Optimal Merton's Problem under Multivariate Affine Volterra Models with Jumps.
We tackle the problem of Utility Maximization under Multivariate Affine Volterra Models with Jumps via BSDEJs and Martingale Optimality Principle: The resulting optimal …
Emmanuel G.
Researcher in Mathematics and Applications
Sorbonne Université
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Laboratoire de Probabilités, Statistique et Modélisation (LPSM)
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4 Place Jussieu, Paris, 75005
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Couloir 16-26 Bureau 201
I am a Research Scientist in Mathematics at LPSM Sorbonne Université, working on stochastic analysis, optimal control, diffusion models, and statistics, with applications to mathematical finance and machine learning.
👩🏼🏫 Outreach in mathematics
We present a new theory of stationarity for a class of stochastic Volterra integral equations.
On the mean-variance problem through the lens of multivariate fake stationary affine Volterra dynamics.
This paper investigates the asymptotic behavior of suitably time-modulated Hawkes processes with heavy-tailed kernels in a nearly unstable regime.
On Path-dependent Volterra Integral Equations: Strong Well-posedness and Stochastic Numerics.
The aim of this paper is to provide a comprehensive analysis of the path-dependent Stochastic Volterra Integral Equations (SVIEs), in which both the drift and the diffusion …
On Utility Maximization under Multivariate Fake Stationary Affine Volterra Models.
We tackle the problem of Utility Maximization under Multivariate Fake Stationary Affine Volterra Models via BSDEs and Martingale Optimality Principle: The resulting optimal …
Fake stationary rough Heston volatility: Microstructure-inspired foundations
This paper investigates the asymptotic behavior of suitably time-modulated Hawkes processes with heavy-tailed kernels in a nearly unstable regime.
👩🏼🏫 Awards and Certificates
This page highlights selected awards and recognitions !
On a Stationarity Theory for Stochastic Volterra Integral Equations
This paper presents a new theory for the stationarity of stochastic Volterra integral equations.
Solving The Dynamic Volatility Fitting Problem: A Deep Reinforcement Learning Approach
The volatility fitting is one of the core problems in the equity derivatives business. Through a set of deterministic rules, the degrees of freedom in the implied volatility …