Program Year
LEVELLING COURSES  2 courses

View detail : R programming for finance
R programming for finance
Total hours : 21

View detail : Python for finance
Python for finance
Lecturer :
HOUCINE SENOUSSI
Total hours : 18
Overview :
Our aim in this course is to implement some key concepts in quantitative finance using popular Python packages such as :
 NumPy : the fundamental package for scientific computing.
 Matplotlib : the main 2D plotting library.
 SciPy : another scientific computing library containing toolboxes dedicated to optimization, statistics and many other fields.
 Pandas : A library offering powerful data structures and tools for data analysis.
The data we will use is extracted from free online sources (Google, Yahoo, ...). The main parts of the course are the following :
1. Python basics : Data types, data structures, programs structure and packages.
2. Numpy, Matplotlib : discovering these packages with application to Monte Carlo simulation (look at the potential evolution of asset prices over time/Random walk).
3. Scipy : Introduction and application to a regression analysis of stock prices.
4. Pandas and Matplotlib. Introduction and Application (I) : importing, visualizing and analysing Time series financial data.
5. Pandas and Matplotlib. Advanced aspects and application (II) : Volatility calculation, Algorithmic trading, Creating, testing and improving a trading strategy.
Coefficient : 1
Require prerequisites :
Basics of algorithmics.
Learning outcomes :
Mastering the structure of the Python language, a good knowledge of the most important libraries for financial applications (Numpy, Matlplotlib, Scipy, Pandas).
Assessment :
Several programming assignments (one for each class).

View detail : Monte Carlo Simulations in finance  MathLab
Monte Carlo Simulations in finance  MathLab
Lecturer :
IRINA KORTCHEMSKI
Total hours : 21
Overview :
Lecture 1 and 2: Introduction to MATLAB. Tutorial with numerical optimization of Rosenbrock’s function and simulation of the Brownien Motion. Markowitz portfolio optimization.
Lecture 3: Binomial options pricing model. European, American, Butterfly and Barrier Knock  Out options. Simulation of a Binomial tree and assets trajectories.
Lecture 4: Black and Scholes Model. MonteCarlo method for option valuation. European option. Correlated Brownian motions. Basket et Exchange options.
Lecture 5: Black and Scholes Model. Strongly Pathdependent options. Asian option. Lookback and Choosers. Stochastic volatility models. EulerMaruyama approximation of Stochastic Differential Equations. Option and asset pricing in the Heston model.
Lecture 6 and 7: Merton Model. Poisson distribution. Simulation of assets trajectories with jumps. Option pricing in the Merton model.
Coefficient : 1
Recommended prerequisites :
The notions of stochastic calculus, Black and Scholes models, Ito's formula.
Learning outcomes :
The students will learn important principles of implementation of financial models and master algorithms of evaluation of different types of derivative securities: European, American, standard, barrier and path dependent options on stocks.This course gives a comprehensive introduction to Monte Carlo and finite difference methods for pricing financial derivatives. At the end of the course, the student should have a thorough understanding of the theory behind Monte Carlo methods, be able to implement them for a range of applications, and have an appreciation of some of the current research areas.
Assessment :
Control of Knowledge: Defense of a Project.
Bibliographyrecommended reading
Reading List:
1) S E Shreve, Stochastic Calculus for Finance II: ContinuousTime Models, Springer 2004.
2) P Glasserman, Monte Carlo Methods in Financial Engineering, SpringerVerlag, 2004.
3) P Wilmott, S D Howison and J Dewynne, Mathematics of Financial Derivatives, CUP, 1995.

View detail : Financial Econometrics I
Financial Econometrics I
Lecturer :
GAELLE LE FOL
Total hours : 24
Overview :
This course is an introduction and/or refresher course in Econometrics that focuses on techniques for estimating regression models, on problems commonly encountered in estimating such models, and on interpreting the estimates. The goal is to provide participants with the basic skills and knowledge necessary to undertake empirical research and to prepare them to the advanced course in Econometrics of Financial Markets. If Gretl will be the econometric software used in the course, it is possible to use R.
Course outline
 How to build an econometric model and how to use it?
 The (simple and multiple) linear regression model
 Inference, hypothesis testing and prediction
 Specification and diagnostic testing (heteroskedasticity, autocorrelation, model specification)
 Selection criteria
 Alternative to OLS (2SLS, ML, GLS, Quantile regression)
Coefficient : 3 ECTS / Coefficient 1.5 ==> M1 Financial Markets
Recommended prerequisites :
First course in programming
Require prerequisites :
Mathematics and Statistics (bachelor level)
Learning outcomes :
Theoretical and practical knowledge of linear regression models estimation technics. Being able to set up an econometric analysis.
Bibliographyrecommended reading
 Adkins L. C., learneconometrics.com/gretl/using_gretl_for_POE5.pdfUsing gretl for Principles of Econometrics, Version 1.041, August 2018, Free copy;
 Brooks C., Introductory Econometrics for Finance, Second Edition, Cambridge University Press, 2014 ;
 Gelman A., J. Hill and A. Vehtari, 2021, Regression and Other Stories, 1st Edition, Cambridge University Press, 2021;
 Gujarati D., Basic Econometrics, McGraw Hill Higher Education; 5th Revised edition edition, 2009 ;
 Hill C., W. Griffiths and G. Lim, Principles of Econometrics, Wiley, 5th Edition, 2018 ;

View detail : Crash course evaluation
Crash course evaluation
Lecturer :
PASCAL DUMONTIER
Total hours : 30
Coefficient : 1

View detail : DIGITAL Finance
DIGITAL Finance
Total hours : 24
Coefficient : 1
FUNDAMENTAL COURSES  4 courses for 24 ECTS (Option 1) or 5 courses for 30 ECTS (Option 2)

View detail : Game theory
Game theory
Ects : 6
Lecturer :
MARION OURY
Total hours : 36
Overview :
Chapter 1: Normal form games: pure and mixed strategy Nash equilibrium; weakly/strictly dominated strategies , iterated elimination of dominated strategies.
Chapter 2: Dynamic games: Backward induction, subgame perfect Nash equilibrium, repeated games.
Chapter 3: Incomplete information (in static games): Bayesian Nash equilibrium; introduction to some applications (auctions, finance...)
Coefficient : 1
Require prerequisites :
The student must be at ease with some basic mathematical notions such as: derivations, firstorder conditions...
Learning outcomes :
The objective of the course is to give some fundamental background in interactive decision making and its applications. After having attended the classes, the students will be able to understand the basic tools of game theory and the importance of this field in economics and finance.
Assessment :
A midterm exam and a final exam

View detail : Term structures : theory, models and empirical tests
Term structures : theory, models and empirical tests
Ects : 6
Lecturer :
DELPHINE LAUTIER
Total hours : 30
Overview :
At the end of this course, the students must have a broad knowledge about the term structures of derivative prices: the theories, the valuation methods, the econometric techniques, the empirical tests as well as the applications.
Coefficient : 1
Recommended prerequisites :
Students who choose this course must also attend the course “Finance in continuous time”
Learning outcomes :
This course in a introduction to research on term structures. While being centered on the case of commodities, it also proposes some comparisons with interest rates, and some generalization to other assets like equities and foreign exchange. The course presents the theories of the term structures, their empirical implications, the methodological issues associated with empirical tests, and empirical tests.
Assessment :
Participation, 20% + Final exam, 80%
Bibliographyrecommended reading
Main references:
 Danthine J.P., Donaldson J.B., Intermediate Financial Theory, 2d Ed., Elsevier, 2005.
 Hull J., Options, futures and other derivatives, 9th Ed.
 Kolb R.W. , Overdahl J.A. , Futures, options, and swaps, 5th Ed., Blackwell, 2007.
 Williams J., The economic function of futures markets, Cambridge University Press, 1986
 Wilmott P., Paul Wilmott on Quantitative Finance, 3volume set, 2nd Ed., Wiley, 2006.

View detail : Finance in continuous time
Finance in continuous time
Ects : 6
Lecturer :
RENE AID
Total hours : 30
Overview :
Asset pricing, contingent claim, stochastic process,
brownian motion, Itô's formula, optimal stopping time.
This course is an introduction to "Derivative pricing and stochastic calculus II".
It introduces the standard concepts and tools allowing to
understand arbitrage theory in continuoustime. The requirements from
probability theory are made as basic as possible to make the lectures
accessible to studends without a strong background in applied
mathematics.
Coefficient : 1 (Master Finance)<br /> 3ECTS  Coefficient 1 (M2 Quantitative Economics)
Learning outcomes :
In the end of this course, the students must be comfortable with:
i) Basic concepts of contingent claims,
ii) the binomial model;
iii) stochastic integrals and Itôs calculus;
iv) the Black and Scholes model,
v) Merton's optimal porfolio problem.
Bibliographyrecommended reading
Steven Shreve, Stochastic Calculus for Finance I: The Binomial Asset Pricing Model, 2005.
Steven Shreve, Stochastic Calculus for Finance II: ContinuousTime Models , 2005.

View detail : Derivative Pricing and Stochastic calculus
Derivative Pricing and Stochastic calculus
Ects : 6
Lecturer :
PAUL GASSIAT
Total hours : 24
Overview :
Advanced derivative pricing and stochastic calculus.
This course requires that the students have validated the course "Finance in continuous time". Its gives more insights into the theory of derivative asset pricing as well as the main models and techniques used in practice.
Coefficient : Coefficient 1 (M2 Research in Finance)<br /> Coefficient 3 (M2 Financials Markets)
Require prerequisites :
Basic probability theory, stochastic processes (martingales,...), stochastic calculus in continuous time (Brownian motion, Itô formula, Stochastic differential equations)
Learning outcomes :
The lecture starts with discrete time models which can be viewed as a proxy for continuous settings, and for which we present in detail the theory of arbitrage pricing. We then develop on the theory of continuous time models. We start with a general Itôtype framework and then specialize to different situations: Markovian models, local and stochastic volatility models. For each of them, we discuss the valuation and the hedging of different types of options : plain Vanilla and barrier options, American options, options on realized variance, etc. Finally, we present several specific volatility models (Heston, CEV, SABR,...) and discuss their specificities.
Bibliographyrecommended reading
Bouchard B. et Chassagneux J.F., Fundamentals and advanced Techniques in derivatives hedging, Springer, 2016.
Lamberton D. et B. Lapeyre, Introduction au calcul stochastique appliqué à la finance, Ellipses, Paris, 1999.

View detail : Corporate finance
Corporate finance
Ects : 6
Lecturer :
EDITH GINGLINGER
LUC RENNEBOOG
Total hours : 30
Overview :
Corporate finance addresses the questions of how companies raise financing and structure their debt. The major areas covered in this course are:
 Primary markets: why do companies go public, and why are initial public offerings (IPOs) generally underpriced?
 Capital structure: how do companies choose between different types of securities, particularly debt and equity? Why do firms hold cash?
 Security issuance, seasoned equity offerings, convertible bond offerings
 Internal capital markets and restructuring
 Corporate payout policy: How much do companies pay out to investors as dividends?
 Corporate social responsability
Coefficient : 1
Recommended prerequisites :
This course requires that the students have validated the course "Fundamentals of corporate finance".
Learning outcomes :
In this course we will examine some of the most important empirical contributions to corporate finance in the areas of initial public offerings, capital structure, security issuance, internal capital market, dividend policy and corporate social responsibility. The objective is to prepare students to critically evaluate and conduct research in corporate finance.
Assessment :
Presentations of research papers: 40%
Deliverables (exercises, cases): 20%
Final Exam: 40%
Bibliographyrecommended reading
Constantinides George M., Milton Harris, Rene M. Stulz, 2013, Handbook of the Economics of Finance: Corporate Finance, NorthHolland
Eckbo Espen, 2008, Handbook of corporate finance, Empirical corporate finance,vol. 1 et 2 North Holland
Tirole J., 2006, The theory of corporate finance, Princeton University Press

View detail : Management of Credit Risk : Theory and applications
Management of Credit Risk : Theory and applications
Ects : 6
Lecturer :
OLIVIER TOUTAIN
Total hours : 30
Overview :
This course is an introduction to Credit Risk in its different dimensions (Default/Recovery/Transition), starting from a description of the phenomenology of Credit Risk, the different intruments subject to credit risk to the different modelling approach both for single name or portfolio exposure. Numerous concrete examples illustrate the concepts introduced and the mathematical model are studied through exercises. The aim is to cover the broad domain of credit risk from retail products (credit card, mortgages) to sovereign credit risk, looking at the existing practicla issues that students would have to solve in their future employment either as Risk Managers, Traders, Asset Managers, Credit Risk Officer, Analysts, ...
A book covering the different concepts presented in class is made available and corrected exercise are also available to the students.
Coefficient : 1
Recommended prerequisites :
Basic knowledge of fixed income products and interest rate notions.
Basic knowledge of probability / statistics is a plus (Theorem of Total Probability, Law of Large Number, Markov Chain, Univariate distributions)
Learning outcomes :
The key concepts pertaining to credit risk should be understood by students and a solid framework would allow an easier analysis of credit risk and its management in their future job.
Assessment :
A final exam mixing (i) questions on topic seen during the class and (ii) quantitative exercises to measure credit risk.
Bibliographyrecommended reading
Credit Risk  Pricing, Measurement, and Management  Darrelle Duffie  Princeton Universirty Press
Credit Risk Modeling  David Lando
Credit Risk  Tomasz Bielecki, Marek Rutkowski

View detail : Asset pricing theory
Asset pricing theory
Ects : 6
Lecturer :
JEROME DUGAST
Total hours : 30
Overview :
In this course, we will discuss a wide range of topics ranging from optimal portfolio, the CAPM, factor models, consumptionbased asset pricing, and arbitrage pricing, to more special ones including asymmetric information, and limits to arbitrage.
 Optimal Portfolio Theory and the CAPM
 Factor Models
 Decision Making under Uncertainty
 Consumptionbased Asset Pricing
 Arbitrage Pricing
 Dynamic Asset Pricing
 Asymmetric Information and Asset Prices
 Limits to Arbitrage
Coefficient : 1 (M1 Finance)<br /> 3ECTS  Coefficient 2 (M2 Quantitative Economics)
Learning outcomes :
Master the theoretical concepts of asset pricing
Assessment :
Evaluation: assignments 30%, final exam 70%

View detail : Information economics
Information economics
Ects : 6
Lecturer :
FRANCOISE FORGES
Total hours : 36
Overview :
The course starts with models of economic interaction under asymmetric information (typically, dynamic games with incomplete information). Signaling games are studied as a first illustration, with economic applications (e.g., Spence's signaling model). The course goes on with auctions: private values, revenue equivalence theorem, common values (winner’s curse), etc. and moves from optimal auction mechanisms to the general topic of mechanism design (general framework, revelation principle, optimal mechanisms in various economic frameworks). Contract theory is studied in the light of mechanism design. The next themes are efficient (e.g., VickreyClarkeGroves) mechanisms and implementation. The study of multiagent, multiprincipal problems is possibly pushed further.
Coefficient : 2
Require prerequisites :
A basic course in game theory (L3 or M1) and in microeconomics (L3 or M1).
Learning outcomes :
After attending the classes, the students will be able to model and analyze problems of resource allocation among "asymmetric" agents, who differ from each other regarding (i) their information on the basic economic situation at hand and (ii) their commitment power.
Bibliographyrecommended reading
Jehle, G. and P. Reny (2011),
Advanced Microeconomics, Pearson.
MasColell, A., M. Whinston, and J. Green (1995),
Microeconomic Theory, Oxford University Press.
SEMINARS  2 courses for 6 ECTS (Option 1) or 0 (Option 2)

View detail : Machine Learning in Finance
Machine Learning in Finance
Ects : 3
Lecturer :
PIERRE BRUGIERE
Total hours : 21
Overview :
Methods of Statistical Learning, applied to some financial problems of credit rating, anomaly detection and yield curve approximations
Coefficient : 1
Recommended prerequisites :
Basic linear algebra and differential calculus.
Require prerequisites :
Basic linear algebra and differential calculus.
Learning outcomes :
Vapnik Chervonenkis dimension, PAC learning, calibration versus prediction, SVM (Support Vector Machines) classifiers, Mercer's theorem, CSVMs, muSVMs and single class SVMs. Basics of decision trees, random forests and penalized regressions.
Assessment :
Exam
Bibliographyrecommended reading
Trevor Hastie, Robert Tibshirani, Jerome Friedman: The Elements of Statistical Learning, Springer
Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani : An Introduction to Statistical Learning, Springer
Christopher Bishop: Pattern Recognition and Machine Learning, Springer

View detail : Regulation and financial Markets
Regulation and financial Markets
Ects : 3
Lecturer :
THIBAULT GODBILLON
Total hours : 21
Overview :
Financial regulation, prudential regulation around the world, regulation to "too big to fail" banks and fragmented environment
To give the students an overview of recent and future regulatory prudential and market reforms, at the global level and across regions.
Coefficient : Coefficient 1 (M2 Research in Finance)<br /> Coefficient 1.5 (M2 Financial Markets)
Learning outcomes :
Master the regulatory prudential and market reforms, at the global level and across regions

View detail : Structured products in practice
Structured products in practice
Ects : 3
Lecturer :
AYMERIC KALIFE
Total hours : 21
Overview :
Structured products, evaluation and control.
This course is an initiation to new structured products. It shows how to value such products, and how to control the associated risks
Coefficient : 1
Learning outcomes :
Participants will lear how financial institutions can build and structured products, how they value them, and what they are done for.

View detail : Microeconomics for finance
Microeconomics for finance
Ects : 3
Lecturer :
JEROME DUGAST
Total hours : 24
Overview :
Syllabus:
1. Equilibrium in an Exchange Economy
2. Decision Making under Uncertainty
3. Equilibrium in Markets for Securities
4. Investment Decision under Market Imperfections: the PrincipalAgent Problem
Coefficient : 0.5 (M1 finance)<br /> 1 (M2 Finance)
Recommended prerequisites :
Basic notions of mathematical analysis and algebra are required.
Learning outcomes :
This 24 hours course aims at acquainting students with relevant microeconomics methods to tackle finance issues.
Assessment :
Final exam and assignment
FUNDAMENTAL COURSES  2 courses for 12 ECTS (Option 1) or 1 course for 6 ECTS (Option 2)

View detail : Fixed income derivatives
Fixed income derivatives
Ects : 6
Lecturer :
AYMERIC KALIFE
Total hours : 30
Overview :
Interest rate derivatives, investment and hedging
The objective of the course is to give an all round comprehensive knowledge and understanding of the theory and the daytoday use of interest rates derivatives, for both investment and hedging purposes.
Various views about the level and shape of the yield curve are implemented with selected absolute and relative value trades. across “Directional” and “Volatility” strategies.
Finally, this course introduces to the the sustainable investing landscape (“ESG”) which has met some growing and significant appetite over the past decade, while providing insights and methodology for managing fixed income ESG investment strategies.
Coefficient : 1
Learning outcomes :
Participants will learn how banks, portfolio managers and corporate treasuries use rates derivatives alike in the management of risks, for trading, hedging and arbitrage and their role in the daytoday running of the finances of businesses.
Assessment :
Take home exam: trade idea
Table exam
Bibliographyrecommended reading
FixedIncome Securities: Valuation, Risk Management, and Portfolio Strategies, Lionel Martellini, Philippe Priaulet
Fixed Income Analysis, CFA institute,
Barbara S. Petitt (Author), Jerald E. Pinto, Wendy L. Pirie, Bob
Interest Rate Risk Modeling, Wiley, Sanjay K. Nawalkha, Gloria M. Soto, Natalia A. Beliaeva
Fixed Income Mathematics, Analytical & Statistical Techniques, Frank J. Fabozzi

View detail : Information and financial markets
Information and financial markets
Ects : 6
Lecturer :
PASCAL DUMONTIER
Total hours : 30
Overview :
Because investors are the major users of information disclosed by firms, specially listed firms, reporting regulatory bodies are more and more concerned about providing investororiented information. The course aims to provide an overview of the research dedicated to the role of corporate reporting in capital markets. By focusing on both theory and empirical studies, it helps students become familiar with the foundations of research in corporate disclosure and reporting.
Coefficient : 1
Recommended prerequisites :
Good understanding of the principles and foundations of finance, and basics of econometrics.
Learning outcomes :
Become familiar with the questions, goals and tools of research in corporate disclosure and reporting.
Assessment :
Oral presentation (50% of final grade). Term paper (50% of final grade)
Bibliographyrecommended reading
William Scott, “
Financial Accounting Theory” PearsonPrentice Hall 7th edition (2014).
Ross Watts & Jerold Zimmerman "
Positive Accounting Theory" Prentice Hall (1986) 388 p.
Craig Deegan & Jeffrey Unerman "
Financial Accounting
Theory"? Mc Graw Hill (2011) 576 p.

View detail : Microeconomics : public policy
Microeconomics : public policy
Ects : 6
Lecturer :
GABRIELLE FACK
SIDARTHA GORDON
Total hours : 36
Coefficient : 1
SEMINARS  4 courses for 12 ECTS (Option 1) or 6 courses for 18 ECTS (Option 2)

View detail : Strategies and actors on financial markets
Strategies and actors on financial markets
Ects : 3
Lecturer :
CHARLESALBERT LEHALLE
Total hours : 21
Coefficient : 1

View detail : Microstructure of financial markets
Microstructure of financial markets
Ects : 3
Lecturer :
JEROME DUGAST
Total hours : 21
Overview :
The field of market microstructure combines theoretical modeling, institutional knowledge, and empirical analysis to understand how prices result from the interactions of traders in financial markets. The course aims to acquaint students with (i) the canonical models in microstructure, and (ii) econometric models used to test the predictions of those models.
Course structure:
 Trading Mechanisms
 Measuring Liquidity
 Price Dynamics and Liquidity
 Trade Size and Market Depth
 Empirical Analysis
Coefficient : 1
Learning outcomes :
Master the concepts of financial markets microstructure
Assessment :
Evaluation: assignement 30%, final exam 70%
Bibliographyrecommended reading
Foucault, Thierry, Marco Pagano, and Ailsa Röell, Market Liquidity: Theory, Evidence, and Policy, Oxford University Press, 2013.

View detail : Advanced corporate finance
Advanced corporate finance
Ects : 3
Lecturer :
GILLES CHEMLA
Total hours : 21
Overview :
Recent developments in the theory of corporate finance
To follow this course, the students must validate the course "Corporate finance". Course objective : the main objective of the course is to familiarize students with a number of important, recent results and subjects that have been added to the theory of corporate finance. A second important objective is to provide an overview of some of the modelling issues faced and of the methods that are currently employed in the area of corporate finance
Coefficient : 1
Learning outcomes :
The students will master the most recent reserach issues in corporate finance, with specific insights into modelling.

View detail : Behavioral finance
Behavioral finance
Ects : 3
Lecturer :
ALBERTO MANCONI
Total hours : 21
Overview :
Bevavioral finance, cognitive and social psychology in finance
Introduce students to this relatively new subdiscipline of finance which incorporates insights from cognitive and social psychology into finance. In the past 20 years behavioral finance has emerged as an important stream of thinking in finance.
Coefficient : Coefficient 1 (M2 Research in Finance)<br /> Coefficient 1.5 (M2 Financial Markets)
Learning outcomes :
Relaxing the traditional assumptions of finance models has proved a fruit ful way of understanding financial decisionmaking and anomalies found in empirical tests.

View detail : Advanced empirical corporate finance
Advanced empirical corporate finance
Ects : 3
Lecturer :
RYAN WILLIAMS
Total hours : 24
Coefficient : 1

View detail : Empirical Asset Pricing
Empirical Asset Pricing
Ects : 3
Total hours : 21
Coefficient : 1

View detail : Advanced Market Microstructure
Advanced Market Microstructure
Ects : 3
Total hours : 21
Coefficient : 1

View detail : Time series
Time series
Ects : 3
Total hours : 21
Coefficient : 1

View detail : Data management (Certif finance digitale)
Data management (Certif finance digitale)
Ects : 3
Total hours : 21
Coefficient : 1

View detail : Machine Learning : empirical applications for finance
Machine Learning : empirical applications for finance
Ects : 3
Lecturer :
HOUCINE SENOUSSI
Total hours : 21
Overview :
Basics of ML
 Definitions, approaches and applications.
 Data mining (DM) : definitions and links with ML.
 Classification and regression problems.
 Building and evaluating an ML model.
 Presentation of the main approaches of ML/DM.
 Application I.
Decision Trees :
 Definitions and algorithms.
 Advanced methods based on DL : Bagging, Boostring and Random forests.
 Application II : Making a decision in finance.
Neural networks:
 Definitions.
 Learning in NN : grandient descent and Backpropagation.
 Advanced methods based on NN (Deep learning).
 Application III : : Stock pricing.
Reinforcement Learning :
 Definitions : Agents and environnments.
 Markovian Decision Process (MDP).
 Policies and optimal policies.
 Qlearning.
 Application IV : Trading.
Coefficient : 1
Require prerequisites :
Python programming language.
Learning outcomes :
Building Machine Learning (ML) models for Finance problems. Using ML Python library (and in particular sickitlearn).
Assessment :
Two/Three assignments (building a model + Python programming).

View detail : Alternative Finance
Alternative Finance
Ects : 3
Lecturer :
MARIUS FRUNZA
OLIVIER TOUTAIN
Total hours : 21
Overview :
The aim of this course is to propose an outof the box perspective upon the financial markets and to explore the financial universe beyond the traditional investments like equity, bonds, currency… . We will focus the course on the products and technics used at the fringe of finance including crowfunding, peer2peer finance, shadow banking, Bitcoin, social and environmental impact products….
Throughout this course students will learn about alternative investment supports and alternative financing solutions. The objectives of this lecture are:
 To understand the mechanism of alternative risks: global warming, catastrophic events including on the economy, …
 To explore new area including Environmental, Social, and Governance (ESG) Investment, cryptocurrency etc..
 To get familiar with modelling methods specific to alternative finance.
Course outline:
1. Alternative finance 101
Two faces of the same coin: as investors or as issuers.
2. Modelling methods for alternative finance
 Introduction to the nonGaussian universe
 Real Option Theory
 Extreme value theory
3. Cryptocurrencies: an alternative financial universe
4. Environmental, Social, and Governance (ESG) Investment
5. Cryptocurrency : an alternative financial universe.
6. Alternative capital markets and Fintechs: Focus on Crowdfunding and P2P finance
7. Alternative Risk Transfer
 Climate risks
 Insurance and reinsurance. Focus on CAT Bonds
8. Fintech workshop (industry view)
Coefficient : 1.5
Require prerequisites :
Students must be enrolled in courses Applied Time series and must have past Introduction to Financial Econometrics, Financial Derivatives.
Learning outcomes :
Knowledge on the modelling methods specific to alternative finance.
Assessment :
Project
Bibliographyrecommended reading
 Alexandridis, A. K. and A. D. Zapranis, 2013: Weather Derivatives, Springer, 300 pages.
 Barrieu P., and L. Albertini, 2009: The Handbook of InsuranceLinked Securities, Wiley, 398 pages.
 Frunza, M., 2010: Carbon allowances: A new financial asset, Editions universitaires europeennes, 164 pages.
 Guthrie G., Real Options in Theory and Practice, 2009, OUP, 432 pages.

View detail : Gouvernance externe et marché du contrôle
Gouvernance externe et marché du contrôle
Ects : 3
Lecturer :
MAURICE NUSSENBAUM
Total hours : 18
Overview :
Takeovers impact on shareholders
Effect of antitakeover actions
Corporate Governance
Impact of activism
Coefficient : 1
Recommended prerequisites :
Basic knowledge of economotrics (Event studies)
Require prerequisites :
Master 1 in Finance
Learning outcomes :
Knowledge of academic research and methods on governance and market of corporate control themes
Assessment :
Oral presentations, papers and particpation
Bibliographyrecommended reading
Corporate finance books and literature
Weston Mitchell et Muhlerin : Takeovers Restructuring Corporate Governance 4 Ed Prentice Hall

View detail : Financial macroeconomics
Financial macroeconomics
Ects : 3
Lecturer :
VALERE FOUREL
Total hours : 24
Coefficient : 1
METHODOLOGY OF RESEARCH AND MASTER’S THESIS  Choose either "Applied Master’s Thesis + Internship" (6 ECTS) or “Research Master’s Thesis” (6 ECTS)

View detail : Seminar on research methodology
Seminar on research methodology
Lecturer :
DELPHINE LAUTIER
Total hours : 23
Overview :
Methodology in Research and the writing of a Master's thesis
This course is an introduction to the methodology of reseach through the writing of the Master's thesis.
Coefficient : 4
Learning outcomes :
In this course, the students learn: i) How to define a research subject; ii) How to select, and use the articles related to their subject, iii) How to organize the content of their Master's thesis, and to write their review of the litterature
Assessment :
The grades depend on the assiduity of the student and his /her ability to produce in due time, five documents. Each document represents one step in the writing of the Master’s thesis.
Document n°1 reveals the preferences of the students about the research subjects in which they are especially interested
Document n°2 gives a definition of the subject, in accordance with the supervisor of the Master's thesis
Document n°3 is a synthesis about the main references that will be needed to write the Master's thesis
Document n°4 is a first draft of the review of the litterature
Document n°5 is the Master's thesis

View detail : Applied Master's thesis
Applied Master's thesis
Ects : 3
Coefficient : 4

View detail : Internship
Internship
Ects : 3
Coefficient : Validation (pas de note)

View detail : Research Master's thesis
Research Master's thesis
Ects : 6
Coefficient : 4

View detail : Frontiers in Finance
Frontiers in Finance
Total hours : 15
PROFESSIONNAL TRAINING

View detail : Formation Alumnye
Formation Alumnye
Total hours : 6

View detail : AMF Certification (On line course)
AMF Certification (On line course)
Academic Training Year 2022  2023  subject to modification
LEVELLING COURSES  2 courses

View detail : Financial Econometrics I
Financial Econometrics I
Lecturer :
GAELLE LE FOL
Total hours : 24
Overview :
This course is an introduction and/or refresher course in Econometrics that focuses on techniques for estimating regression models, on problems commonly encountered in estimating such models, and on interpreting the estimates. The goal is to provide participants with the basic skills and knowledge necessary to undertake empirical research and to prepare them to the advanced course in Econometrics of Financial Markets. If Gretl will be the econometric software used in the course, it is possible to use R.
Course outline
 How to build an econometric model and how to use it?
 The (simple and multiple) linear regression model
 Inference, hypothesis testing and prediction
 Specification and diagnostic testing (heteroskedasticity, autocorrelation, model specification)
 Selection criteria
 Alternative to OLS (2SLS, ML, GLS, Quantile regression)
Coefficient : 3 ECTS / Coefficient 1.5 ==> M1 Financial Markets
Recommended prerequisites :
First course in programming
Require prerequisites :
Mathematics and Statistics (bachelor level)
Learning outcomes :
Theoretical and practical knowledge of linear regression models estimation technics. Being able to set up an econometric analysis.
Bibliographyrecommended reading
 Adkins L. C., learneconometrics.com/gretl/using_gretl_for_POE5.pdfUsing gretl for Principles of Econometrics, Version 1.041, August 2018, Free copy;
 Brooks C., Introductory Econometrics for Finance, Second Edition, Cambridge University Press, 2014 ;
 Gelman A., J. Hill and A. Vehtari, 2021, Regression and Other Stories, 1st Edition, Cambridge University Press, 2021;
 Gujarati D., Basic Econometrics, McGraw Hill Higher Education; 5th Revised edition edition, 2009 ;
 Hill C., W. Griffiths and G. Lim, Principles of Econometrics, Wiley, 5th Edition, 2018 ;

View detail : Monte Carlo Simulations in finance  MathLab
Monte Carlo Simulations in finance  MathLab
Lecturer :
IRINA KORTCHEMSKI
Total hours : 21
Overview :
Lecture 1 and 2: Introduction to MATLAB. Tutorial with numerical optimization of Rosenbrock’s function and simulation of the Brownien Motion. Markowitz portfolio optimization.
Lecture 3: Binomial options pricing model. European, American, Butterfly and Barrier Knock  Out options. Simulation of a Binomial tree and assets trajectories.
Lecture 4: Black and Scholes Model. MonteCarlo method for option valuation. European option. Correlated Brownian motions. Basket et Exchange options.
Lecture 5: Black and Scholes Model. Strongly Pathdependent options. Asian option. Lookback and Choosers. Stochastic volatility models. EulerMaruyama approximation of Stochastic Differential Equations. Option and asset pricing in the Heston model.
Lecture 6 and 7: Merton Model. Poisson distribution. Simulation of assets trajectories with jumps. Option pricing in the Merton model.
Coefficient : 1
Recommended prerequisites :
The notions of stochastic calculus, Black and Scholes models, Ito's formula.
Learning outcomes :
The students will learn important principles of implementation of financial models and master algorithms of evaluation of different types of derivative securities: European, American, standard, barrier and path dependent options on stocks.This course gives a comprehensive introduction to Monte Carlo and finite difference methods for pricing financial derivatives. At the end of the course, the student should have a thorough understanding of the theory behind Monte Carlo methods, be able to implement them for a range of applications, and have an appreciation of some of the current research areas.
Assessment :
Control of Knowledge: Defense of a Project.
Bibliographyrecommended reading
Reading List:
1) S E Shreve, Stochastic Calculus for Finance II: ContinuousTime Models, Springer 2004.
2) P Glasserman, Monte Carlo Methods in Financial Engineering, SpringerVerlag, 2004.
3) P Wilmott, S D Howison and J Dewynne, Mathematics of Financial Derivatives, CUP, 1995.

View detail : Python for finance
Python for finance
Lecturer :
HOUCINE SENOUSSI
Total hours : 18
Overview :
Our aim in this course is to implement some key concepts in quantitative finance using popular Python packages such as :
 NumPy : the fundamental package for scientific computing.
 Matplotlib : the main 2D plotting library.
 SciPy : another scientific computing library containing toolboxes dedicated to optimization, statistics and many other fields.
 Pandas : A library offering powerful data structures and tools for data analysis.
The data we will use is extracted from free online sources (Google, Yahoo, ...). The main parts of the course are the following :
1. Python basics : Data types, data structures, programs structure and packages.
2. Numpy, Matplotlib : discovering these packages with application to Monte Carlo simulation (look at the potential evolution of asset prices over time/Random walk).
3. Scipy : Introduction and application to a regression analysis of stock prices.
4. Pandas and Matplotlib. Introduction and Application (I) : importing, visualizing and analysing Time series financial data.
5. Pandas and Matplotlib. Advanced aspects and application (II) : Volatility calculation, Algorithmic trading, Creating, testing and improving a trading strategy.
Coefficient : 1
Require prerequisites :
Basics of algorithmics.
Learning outcomes :
Mastering the structure of the Python language, a good knowledge of the most important libraries for financial applications (Numpy, Matlplotlib, Scipy, Pandas).
Assessment :
Several programming assignments (one for each class).
MANDATORY FUNDAMENTAL COURSES  4 courses for 24 ECTS

View detail : Finance in continuous time
Finance in continuous time
Ects : 6
Lecturer :
RENE AID
Total hours : 30
Overview :
Asset pricing, contingent claim, stochastic process,
brownian motion, Itô's formula, optimal stopping time.
This course is an introduction to "Derivative pricing and stochastic calculus II".
It introduces the standard concepts and tools allowing to
understand arbitrage theory in continuoustime. The requirements from
probability theory are made as basic as possible to make the lectures
accessible to studends without a strong background in applied
mathematics.
Coefficient : 1 (Master Finance)<br /> 3ECTS  Coefficient 1 (M2 Quantitative Economics)
Learning outcomes :
In the end of this course, the students must be comfortable with:
i) Basic concepts of contingent claims,
ii) the binomial model;
iii) stochastic integrals and Itôs calculus;
iv) the Black and Scholes model,
v) Merton's optimal porfolio problem.
Bibliographyrecommended reading
Steven Shreve, Stochastic Calculus for Finance I: The Binomial Asset Pricing Model, 2005.
Steven Shreve, Stochastic Calculus for Finance II: ContinuousTime Models , 2005.

View detail : Corporate finance
Corporate finance
Ects : 6
Lecturer :
EDITH GINGLINGER
LUC RENNEBOOG
Total hours : 30
Overview :
Corporate finance addresses the questions of how companies raise financing and structure their debt. The major areas covered in this course are:
 Primary markets: why do companies go public, and why are initial public offerings (IPOs) generally underpriced?
 Capital structure: how do companies choose between different types of securities, particularly debt and equity? Why do firms hold cash?
 Security issuance, seasoned equity offerings, convertible bond offerings
 Internal capital markets and restructuring
 Corporate payout policy: How much do companies pay out to investors as dividends?
 Corporate social responsability
Coefficient : 1
Recommended prerequisites :
This course requires that the students have validated the course "Fundamentals of corporate finance".
Learning outcomes :
In this course we will examine some of the most important empirical contributions to corporate finance in the areas of initial public offerings, capital structure, security issuance, internal capital market, dividend policy and corporate social responsibility. The objective is to prepare students to critically evaluate and conduct research in corporate finance.
Assessment :
Presentations of research papers: 40%
Deliverables (exercises, cases): 20%
Final Exam: 40%
Bibliographyrecommended reading
Constantinides George M., Milton Harris, Rene M. Stulz, 2013, Handbook of the Economics of Finance: Corporate Finance, NorthHolland
Eckbo Espen, 2008, Handbook of corporate finance, Empirical corporate finance,vol. 1 et 2 North Holland
Tirole J., 2006, The theory of corporate finance, Princeton University Press

View detail : Asset pricing theory
Asset pricing theory
Ects : 6
Lecturer :
JEROME DUGAST
Total hours : 30
Overview :
In this course, we will discuss a wide range of topics ranging from optimal portfolio, the CAPM, factor models, consumptionbased asset pricing, and arbitrage pricing, to more special ones including asymmetric information, and limits to arbitrage.
 Optimal Portfolio Theory and the CAPM
 Factor Models
 Decision Making under Uncertainty
 Consumptionbased Asset Pricing
 Arbitrage Pricing
 Dynamic Asset Pricing
 Asymmetric Information and Asset Prices
 Limits to Arbitrage
Coefficient : 1 (M1 Finance)<br /> 3ECTS  Coefficient 2 (M2 Quantitative Economics)
Learning outcomes :
Master the theoretical concepts of asset pricing
Assessment :
Evaluation: assignments 30%, final exam 70%

View detail : Term structures : theory, models and empirical tests
Term structures : theory, models and empirical tests
Ects : 6
Lecturer :
DELPHINE LAUTIER
Total hours : 30
Overview :
At the end of this course, the students must have a broad knowledge about the term structures of derivative prices: the theories, the valuation methods, the econometric techniques, the empirical tests as well as the applications.
Coefficient : 1
Recommended prerequisites :
Students who choose this course must also attend the course “Finance in continuous time”
Learning outcomes :
This course in a introduction to research on term structures. While being centered on the case of commodities, it also proposes some comparisons with interest rates, and some generalization to other assets like equities and foreign exchange. The course presents the theories of the term structures, their empirical implications, the methodological issues associated with empirical tests, and empirical tests.
Assessment :
Participation, 20% + Final exam, 80%
Bibliographyrecommended reading
Main references:
 Danthine J.P., Donaldson J.B., Intermediate Financial Theory, 2d Ed., Elsevier, 2005.
 Hull J., Options, futures and other derivatives, 9th Ed.
 Kolb R.W. , Overdahl J.A. , Futures, options, and swaps, 5th Ed., Blackwell, 2007.
 Williams J., The economic function of futures markets, Cambridge University Press, 1986
 Wilmott P., Paul Wilmott on Quantitative Finance, 3volume set, 2nd Ed., Wiley, 2006.
OPTIONNAL FUNDAMENTAL COURSE  1 course for 6 ECTS

View detail : Derivative Pricing and Stochastic calculus
Derivative Pricing and Stochastic calculus
Ects : 6
Lecturer :
PAUL GASSIAT
Total hours : 24
Overview :
Advanced derivative pricing and stochastic calculus.
This course requires that the students have validated the course "Finance in continuous time". Its gives more insights into the theory of derivative asset pricing as well as the main models and techniques used in practice.
Coefficient : Coefficient 1 (M2 Research in Finance)<br /> Coefficient 3 (M2 Financials Markets)
Require prerequisites :
Basic probability theory, stochastic processes (martingales,...), stochastic calculus in continuous time (Brownian motion, Itô formula, Stochastic differential equations)
Learning outcomes :
The lecture starts with discrete time models which can be viewed as a proxy for continuous settings, and for which we present in detail the theory of arbitrage pricing. We then develop on the theory of continuous time models. We start with a general Itôtype framework and then specialize to different situations: Markovian models, local and stochastic volatility models. For each of them, we discuss the valuation and the hedging of different types of options : plain Vanilla and barrier options, American options, options on realized variance, etc. Finally, we present several specific volatility models (Heston, CEV, SABR,...) and discuss their specificities.
Bibliographyrecommended reading
Bouchard B. et Chassagneux J.F., Fundamentals and advanced Techniques in derivatives hedging, Springer, 2016.
Lamberton D. et B. Lapeyre, Introduction au calcul stochastique appliqué à la finance, Ellipses, Paris, 1999.

View detail : Game theory
Game theory
Ects : 6
Lecturer :
MARION OURY
Total hours : 36
Overview :
Chapter 1: Normal form games: pure and mixed strategy Nash equilibrium; weakly/strictly dominated strategies , iterated elimination of dominated strategies.
Chapter 2: Dynamic games: Backward induction, subgame perfect Nash equilibrium, repeated games.
Chapter 3: Incomplete information (in static games): Bayesian Nash equilibrium; introduction to some applications (auctions, finance...)
Coefficient : 1
Require prerequisites :
The student must be at ease with some basic mathematical notions such as: derivations, firstorder conditions...
Learning outcomes :
The objective of the course is to give some fundamental background in interactive decision making and its applications. After having attended the classes, the students will be able to understand the basic tools of game theory and the importance of this field in economics and finance.
Assessment :
A midterm exam and a final exam

View detail : Information economics
Information economics
Ects : 6
Lecturer :
FRANCOISE FORGES
Total hours : 36
Overview :
The course starts with models of economic interaction under asymmetric information (typically, dynamic games with incomplete information). Signaling games are studied as a first illustration, with economic applications (e.g., Spence's signaling model). The course goes on with auctions: private values, revenue equivalence theorem, common values (winner’s curse), etc. and moves from optimal auction mechanisms to the general topic of mechanism design (general framework, revelation principle, optimal mechanisms in various economic frameworks). Contract theory is studied in the light of mechanism design. The next themes are efficient (e.g., VickreyClarkeGroves) mechanisms and implementation. The study of multiagent, multiprincipal problems is possibly pushed further.
Coefficient : 2
Require prerequisites :
A basic course in game theory (L3 or M1) and in microeconomics (L3 or M1).
Learning outcomes :
After attending the classes, the students will be able to model and analyze problems of resource allocation among "asymmetric" agents, who differ from each other regarding (i) their information on the basic economic situation at hand and (ii) their commitment power.
Bibliographyrecommended reading
Jehle, G. and P. Reny (2011),
Advanced Microeconomics, Pearson.
MasColell, A., M. Whinston, and J. Green (1995),
Microeconomic Theory, Oxford University Press.
SEMINARS  2 courses for 6 ECTS

View detail : Regulation and financial Markets
Regulation and financial Markets
Ects : 3
Lecturer :
THIBAULT GODBILLON
Total hours : 21
Overview :
Financial regulation, prudential regulation around the world, regulation to "too big to fail" banks and fragmented environment
To give the students an overview of recent and future regulatory prudential and market reforms, at the global level and across regions.
Coefficient : Coefficient 1 (M2 Research in Finance)<br /> Coefficient 1.5 (M2 Financial Markets)
Learning outcomes :
Master the regulatory prudential and market reforms, at the global level and across regions

View detail : Structured products in practice
Structured products in practice
Ects : 3
Lecturer :
AYMERIC KALIFE
Total hours : 21
Overview :
Structured products, evaluation and control.
This course is an initiation to new structured products. It shows how to value such products, and how to control the associated risks
Coefficient : 1
Learning outcomes :
Participants will lear how financial institutions can build and structured products, how they value them, and what they are done for.

View detail : Machine Learning in Finance
Machine Learning in Finance
Ects : 3
Lecturer :
PIERRE BRUGIERE
Total hours : 21
Overview :
Methods of Statistical Learning, applied to some financial problems of credit rating, anomaly detection and yield curve approximations
Coefficient : 1
Recommended prerequisites :
Basic linear algebra and differential calculus.
Require prerequisites :
Basic linear algebra and differential calculus.
Learning outcomes :
Vapnik Chervonenkis dimension, PAC learning, calibration versus prediction, SVM (Support Vector Machines) classifiers, Mercer's theorem, CSVMs, muSVMs and single class SVMs. Basics of decision trees, random forests and penalized regressions.
Assessment :
Exam
Bibliographyrecommended reading
Trevor Hastie, Robert Tibshirani, Jerome Friedman: The Elements of Statistical Learning, Springer
Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani : An Introduction to Statistical Learning, Springer
Christopher Bishop: Pattern Recognition and Machine Learning, Springer

View detail : Microeconomics for finance
Microeconomics for finance
Ects : 3
Lecturer :
JEROME DUGAST
Total hours : 24
Overview :
Syllabus:
1. Equilibrium in an Exchange Economy
2. Decision Making under Uncertainty
3. Equilibrium in Markets for Securities
4. Investment Decision under Market Imperfections: the PrincipalAgent Problem
Coefficient : 0.5 (M1 finance)<br /> 1 (M2 Finance)
Recommended prerequisites :
Basic notions of mathematical analysis and algebra are required.
Learning outcomes :
This 24 hours course aims at acquainting students with relevant microeconomics methods to tackle finance issues.
Assessment :
Final exam and assignment
MANDATORY SEMINARS  6 courses for 18 ECTS

View detail : Advanced corporate finance
Advanced corporate finance
Ects : 3
Lecturer :
GILLES CHEMLA
Total hours : 21
Overview :
Recent developments in the theory of corporate finance
To follow this course, the students must validate the course "Corporate finance". Course objective : the main objective of the course is to familiarize students with a number of important, recent results and subjects that have been added to the theory of corporate finance. A second important objective is to provide an overview of some of the modelling issues faced and of the methods that are currently employed in the area of corporate finance
Coefficient : 1
Learning outcomes :
The students will master the most recent reserach issues in corporate finance, with specific insights into modelling.

View detail : Advanced empirical corporate finance
Advanced empirical corporate finance
Ects : 3
Lecturer :
RYAN WILLIAMS
Total hours : 24
Coefficient : 1

View detail : Empirical Asset Pricing
Empirical Asset Pricing
Ects : 3
Total hours : 21
Coefficient : 1

View detail : Microstructure of financial markets
Microstructure of financial markets
Ects : 3
Lecturer :
JEROME DUGAST
Total hours : 21
Overview :
The field of market microstructure combines theoretical modeling, institutional knowledge, and empirical analysis to understand how prices result from the interactions of traders in financial markets. The course aims to acquaint students with (i) the canonical models in microstructure, and (ii) econometric models used to test the predictions of those models.
Course structure:
 Trading Mechanisms
 Measuring Liquidity
 Price Dynamics and Liquidity
 Trade Size and Market Depth
 Empirical Analysis
Coefficient : 1
Learning outcomes :
Master the concepts of financial markets microstructure
Assessment :
Evaluation: assignement 30%, final exam 70%
Bibliographyrecommended reading
Foucault, Thierry, Marco Pagano, and Ailsa Röell, Market Liquidity: Theory, Evidence, and Policy, Oxford University Press, 2013.

View detail : Advanced Market Microstructure
Advanced Market Microstructure
Ects : 3
Total hours : 21
Coefficient : 1

View detail : Time series
Time series
Ects : 3
Total hours : 21
Coefficient : 1
SEMINARS  2 courses for 6 ECTS

View detail : Machine Learning : empirical applications for finance
Machine Learning : empirical applications for finance
Ects : 3
Lecturer :
HOUCINE SENOUSSI
Total hours : 21
Overview :
Basics of ML
 Definitions, approaches and applications.
 Data mining (DM) : definitions and links with ML.
 Classification and regression problems.
 Building and evaluating an ML model.
 Presentation of the main approaches of ML/DM.
 Application I.
Decision Trees :
 Definitions and algorithms.
 Advanced methods based on DL : Bagging, Boostring and Random forests.
 Application II : Making a decision in finance.
Neural networks:
 Definitions.
 Learning in NN : grandient descent and Backpropagation.
 Advanced methods based on NN (Deep learning).
 Application III : : Stock pricing.
Reinforcement Learning :
 Definitions : Agents and environnments.
 Markovian Decision Process (MDP).
 Policies and optimal policies.
 Qlearning.
 Application IV : Trading.
Coefficient : 1
Require prerequisites :
Python programming language.
Learning outcomes :
Building Machine Learning (ML) models for Finance problems. Using ML Python library (and in particular sickitlearn).
Assessment :
Two/Three assignments (building a model + Python programming).

View detail : Data management (Certif finance digitale)
Data management (Certif finance digitale)
Ects : 3
Total hours : 21
Coefficient : 1

View detail : Behavioral finance
Behavioral finance
Ects : 3
Lecturer :
ALBERTO MANCONI
Total hours : 21
Overview :
Bevavioral finance, cognitive and social psychology in finance
Introduce students to this relatively new subdiscipline of finance which incorporates insights from cognitive and social psychology into finance. In the past 20 years behavioral finance has emerged as an important stream of thinking in finance.
Coefficient : Coefficient 1 (M2 Research in Finance)<br /> Coefficient 1.5 (M2 Financial Markets)
Learning outcomes :
Relaxing the traditional assumptions of finance models has proved a fruit ful way of understanding financial decisionmaking and anomalies found in empirical tests.

View detail : Financial macroeconomics
Financial macroeconomics
Ects : 3
Lecturer :
VALERE FOUREL
Total hours : 24
Coefficient : 1
METHODOLOGY OF RESEARCH AND MASTER’S THESIS (6 ECTS)

View detail : Seminar on research methodology
Seminar on research methodology
Lecturer :
DELPHINE LAUTIER
Total hours : 23
Overview :
Methodology in Research and the writing of a Master's thesis
This course is an introduction to the methodology of reseach through the writing of the Master's thesis.
Coefficient : 4
Learning outcomes :
In this course, the students learn: i) How to define a research subject; ii) How to select, and use the articles related to their subject, iii) How to organize the content of their Master's thesis, and to write their review of the litterature
Assessment :
The grades depend on the assiduity of the student and his /her ability to produce in due time, five documents. Each document represents one step in the writing of the Master’s thesis.
Document n°1 reveals the preferences of the students about the research subjects in which they are especially interested
Document n°2 gives a definition of the subject, in accordance with the supervisor of the Master's thesis
Document n°3 is a synthesis about the main references that will be needed to write the Master's thesis
Document n°4 is a first draft of the review of the litterature
Document n°5 is the Master's thesis

View detail : Frontiers in Finance
Frontiers in Finance
Total hours : 15

View detail : Research Master's thesis
Research Master's thesis
Ects : 6
Coefficient : 4
PROFESSIONNAL TRAINING

View detail : Formation Alumnye
Formation Alumnye
Total hours : 6

View detail : AMF Certification (On line course)
AMF Certification (On line course)
Academic Training Year 2022  2023  subject to modification
Teaching Modalities
The total teaching time is of 320 to 360 hours, from September to April. It is made of 36 hours of levelling courses in digital finance, corporate finance, financial econometrics and programming in Python and Mathlab.
The fundamental courses are taught mainly (but not only) during the first semester. They represent 150 hours, on corporate finance, mathematical finance, derivatives and asset pricing, management of fixed income, game theory, etc.
The specialization seminars are devoted to topics like advanced corporate finance, behavioral finance, alternative finance, machine learning, regulation, etc. They represent 105 hours.
In parallel with these courses, the student will have to write a Master’s thesis in contemporary finance, and organize his personal work time. There are two possible options: the first is to write a research Master’s thesis (this could be the first step towards a doctorate); the second is to write an applied Master’s thesis. In this case it is mandatory to make a 3months internship.
The program gives students the opportunity to study in English, during the second semester, in one of the following institutions: there is one place at the Bocconi University every two years. There are two places per year at Tilburg University in the MSC “Research in Finance”. Students interested in doing doctorate wil take priority for these places. There are two places at Lugano University (English, Msc in finances or modules at the doctorate level), and two places at HEC Lausanne. Where the number of students wishing to study abroad exceeds the number of places available, priority will be given to those students who have already spent at least on year at Dauphine. Besides, the M2 104 belong to the QTEM network, which give the possibility to study one year abroad.
Finally, the 104 offers, in the beginning of the year, some carreer coaching. It is also possible to obtain a certification by the French Autority of Financial Markets, and by the CFA.
Internships and Supervised Projects
The students will write a Master’s thesis during the year, which will be part of their professional skills. The aim of the Master’s thesis is to produce an original piece of research work on a clearly defined topic within the investigative field of contemporary finance, under the guidance of one of the Masters’ Professors. For students considering a doctoral thesis, the Master’s thesis enables them to get an initial feel for what research involves and is often the foundation for further investigation for a student’s doctorate. For others, the Master’s thesis is a unique opportunity to demonstrate their scientific expertise in the field of finance and experience shows that employers highly value this research approach.
For students who choose the applied research option, the M2 104 includes a 3 months (or more) internship which starts in April and is carried out in a financial institution or in the finance department of a firm. Firms which regularly employ interns include: Société Générale, BNP Paribas, CACIB, Dexia Securities, Rothschild, Natixis, Murex, HSBC, Axa, Groupama, Renault, SFR....
The master receives a wealth of internship offers from companies and past Master’s students which helps students find the right internship every year. Past students pursue careers in Risk Management, Asset Management, Financial Research, theGeneral Inspectorate of Finance, Quantitative Research, Capital Market Trading, Trading, Consulting and Academia (French and foreign universities, Frenchgrandes écoles)
Researchdriven Programs
Training courses are developed in close collaboration with Dauphine's worldclass research programs, which ensure high standards and innovation.
Research is organized around 6 disciplines all centered on the sciences of organizations and decision making.
Learn more about research at Dauphine