Probability • Quantitative Finance • C++ / Python
Master's student in Probability and Finance, focused on stochastic modelling, numerical methods, robust market data aggregation and market microstructure simulation.
About
I work on problems where probability theory, numerical methods and software engineering meet: stochastic processes, Monte Carlo methods, model calibration, market data quality and C++/Python tooling.
My objective is to build research-grade implementations: clean modelling assumptions, reproducible experiments, precise numerical diagnostics and code that can evolve toward production.
Research & interests
Interest in local stochastic volatility, stochastic rates, Monte Carlo methods, particle-based calibration and numerical stability in option pricing.
Design of agent-based market simulators with latency, order book dynamics, liquidity providers, noise flow and short-horizon trading agents.
Work on probabilistic structures including anisotropic percolation, projection techniques and rigorous finite/infinite-volume arguments.
Work
A quantitative study focused on robust aggregation of cryptocurrency prices across fragmented venues, with an emphasis on market microstructure, noisy quotes and statistical reliability.
View case study →Numerical work around equity derivatives models combining local stochastic volatility, stochastic interest rates and calibration-oriented simulation.
View case study →Architecture for simulating order-driven markets with agents, strategies, reaction times, matching rules and detailed execution logs.
View case study →Study of paths built from the maximum modulus principle, following points where a holomorphic function reaches its maximal modulus on admissible families of surrounding curves.
View note →Contact