MyQuantLib
February 24, 2026
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1 min read
This project started from my publication in SIFIN: High Order Approximations and Simulation Schemes for the Log-Heston Process. For pricing complex exotic derivatives and simulating complex models like affine stochastic volatility with jumps, the Monte Carlo engine of the library builds upon the basic structure of the second-order scheme for the log-Heston model developed in my research.
Authors
Quantitative Researcher, Ph.D.
I am a Quantitative Researcher and Applied Mathematician with a Ph.D. from École des Ponts ParisTech and University of Roma Tor Vergata. My expertise lies at the intersection of stochastic calculus, high-performance computing, and numerical methods. I specialize in modeling complex stochastic dynamics and building high-performance numerical solutions in C++ and Python, transforming advanced mathematical theory into fast, accurate pricing and risk infrastructure. With prior industry experience as a Quantitative Analyst at Enel and Risk Analyst at AXA, I am passionate about applying advanced mathematical techniques, Monte Carlo simulations, and data-driven methods to solve complex pricing, risk, and alpha-generation problems in financial markets.