M2MO: Modélisation Aléatoire, Finance et Data Science

Master en statistique, probabilités et finance - Université Paris 7 - Paris Diderot

International students

Presentation of M2MO for international students


M2MO is a top-level one-year master program in probability and statistics with specialization in quantitative finance or in data science.

The finance track offers courses in all aspects of modern quantitative finance starting from the basics of option pricing to more advanced subjects like volatility surface modeling, advanced interest rate models, credit risk, portfolio management etc.

The data science track offers courses in statistical learning, advanced techniques for big data, treatment of massive data sets and on applications of these methodologies in an industrial environment.

Our alumni are hired by quantitative research and risk management teams of major international banks (mostly in Paris and London), insurance companies, and by statistical departments of other major industrial actors. Alternatively, after a PhD thesis in a relevant field, M2MO offers access to research and faculty positions in the academia.

The program starts around mid-September and is structured in three two-month periods (trimesters) of intensive courses, followed by a 5-6 month industrial internship from mid-April to the end of September. During the first 2 months of the program, students of both tracks follow intensive courses on the theory of stochastic process and advanced statistical methods. The second and the third trimesters are dedicated to elective courses in the chosen specialization domains.

The M2MO is formally a second-year master program, which means that admission is open to students who possess a French M1-level degree or equivalent. International students typically come when they already have a master's degree in mathematics and want to acquire a top-notch specialization in quantitative finance or data science.

The tuition fee is 3700 euros for the whole administrative year.

List of courses

    1. Core modules
      • Stochastic calculus and diffusion processes (S Péché )
      • Markov Chain (M. Merle)
      • Introduction to machine learning (S. Gaiffas and A. Fisher)
      • Basics of Data modelling and statisctiacl inference (S. Delattre)
    2. Quantitative Finance Modules
      • Derivatives modelling  (S. Crépey & S. Scotti)
      • Financial products (B. Bruder)
      • Quant analysis (S. Crépey)
      • Energy markets (R. Aid & P. Gruet)
      • Advanced interest rate modelling (Z. Grbac)
    3. Data Science Modules
      • Statistical learning (S. Clemencon and E. Chautru)
      • Optimization for machine learning (G. Garrigos) 
      • Graphical models for machine learning (F. Rossi)
      • Projects in Data Science: CRM (K. Tribouley)
      • Methods for large/high dimensional data sets (S. Boucheron)
      • Introduction to reinforcement learning (J. Lussange)
      • Data science and statistics for industry (M. Sayed and L. Massoulard)
      • Deep learning (S. Gaiffas)
    4. Asset Management Modules
      • Stochastic control in finance (H. Pham)
      • Nonlinear methods in Finance (M.C. Quenez)
      • Algorithmic trading (O. Gueant)
      • Quantitative Asset management (B. Bruder)
    5. Computer Science Modules
      • C++ (O. Carton)
      • Statistical softwares (S. Souchet)
    6. Risk Management Modules 
      • Credit risk modelling (R. Rouge)
      • Risk measures and risk Management (H. Pham & A. El Alami)
      • Copulae and financial applications (J.D. Fermanian)
    7. Statistics & Machine learning in Finance Modules
      • Financial time series (J.M. Bardet)
      • Prediction and sequential investments (J.Y. Audibert)
      • Statistical inference for diffusion processes (A. Gloter)
      • Point processes and applications in finance (E. Locherbach)
      • Machine learning for finance (H. Pham)
    8. Numerical & computational Methods Modules
      • PDE methods in finance (Y. Achdou & O. Bokanowski)
      • Monte Carlo Methods (N.Frikha)
      • Advanced probabilistic numerical methods in finance (J.-F. Chassagneux)