Noufel Frikha

Professor of Applied Mathematics

Université Paris 1 - Panthéon Sorbonne

Biography

Noufel Frikha is a professor of applied mathematics at Université Paris 1 - Panthéon Sorbonne. His research interests include numerical probability, stochastic analysis, mathematical finance and machine learning. He is currently the director of the master 2 program IRFA (Ingénierie du Risque : Finance et Assurance).

Interests
  • Probability Theory
  • Numerical probability
  • Mathematical Finance
Education

  • HDR, 2017

    Université Paris Cité

  • PhD in Applied Mathematics, 2010

    Sorbonne Université

Experience

 
 
 
 
 
Full Professor of Applied Mathematics
Université Paris 1 - Panthéon Sorbonne
September 2022 – Present Paris

I’m a member of UFR 02 and CES. Here is a list of my current teaching courses:

  • Market Risk Measures (M2)
  • Calibration in Finance (M2)
  • Portfolio choice theory and asset pricing (M1)
  • Modélisation financière (L3)
 
 
 
 
 
Assistant Professor of Applied Mathematics
Université Paris Cité
September 2012 – August 2022 Paris

I was a member of UFR 27 and LPSM. I had the pleasure to teach:

  • Stochastic calculus and Mathematical finance (M2)
  • Mathematical Finance (M1)
  • Monte Carlo methods (M2)

Featured Publications

Noufel Frikha, Arturo Kohatsu-Higa
July, 2023 In ICW
On some asymptotic expansions of skew diffusions
On some asymptotic expansions of skew diffusions

Relying on a perturbation technique of Markov semigroup, also known as the parametrix method, we establish some asymptotic expansion for the Markov semigroup generated by a skew diffusion process with respect to a small parameter. Though the approach developed here is general, we focus on the case of short time and small skew heat kernel expansions
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Recent Publications

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Martin Arnaiz Iglesias, Adil Rengim Cetingoz, Noufel Frikha (2024). Mirror Descent algorithms for Risk Budgeting Portfolios.

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Stéphane Crépey, Noufel Frikha, Azar Louzi, Jonathan Spence (2024). Adaptive Multilevel Stochastic Approximation of the Value-at-Risk.

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Noufel Frikha, Huyên Pham, Xuanye Song (2024). Full error analysis of policy gradient learning algorithms for exploratory linear quadratic mean-field control problem in continuous time with common noise.

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Stéphane Crépey, Noufel Frikha, Azar Louzi, Gilles Pagès (2023). Asymptotic Error Analysis of the Multilevel Stochastic Approximations of the Value-at-Risk and the Expected Shortfall. Forthcoming in Electronic Journal of Probability.

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Jean-François Chassagneux, Junchao Chen, Noufel Frikha, Chao Zhou (2023). A learning scheme by sparse grids and Picard approximations for semilinear parabolic PDEs. IMA Journal of Numerical Analysis.

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Recent & Upcoming Talks

Phd students

  • Martin Arnaiz Iglesias, 2024-, Some learning schemes for the risk budgeting problem.
  • Dounia Essaket, 2022-, Bloomberg Quant PhD fellow. Thesis co-supervised with S. Crépey.
  • Xuanye Song, 2021-2024, Mean-field Reinforcement learning in continuous time: theoretical and numerical aspects, and applications. Defended on 12th December 2024. Thesis co-supervised with H. Pham.
  • Azar Louzi, 2021-2024, Multilevel stochastic algorithms for VaR and ES. Defended on 18th December 2024. Thesis co-supervised with S. Crépey.
  • Junchao Chen, 2018-2021, Probabilistic numerical approximation schemes in finance: deep learning methods for high-dimensional BSDEs and unbiased Monte Carlo algorithms for stochastic volatility models. Defended on 23th March 2022. Thesis co-supervised with J.-F. Chassagneux.
  • Houzhi Li, 2017-2020, Study of numerical methods for some stochastic differential equations in finance and modeling of capital distribution in financial market. Defended on 30th March 2021. Thesis co-supervised with P. Tankov. Houzhi is now a Quantitative Researcher at Shanghai Luoshu Investment Co. Ltd.

Contact

You can contact me by email