Stage 3A - Quant - Extension Of Forward Market Model With Stochastic Volatility

Murex is a global fintech leader in trading, risk management and processing solutions for capital markets. Operating from our 19 offices, 2700 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. The team The MACS (Murex AnalytiCS) modelling team is responsible for the implementation of efficient and innovative evaluation methods and the computation of risk measures (Sensitivity computations, VaR, PFE, XVA) for financial products (from vanillas to the most exotic ones). It is a cross assets team (Equities, FX, Rates, Commodities and Credit) which understands, implements (calibration and financial evaluation) and maintains standard modelizations as well as more innovative ones to fit market needs. MACS models can be used through a pricing library but are also exposed using a REST service. Particular care is put on producing accurate and reliable results in a timely manner, which leverages on modern technologies (ex: GPU computing) or adapted numerical methods. Your missions/What you'll do ? Following the LIBOR Transition, the reference rate is now a composition of a Risk-Free Rate (RFR). And consequently, the Libor Market Model (FMM) has been extended to the Forward market Model (FMM) to consider this new change of reference. Main difficulty of this class of model is to fit the swaption volatility market quote in the following three dimensions: maturity, tenor and strike (swaption volatility cube). That's why we need complex modelization with parametric local volatility and/or stochastic volatility. The aim of the internship is to study the extension of the SABR-LMM model into a SABR-FMM model. Firstly, we will focus to understand the different impacts on swaptions surface of the different model parameters of the SABR model. After we will compare the Monte Carlo evaluation with our internal FMM modelization. And finally, we will see the impact on Correlation products like CMS Spread Option and try to determine a calibration routine in order to fit the swaption volatility cube. Your profile/Who you are ? 3rd year student in Computer Science / Financial engineering or Master Student in Mathematical Finance with: Strong knowledge of quantitative finance Good knowledge of C++ (knowledge of Python or of parallel computing would be a plus) Interest in financial markets French and English speaking Duration : 6 months