120 citations
- CY Cergy Paris UniversitéFR28 papers
- Centre de Recherche en Économie et StatistiqueFR6 papers
- Sorbonne UniversitéFR4 papers
- Université Paris CitéFR4 papers
- Johannes Kepler University of LinzAT3 papers
- Center for Responsible TravelUS2 papers
- Centre for Research in Engineering Surface TechnologyIE2 papers
- Decision Sciences (United States)US2 papers
- Département mathématiques, informatique, sciences de la donnée et technologies du numériqueFR2 papers
- ESSEC Business SchoolSG2 papers
- Laboratoire de Mathématiques et ApplicationsFR2 papers
- Mathématiques Appliquées à Paris 5FR2 papers
44 papers
Do designated market makers provide liquidity during downward extreme price movements?
Mario Bellia, Kim Christensen, Aleksey Kolokolov +2
We study the trading activity of designated market makers (DMMs) in electronic markets using a unique dataset with audit-trail information on trader classification. DMMs may either…
Statistical Inference in Large Multi-way Networks
Lucas Resende, Guillaume Lecué, Lionel Wilner +1
We propose the Polyads estimator, a new method to estimate structural parameters in weighted multi-way networks while controlling for rich, arbitrary structures of fixed effects. T…
Multivariate Discrete Generalized Pareto Distributions: Theory, Simulation, and Applications to Dry spells
Samira Aka, Marie Kratz, Philippe Naveau
This article extends the multivariate extreme value theory (MEVT) to discrete settings, focusing on the generalized Pareto distribution (GPD) as a foundational tool. The purpose of…
Low-Rank Graphon Estimation: Theory and Applications to Graphon Games
Olga Klopp, Fedor Noskov
We study low-rank estimation of an unknown sparse graphon from sampled network data under operator-norm loss, motivated by targeted interventions in graphon games. Starting from th…
On importance sampling and independent Metropolis-Hastings with an unbounded weight function
George Deligiannidis, Pierre E. Jacob, El Mahdi Khribch +1
Importance sampling and independent Metropolis-Hastings are among the fundamental building blocks of Monte Carlo methods. Both require a proposal distribution that globally approxi…
Minimax optimality of deep neural networks on dependent data via PAC-Bayes bounds
Pierre Alquier, William Kengne
In a groundbreaking work, Schmidt-Hieber (2020) proved the minimax optimality of deep neural networks with ReLu activation for least-square regression estimation over a large class…