M2 Quantitative Finance

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  • Places available
    30
  • Language(s) of instruction
    English
    French
Présentation
Objectives

The global financial crisis of 2008-09 led to a simplification of financial derivatives, along with an increasing weight of the regulation (FRTB, MiFID, interest rate reform, Solvency II on the insurance side,...). Data and their analysis are everyday more at the core of all systems. This poses unprecedented computational challenges, which can only be addressed by combining the resources of distributed, cloud, and GPU computing. Finally, today's quantitative finance is every day more diverse: investment banking, but also buy side (hedge funds), finance of insurance, fintech, etc.
In line with these evolutions, M2QF brings to high level scientific students an invaluable expertise in the field of quantitative finance, considered from the double point of view of mathematics (probability and statistics, computational methods) and data science. Job opportunities after the master program: quantitative analyst, risk manager, IT quant, insurance, data scientist for finance, PhD thesis in quantitative finance,...

Location
EVRY
Course Prerequisites

Good M1-level (or equivalent) competence in probability and statistics, market finance, programming (in C, Python)

Skills
  • Be able to mathematically formalise a quantitative problem arising in the field of market finance.

  • Understand the conditions of validity for a mathematical result, the conditions of application for a model, the domain of validity for a statistical learner.

  • Perform mathematical calculations within the framework of a market finance model.

  • Computationally implement a mathematical market finance model.

  • Set up and interpret a statistical learning strategy.

  • Be able to operate in an English-speaking work environment.

Career prospects

-  financial engineer
- quantitative analyst
- risk manager
- IT-quant
- financial consultant
- insurance finance
- data scientists in finance
- thesis in quantitative finance

Collaboration(s)
Laboratories

Laboratoire de Mathématiques et Modélisation d'Evry.

Programme

Pour obtenir les 18 ECTS à choix du premier semestre, les étudiants doivent choisir 3UEs à 6. Ils peuvent en choisir une quatrième qui apparaîtra sur un supplément au diplôme.

Subjects ECTS Lecture directed study practical class Lecture/directed study Lecture/practical class directed study/practical class distance-learning course Project Supervised studies
Projet informatique 3 2 10
Méthodes numériques de pricing et calibration de modèles 6 42
Anglais financier 3 25
Subjects ECTS Lecture directed study practical class Lecture/directed study Lecture/practical class directed study/practical class distance-learning course Project Supervised studies
Programmation informatique 6 18 24
Modélisation de la courbe des taux (3ECTS) ET/OU Deep learning (3ECTS) ET/OU Econométrie financière (3ECTS) 6 54 21
Machine learning 6 21 21
Gestion des risques 6 36 24
Finance de l'assurance ET Marchés financiers et finance actuarielle 6 69 3
Calcul Stochastique 6 21 21

Pour obtenir les 16 ECTS à choix du second semestre, les étudiants doivent choisir 4UEs à 4. Ils peuvent en choisir une cinquième qui apparaîtra sur un supplément au diplôme.

Subjects ECTS Lecture directed study practical class Lecture/directed study Lecture/practical class directed study/practical class distance-learning course Project Supervised studies
XVAs, FRTB, et analyse regulatory quant 4 18 16
Techniques de machine learning pour le pricing d'options, la calibration de modèles et la couverture (2ECTS) ET/OU Données Haute Fréquence et carnets d'ordre (2ECTS) ET/OU Gestion d'actifs avancée 4 57 17
Produits dérivés 4 44 6
Cutting edge finance 4 64
Contrôle stochastique ET Modélisation finance d'entreprise et de l'assurance 4 42
Analyse stochastique 4 42
Subjects ECTS Lecture directed study practical class Lecture/directed study Lecture/practical class directed study/practical class distance-learning course Project Supervised studies
Stage professionnel 12
Préparation au TOEIC 2 25
Modalités de candidatures
Application period
From 01/02/2021 to 30/07/2021
Compulsory supporting documents
  • Motivation letter.

  • All transcripts of the years / semesters validated since the high school diploma at the date of application.

  • Curriculum Vitae.

Additional supporting documents
  • VAP file (obligatory for all persons requesting a valuation of the assets to enter the diploma).

Contact(s)
Course manager(s)
Vathana LY VATH - vathana.lyvath@ensiie.fr
Administrative office
Adelia Soarez Da Costa - adelia.soaresdacosta@univ-evry.fr
Admission