ENSAE Paris - École d'ingénieurs pour l'économie, la data science, la finance et l'actuariat

Finance, Risks and Data

Finance, Risks and Data


The third year of the ENSAE Paris engineering program includes six specialization tracks. Each track has been designed to offer a coherent sequence of courses (advanced theoretical courses, applications, projects, seminars, etc.), preparing students for a major field of work and giving them a broad and in-depth vision of that field. Each of the professions practiced by ENSAEs will call to varying degrees on statistical methods, data science and modeling, which are therefore present within each track.

Learn more about admissions to the engineering program

The "Finance and Risk Management" track is designed to train financial market specialists and, more generally, banking and asset management professionals.

Professors in charge of the track

Jean-Michel Zakoïan

Peter Tankov


This specialization is intended for students with a solid background in probability and statistics, who are motivated by the applications of random modeling in the world of finance and who are skilled with computer tools (algorithms/coding).

The strength of the ENSAE Paris program lies in the balance between applied mathematics (based on probability, process theory and associated numerical methods) and economics, econometrics and statistics. Theoretical courses alternate with applied finance courses, and are complemented by professional seminars.

The world of finance is now at a crossroads. In a world of persistently low interest rates and reduced margins, most financial institutions must find new growth drivers and seek to become true "financial industries": rationalizing and optimizing tools and models (artificial intelligence), managing risks in an increasingly fine-tuned way, supporting energy and ecological transitions, etc. Because of their multiple skills in mathematical finance, statistics/machine learning, economics and modeling in the broadest sense, ENSAEs are particularly well positioned to take an active part in this transformation. Their versatility allows them to occupy multiple technical positions and to evolve within large and prestigious financial institutions. 

Financial markets are a universe fueled by overabundant data, which must be mastered and exploited to gain comparative advantages. The application of Data Science to the resolution of problems in the world of finance (price prediction, optimal execution, portfolio optimisation, hedging strategies, risk calculations, etc.) has become a major area of innovation. This specialization track will focus on recent developments in this field, both from a practical and theoretical point of view. 

One of the major developments of the last decade has been the financial sector's recognition of environmental issues. Green finance seeks to develop instruments for directing financing towards environmental transition, and to design tools for taking climate risks into account. The wave of green finance is transforming traditional finance professions such as risk management and portfolio management, and creating new ones such as ESG analysts. 

The development of green finance is based on extra-financial ratings, which seek to assess the environmental performance of companies using different sources of data. Quantitative techniques in green finance include data analysis, scenario analysis, quantitative portfolio management and climate-sensitive economic models.

The demand for experts in green finance is growing all the time, and ENSAE students, with their dual skills in economics and data science, are perfectly positioned to meet it. The Finance, Risks and Data track offers a coherent set of courses enabling students to train in this crucial field: the "Green Finance: core notions" course introduces key concepts; the "Green Finance: risk and portfolio management" course presents methods for managing climate risks and managing portfolios under environmental constraints; and the "Energy risk management" course focuses on the specific case of the energy sector, which plays a key role in the environmental transition. Green finance topics are also regularly offered as part of the "in-depth project" module.


At this level of competence, this training is unique. It provides students with a wide range of career opportunities. The jobs most likely to be occupied upon graduation are: market operators (traders, structurers), financial engineers ("quants"), portfolio managers, risk managers for market or credit risks, etc. The technical background provided by the program will give the necessary weapons to move on to positions of great responsibility or to other environments, for example in finance management.

All market finance professions are characterized by their high technical level. At the end of the program, young graduates will be able to handle the complex valuation/hedging tools of sophisticated derivatives, to construct and quantify the risks of a financial portfolio, and to propose tools and measures for relevant management of the activity.

Young ENSAE students will be equipped to find their place in the various families of financial institutions (banks, brokers, management companies, hedge funds, pension funds), insurance companies, national or international regulators, consulting or software companies, rating agencies, etc. A large proportion of careers are international (at least in part), particularly in the major financial centers of London, New York, Tokyo and Singapore.

Depending on their own aspirations, students can follow a curriculum more oriented towards the core professions of trading rooms (quant/structurer/trader) by choosing a "Market Finance" course or a more multidisciplinary training adapted to the professions of risk manager/portfolio manager/risk modeller by opting for a "Risk Management and Regulation" course. A wide choice of optional courses allows students to strengthen their specialization in one field or, on the contrary, to acquire a very versatile training.

Finally, the training offered by ENSAE Paris can be completed by one of the co-accredited research Masters in mathematical finance: M2 Statistics, Finance and Actuarial Science (Institut Polytechnique de Paris) or M2 Random Modeling (M2MO, Université Paris-Diderot). The programs of these two master's degrees are designed to avoid a double load for these ENSAE Paris students in a dual-course program. They allow students to move towards academic research in many fields: mathematical finance, econometrics of finance, numerical probabilities, stochastic control, etc.


The compulsory scientific courses and the optional courses recommended for the track are described below. Each semester, you can choose one modern language (maximum). English is compulsory if your level is lower than B2. The options can be mixed between the different tracks (under time constraints) to create hybrid profiles. It is then recommended to discuss their coherence, as well as the articulation of the choice of courses with a possible M2 followed in parallel, with the director of masters or/and the person in charge of the tracks.

First semester

Course ECTS Hours (course+tutorials)
Foundations of Risk management 3 18+0
Pricing and hedging of financial derivatives 4 24+9
Plus either :
Stochastic Calculus (ENSTA) or  3 13,5+7,5
Financial econometrics 4 18+6

You can choose from 3 to 6 options (including foreign language) from the entire 3A course catalog, semester 1 (subject to compatibility with your schedule), to reach a total of 30 to 31 ECTS over the semester. We recommend the following courses in this track:

Course ECTS Hours (course+tutorials)
Advanced Machine Learning 4 21+9
Applied statistical learning 4 15+9
Financial analysis and firm strategy 2 12+0
Blockchain: Bitcoin and Smart-Contracts 3 18+0
Copulas and financial applications 3 18+0
Duration Models 3 12+0
Dynamic models with latent variables 3 18+0
Ethics and responsibility in data science 2 12+0
Green finance: risk and portfolio management 3 18+0
Green finance: core notions 4 24+0
Machine-learning for Portfolio Management and Trading 2 12+0
Numerical methods for PDE in finance (ENSTA) 3 18+0
Modeling and managing energy risks 2 12+0
Project in finance and assurance – S1 3  
Risk measures 2 12+0
Stochastic Calculus (ENSTA) 3 13,5+7,5

Second semester

You can choose 3 to 6 options (including foreign language) from the entire 3A course catalog, semester 1 (subject to compatibility with your schedule), to reach a total of 30 to 31 ECTS over the semester. We recommend the following courses in this course:

Course ECTS Hours (coursr+tutorials)
Algorithmic Trading 3 15+0
Credit risk 3 18+0
General concepts in law and introduction to banking law 2 12+0
Econometrics of Commodity and Asset Pricing 3 18+0
Extreme-value theory 2 18+0
GARCH and stochastic volatility models 3 18+0
Machine learning in finance: Theoretical foundations 3 21+0
Levy process (ENSTA) 3 15+0
GPU program 2 12+0
Research project in finance and insurance - 2nd semester 3 0+0
Financial regulation (ENSTA) 2 18+0
Credit Risks: practical approach (ENSTA) 2 18+0
Pricing and hedging derivatives with multiple yield curves (ENSTA) 3 18+0
Portfolio Management 4 24+0
Numerical Methods in Financial Engineering 3 20+4