5 ECTS)
  • Convex analysis and optimization
  • Decision making: foundations
  • Decision under uncertainty
  • Information, design et markets (5 ECTS)​
  • General equilibrium theory (5 ECTS) 
  • Game theory (5 ECTS)
  • ​Statistical learning
  • Computer training: C++​​
The syllabus are available here.
The students can also choose one course taught in another Master programme (e.g. M2 IRFA, M2 APE, etc.)

Specialization and advanced courses (UE2)

  • Stochastic calculus 2 (5 ECTS)​
  • Yield curve models
  • PDE Methods in Finance 
  • Interdisciplinary finance
  • Calibration in quantitative finance
  • Malliavin calculus and Monte Carlo methods
  • Algorithmic trading
  • Optimal control
  • Advanced combinatorial optimization
  • An introduction to data science and big data
  • Advanced decision and modelling
  • Network models and applications
  • Natural resources management
  • ​Microeconomics of insurance
  • ​Advanced game theory​
  • Cooperative games
  • Rationality and strategy in economics and politics
  • Information, finance and game theory
  • Equilibrium, fixed-points and computations
  • Computational models for socio-economic interaction
The students can also choose one course taught in another Master programme (e.g. M2 IRFA, M2 APE, etc.)