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Operating from our 19 offices, 3 000 Murexians from over 60 different nationalities ensure the development, implementation and support of our platform which is used by banks, asset managers, corporations and utilities, across the world.
Join Murex and work on the challenges of an industry at the forefront of innovation and thrive in a people-centric environment.
You'll be part of one global team where you can learn fast and stay true to yourself.
Équipe :
L'équipe «MACS» est responsable de l'implémentation des méthodes d'évaluation innovantes et efficientes cross-assets (Action, Change, Commodities , Crédit et Taux). Cela comprendd'une part la compréhension des modèles standards et la conception éventuelle de nouvelles modélisations adaptées aux besoins du marché et d'autre part l'implémentation et la maintenance de solutions (librairie quantitative, service) permettant la calibration des modèles et l'évaluation et le calcul des mesures de risque (sensibilités, VaR , PFE, XVA) des différents produits financiers. Une attention toute particulière est portée sur la précision des différentes méthodes implémentées ainsi que sur l'optimisation des temps de calcul, ce qui nécessite d'adapter les solutions aux technologies les plus innovantes (GPU par exemple).
Missions:
Suite à la transition du LIBOR, le taux de référence est maintenant déterminé par la composition d'un taux sans risque (Risk Free Rate - RFR). Par conséquent, le Libor Market Model (LMM) a été étendu au Forward Market Model (FMM) pour prendre en compte ce changement de référence.
Les formulations les plus simples de cette classe de modèle ne permettent pas de reproduite les cotations de marché des volatilités des swaptions et caplets dans leur s trois dimensions: tenor , maturité et strike (le cube des volatilités de swaptions ). Pour répondre à cette exigence, il faut considérer les extensions du modèle initial avec une volatilité locale (éventuellement paramétrique ) et/ou une volatilité stochastique. Le but de ce stage est d'étudier le modèle SABR-FMM (extension du SABR-LMM qui comprend une composante de volatilité locale et stochastique de type SABR ).
Dans un premier temps , vous implémenterez une méthode de Monte Carlo de ce modèle et analyser ez les différents impacts des paramètres du modèle SABR sur la surface de volatilité des swaptions ainsi que les limites potentielle d'une telle diffusion . Dans un deuxième temps, vous développerez et implémenterez des formules approximatives des produits de base dans ce modèle et comparerez les résultats de la méthode de Monte Carlo. Vous étudierez ensuite l'impact de ce modèle sur les produits de corrélation tels que les CMS Spread options et fort des différentes études vous élaborerez un protocole de calibrage de ce modèle. Enfin vous comparer ez ce modèle et le modèle FMM avec volatilité locale.
Profil:
- Étudiant en dernière année d'École d'Ingénieurs/Informatique ou en Master universitaire, vous avez de solides connaissances des marchés de capitaux et des modélisations et méthodes associées.
- Vous avez de bonnes connaissances en Python / C++.
- Dynamique, rigoureux, et autonome, vous êtes capable de travailler dans un environnement agile.
- En intégrant l'équipe MACS (Murex AnalytiCS ), vous travaillerez sur un sujet stimulant et innovant.
- Vous évoluerez dans une équipe motivée et engagée cross-assets et cross-technologie
- Vous aurez à disposition si besoin les dernières technologies (GPU par exemple).
- Vous évoluerez dans un environnement agile, international, multiculturel et en croissance.
Disponibilité immédiate pour une durée de 6 mois
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