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.
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The "Finance and Risk Management" track is designed to train financial market specialists and, more generally, banking and asset management professionals.
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.
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.
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:
|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|
|Dynamic models with latent variables||3||18+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|
|Stochastic Calculus (ENSTA)||3||13,5+7,5|
|Ethics and responsibility in data science||2||12+0|
|Machine-learning for Portfolio Management and Trading||2||12+0|
|Numerical Methods in Financial Engineering ou Machine learning in finance: Theoretical foundations||3 or 3||20+4 or 21+0|
|Models of the interest rate curve||3||21+0|
|Seminar in finance|
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:
|General concepts in law and introduction to banking law||2||12+0|
|Econometrics of Commodity and Asset Pricing||3||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|
|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|
|Numerical Methods in Financial Engineering||3||20+4|
- The end-of-study internship: 10 ECTS credits for the 2nd semester of the 3rd year