In-depth project – S1


Objective

Students in the M2 "Statistics, Finance, Actuarial Science" and the Actuarial Science and Finance & Risk Management tracks of the ENSAE engineering cycle have the opportunity to participate in a prospective work, in groups of 3 or 4 and under the supervision of a supervisor, financial industry professional or researcher in finance/insurance. The supervisor will meet the students once or twice a month to coordinate and guide their work, in person or by videoconference. He or she will also provide the students with the data necessary to conduct the project.

In-depth projects focus on a well-identified topic in quantitative finance or risk management, and have a real interest in the company. For example, it can be an exploratory research around a new model, the analysis of a specific database or the study and development of a new management strategy. Projects will include typically a bibliographic research stage, a data analysis stage and a computer implementation stage at based on algorithms. Although the results of the project may be interesting and useful for the company, it is by no means a consulting mission.
The objective is to be prospective, to test innovative ideas.

The publication of an article, the result of such work, could be considered in some cases.

An intermediary report will be issued at the end of January, a final report in May and an oral presentation. This is a fully-fledged course, which will allow the validation of 3 ECTS in the first semester and 3 ECTS in the second semester.

Examples of topics proposed in 2020-2021 :

  1. Shareholder engagement (CREST)
  2. Integration des vues des investisseurs dans le processus de gestion de portefeuille (BFT Investment Managers)
  3. Historical simulation for Equity index Futures and Options (Zeliade Systems)
  4. Modélisation multidimensionnelle des prix sur les marchés intraday de l’électricité (EDF Lab)
  5. Trading VIX futures (Marker Cipher)
  6. Optimal execution of trading strategies (Varenne Capital Partners)
  7. Deep reinforcement learning for trading (Andurand capital)
  8. Assurance Cyber : estimation de lois jointes et prédiction multi-dimensionnelle (Sorbonne Unviersité)
  9. Amélioration de la méthodologie de pricing relative au péril « feu de forêt »(Axa)
  10. Modélisation par les processus de Hawkes des sinistres individuels en prenant en compte les délais de déclaration (Milliman / Axa)
  11. Finance verte et stress tests climatiques (Mazars)
  12. Simulation des stress tests pour un portefeuille des actifs corrélés de grande dimension (Natixis)

Planning

The indicative timetable for the implementation of the projects is as follows:
– Beginning of October: dissemination of the topics to the students, group building
– End of January: submission of the mid-term report
– Early May: submission of the final report
– Before the end of May: defense

This training aims in particular to give students the following skills:
– Bibliographical research
– Analysis of financial data
– Formulation and implementation of a mathematical model based on an business issues
– Methodology, specificity and requirements of industrial/academic research

Références