Details of a Researcher - Poignard, Benjamin

Poignard, Benjamin

写真a

Affiliation: Faculty of Science and Technology, Department of Mathematics ( Yagami )

Position: Associate Professor

Scopus Paper Info

Paper Count: 12 Citation Count: 40 h-index: 4

Click to view the Scopus page. The data was downloaded from Scopus API in April 05, 2026, via http://api.elsevier.com and http://www.scopus.com .

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Career 【 Display / hide 】

2017.07

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2019.03

Japan Society for the Promotion of Science, Graduate School of Engineering Science, Osaka University
2019.04

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2023.03

Osaka University, Graduate School of Economics
2019.06

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2023.03

RIKEN AIP, High-dimensional statistical modeling unit
2023.04

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2025.03

The University of Osaka, Graduate School of Economics
2023.04

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Present

RIKEN AIP, Continuous optimization team, Visiting scientist

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Academic Background 【 Display / hide 】

2009.08

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2014.09

ESCP Europe, Master in Management (Grande Ecole Program)

France
2009.09

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2011.06

University Paris 1 Pantheon-Sorbonne, Bachelor and Master 1 in applied mathematics, 2009-2011

France
2011.09

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2012.09

University Paris 1 Pantheon-Sorbonne, Master Modeling and Mathematical Methods in Economics and Finance (DEA MMME)

France
2012.09

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2014.09

ENSAE Paris, Statistician-Economist (Grande Ecole Program)

France
2014.10

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2017.06

Center for Research in Economics and Statistics (CREST) and PSL University, PhD in applied mathematics

France

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Research Areas 【 Display / hide 】

Informatics / Statistical science (Asymptotic theory; Sparsity; Time series)

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Research Keywords 【 Display / hide 】

Sparsity
Time series
Asymptotic theory
High-dimensional statistics

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Papers 【 Display / hide 】

Model-based vs. agnostic methods for the prediction of time-varying covariance matrices

Jean-David Fermanian, Benjamin Poignard, Panos Xidonas

Annals of Operations Research (Springer Science and Business Media LLC) 346 ( 1 ) 511 - 548 2024.09

Lead author, Last author, Corresponding author, Accepted, ISSN 0254-5330

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This article is written in memory of Harry Markowitz, the founder of modern portfolio theory. We report a few human perspectives of his character, we review a large number of his contributions, published both in operations research and finance oriented journals, and we focus on one of the most critical, and still open, portfolio theory issues, the forecast of covariance matrices. Our contribution in this paper is placed exactly towards this direction. More specifically, we compare the performances of several approaches to predict the variance-covariance matrices of vectors of asset returns, through simulated and real data experiments: some dynamic models such as Dynamic Conditional Correlation (DCC) and C-vine GARCH on one side, and several agnostic methods (Average Oracle, usual “Sample” matrix) on the other side. The most robust methods seem to be DCC and the Average Oracle approaches.
- Access to Document (DOI)
Sparse M-estimators in semi-parametric copula models

Jean-David Fermanian, Benjamin Poignard

Bernoulli (Bernoulli Society for Mathematical Statistics and Probability) 30 ( 3 ) 2475 - 2500 2024.08

Lead author, Last author, Corresponding author, Accepted, ISSN 1350-7265

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We study the large-sample properties of sparse M-estimators in the presence of pseudo-observations. Our frame work covers a broad class of semi-parametric copula models, for which the marginal distributions are unknown and replaced by their empirical counterparts. It is well known that the latter modification significantly alters the limiting laws compared to usual M-estimation. We establish the consistency and the asymptotic normality of our sparsepenalized M-estimator and we prove the asymptotic oracle property with pseudo-observations, possibly in the case when the number of parameters is diverging. Our framework allows to manage copula-based loss functions that are potentially unbounded. Additionally, we state the weak limit of multivariate rank statistics for an arbitrary dimension and the weak convergence of empirical copula processes indexed by maps. We apply our inference method to Canonical Maximum Likelihood losses with Gaussian copulas, mixtures of copulas or conditional copulas. The theoretical results are illustrated by two numerical experiments.
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Estimation of high-dimensional vector autoregression via sparse precision matrix

Poignard B., Asai M.

Econometrics Journal 26 ( 2 ) 307 - 326 2023.05

Lead author, Last author, Corresponding author, Accepted, ISSN 13684221

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We consider the problem of estimating sparse vector autoregression (VAR) via penalized precision matrices. This matrix is the output of the underlying directed acyclic graph of the VAR process, whose zero components correspond to the zero coefficients of the graphical representation of the VAR. The sparsity-based precision matrix estimator is deduced from the D-trace loss with convex and nonconvex penalty functions. We establish the consistency of the penalized estimator and provide the conditions for which all true zero entries of the precision matrix are actually estimated as zero with probability tending to one. The relevance of the method is supported by simulated experiments and a real data application.
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High‐dimensional sparse multivariate stochastic volatility models

Benjamin Poignard, Manabu Asai

Journal of Time Series Analysis (Wiley) 44 ( 1 ) 4 - 22 2022.04

Lead author, Last author, Corresponding author, Accepted, ISSN 0143-9782

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Although multivariate stochastic volatility models usually produce more accurate forecasts compared with the MGARCH models, their estimation techniques such as Bayesian MCMC typically suffer from the curse of dimensionality. We propose a fast and efficient estimation approach for MSV based on a penalized OLS framework. Specifying the MSV model as a multivariate state-space model, we carry out a two-step penalized procedure. We provide the asymptotic properties of the two-step estimator and the oracle property of the first-step estimator when the number of parameters diverges. The performances of our method are illustrated through simulations and financial data. Supplementary Material presenting technical proofs is available online.
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High-dimensional nonlinear feature selection with Hilbert-Schmidt Independence criterion Lasso

Makoto Yamada, Benjamin Poignard, Hiroaki Yamada, Tobias Freidling

Journal of the Japan Statistical Society 2022.04

Lead author, Last author, Corresponding author, Accepted
- Access to Document (DOI)