M2 Modélisation Aléatoire

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

 
 
 
 
 
 
International students
 
 

Presentation of M2MO for international students

Imprimer

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.

There is no tuition fee, the students are only required to pay a symbolic administrative fee of the order of 300 euros per year.

List of courses

Imprimer
    1. Core modules
      • Stochastic calculus and diffusion processes (F. Comets)
      • Markov Chain (M. Merle)
      • Introduction to machine learning (S. Gaiffas)
      • Data modelling: founding principles (A. Fischer & S Gribkova)
    2. Quantitative Finance Modules
      • Stochastic processes in finance (P. Tankov & C. Fontana)
      • Financial products (B. Bruder)
      • Energy markets (R. Aid & O. Feron)
      • Advanced interest rate modelling (Z. Grbac)
      • Model risk and model validation for pricing (P. Tankov)
    3. Data Science Modules
      • Mixture models for data analysis (F. Rossi)
      • Statistical learning (S. Clemencon)
      • Wavelet methods (G. Kerkyacharian)
      • Methods for large/high dimensional data sets (S. Boucheron)
      • Computational Statistics (P. Latouche)
      • Data science and statistics for industry (M. Mougeot)
    4. Asset Management Modules
      • Stochastic control in finance (H. Pham)
      • Backward Stochastic Differential Equation 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)
      • Projects (S. Scotti)
    6. Probability & Statistics Modules 
      • Mixing time and cutoff phenomenon for Markov chains (J. Salez)
      • Interacting particle systems (C. Toninelli)
    7. Risk Management Modules 
      • Credit risk modelling (R. Rouge)
      • Risk measures and risk Management (H. Pham & A. Ouattara)
      • Copula with applications to finance (J.D. Fermanian)
    8. Statistics & Finance Modules
      • Financial time series (J.M. Bardet)
      • Prediction and sequential investments (J.Y. Audibert)
      • Statistical inference for diffusion processes (A. Gloter)
      • Filtering techniques and statistical analysis applied in finance (J. Turc)
    9. Numerical & Approximation Methods Modules
      • PDE methods in finance (Y. Achdou & O. Bokanowski)
      • Monte Carlo Methods (N.Frikha)
      • Advanced probabilistic numerical methods in finance (J.-F. Chassagneux)
      • Asymptotic methods in finance (H. Pham)