Stéphane Crépey

Université Paris Cité

Stéphane Crépey is Distinguished professor of mathematics at Université Paris Cité (UPC), Laboratoire de Probabilités, Statistique et Modélisation (LPSM), Head of Team Mathematical Finance and Numerical Probability. His research interests are:

counterparty credit risk and XVA analysis, central counterparties, risk analysis;
machine learning in finance: supervised learning of prices, sensitivities and risk metrics from simulated payoffs, model calibration by neural nets or Gaussian processes regression, anomaly detection;
model risk and uncertainty quantification;
backward stochastic differential equations, random times modeling, enlargement of filtration.

He is the author of 80+ research papers published in journals including Annals of Probability, Annals of Applied Probability, Finance and Stochastics, Mathematical Finance, Stochastic Processes and their Applications, Electronic Journal Probability, SIAM Journal on Financial Mathematics, SIAM/ASA Journal on Uncertainty Quantification, SIAM Journal on Mathematical Analysis, Quantitative Finance, and Risk Magazine. He co-authored two books: Financial Modeling, a Backward Stochastic Differential Equations Perspective (S. Crépey, Springer Finance Textbook Series, 2013) and Counterparty Risk and Funding, a Tale of Two Puzzles (S. Crépey, T. Bielecki and D. Brigo, Chapman & Hall/CRC Financial Mathematics Series, 2014).

He graduated from ENSAE ParisTech and holds a PhD in differential games and mathematical finance from Ecole Polytechnique and INRIA Sophia Antipolis.

Articles by Stéphane Crépey

Cutting Edge

CVA sensitivities, hedging and risk

A probabilistic machine learning approach to CVA calculations is proposed

26 Jul 2024

Banking

A Darwinian theory of model risk

An ex ante methodology is proposed to analyse the model risk pattern for a broad class of structures

12 Jul 2021

Original research

Nowcasting networks

The authors devise a neural network-based compression/completion methodology for financial nowcasting.

09 Mar 2021

Original research

Gaussian process regression for derivative portfolio modeling and application to credit valuation adjustment computations

The authors present a multi-Gaussian process regression approach, which is well suited for the over-the-counter derivative portfolio valuation involved in credit valuation adjustment (CVA) computation.

27 Jul 2020

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