About

About Me

Prior to the MFE, Loic studied statistics and economics at ENSAE Paris where he developed solid skills in stochastic calculus, probability, econometrics, and machine learning using different languages (Python, R and C++). He also holds an associate degree in law from Paris I Panthéon-Sorbonne University.

His internship experience includes asset management, equity trading and quantitative research. While at the Dutch investment bank Kempen, he worked on several data science and machine learning projects to help traders identify signals. Then, in a quantitative research role at EY Advisory, he was responsible for developing and calibrating a local volatility model that improved option pricing precision as well as working on call-spread options P&L calculations and client engagements for major French banks.

Loic enjoys exploring innovative methodologies with machine learning such as evolution strategies for trading and deep learning for options pricing. He also has a keen interest in systematic trading and quantitative strategies. Loic spends his free time reading economic news and studying geopolitics; he is also a longtime sailor and sailing instructor.

I am most skilled in: Python and Machine Learning

Other interests and hobbies: Sailing, Photography, Geopolitics

Experience

Quantitative Researcher — Brevan Howard

Aug 2024 – Present

I lead a project on researching and developing a model for local volatility in the field of Vanilla options. The project focused on taking into consideration the smile of volatility observed in the market; the evaluation of the model was conducted through the comparison of different pricing with generated and real data. I also contributed to different client engagements to support the team and provide help on specific asset valuation of equity-linked swaps for a top tier French bank using multiple data sources.

Quantitative Researcher — Centiva Capital

Oct 2020 – July 2024

I lead a project on researching and developing a model for local volatility in the field of Vanilla options. The project focused on taking into consideration the smile of volatility observed in the market; the evaluation of the model was conducted through the comparison of different pricing with generated and real data. I also contributed to different client engagements to support the team and provide help on specific asset valuation of equity-linked swaps using multiple data sources.

Quantitative Advisory Intern — EY Advisory

Sept 2019 – Feb 2020

I lead a project on researching and developing a model for local volatility in the field of Vanilla options. The project focused on taking into consideration the smile of volatility observed in the market. The evaluation of the model was conducted through the comparison of different pricing with generated and real data. I also contributed to client engagements for major French banks.

Equity Trading Intern — Kempen & Co.

June 2019 – Aug 2019

As an Equity Trading Intern, my work focused on bringing data-driven approaches to stock picking and helping traders identify opportunities in the trading of life science companies. I was in charge of developing APIs, building data pipelines, and assessing the performance of different methodologies to extract information from financial market data. I worked closely with quantitative analysts to monitor the implementation and deployment of these approaches.

Credit Portfolio Management Intern — Amundi Asset Management

June 2018 – Sept 2018

As part of the Credit Investment Grade team, I was in charge of creating a hedging tool based on market data to help portfolio managers manage risk. I was also exploring portfolio profiling to support the growing demand for SRI (socially responsible investment) and ESG (environmental, social, governance) labelled portfolios.

Education

Master of Financial Engineering — UC Berkeley

2020 – 2021 · Specialization: Machine Learning

Classes focused on statistics, quantitative finance, machine learning and deep learning, mostly applied to finance projects. Top 3 MFE program globally.

Master of Statistics and Economics — ENSAE Paris

2017 – 2021 · Specialization: Data Science and Machine Learning for Finance

Classes focused on statistics, time series, probability and quantitative finance. Projects included high-frequency data analysis of the electricity spot market in Germany, studying liquidity questions and predictability of price moves. One of the most prestigious Grandes Écoles in France, specialized in statistics and machine learning.

Advanced Classes of Mathematics and Physics — Lycée Louis-le-Grand

2015 – 2017 · Classes Préparatoires

Very intensive syllabus in theoretical mathematics and physics, including computer science and engineering. Final project focused on modelling highway traffic to determine the optimal toll gate configuration. Top 3 Classes Préparatoires in France.