PUBLICATIONS


PUBLISHED AND ACCEPTED PAPERS

2025

[1] M. Allouche, S. Girard, and E. Gobet, ExceedGAN: simulation above extreme thresholds using Generative
Adversarial Networks, Extremes (2025+). https://inria.hal.science/hal-05044516.
[2] M. Allouche, S. Girard, and E. Gobet, Learning extreme Expected Shortfall and Conditional Tail Moments with neural networks. Application to cryptocurrency data, Neural Networks (2025). https://inria.hal.science/hal-04347859.
[3] D. Barrera, S. Crépey, E. Gobet, H.-D. Nguyen, and B. Saadeddine, Statistical Learning of Value-at-Risk
and Expected Shortfall, Mathematical Finance (2025). First published: 07 September 2025.
[4] D. Bastide and S. Crépey, Provisions and Economic Capital for Credit Losses, Quantitative Finance 25
(2025), no. 4, 617-631.
[5] D. Bastide, S. Crépey, S. Drapeau, and M. Tadese, Resolving a Clearing Member’s Default: A Radner Equi-
librium Approach, Mathematics and Financial Economics 19 (2025), 183-223.
[6] C. Bénézet, S. Crépey, and D. Essaket, The Recalibration Conundrum: Hedging Valuation Adjustment
for Callable Claims, International Journal of Theoretical and Applied Finance, posted on 2025, DOI
10.1142/S0219024925500220.
[7] C. Crofils, E. Gallic, and G. Vermandel, The dynamic effects of weather shocks on agricultural production,
Journal of Environmental Economics and Management 130 (2025), 103078.
[8] L. Dubois, JG. Sahuc, and G. Vermandel, A general equilibrium approach to carbon permit banking, Journal
of Environmental Economics and Management 129 (2025), 103076.
[9] J. El Methni and S. Girard, A refined extreme quantile estimator for Weibull tail-distributions, REVSTAT -
Statistical Journal (2025). http://hal.science/hal-04022982.
[10] S. Girard, E. Gobet, and J. Pachebat, HTGAN: Heavy-Tail GAN for multivariate dependent extremes via
latent-dimensional control, International Journal of Computer Mathematics (2025+). hal-04700084.
[11] E. Gobet and M. Grangereau, Federated stochastic control of numerous heterogeneous energy storage systems,
Journal on Optimization Theory and Applications 208:107 (2026).
[12] E. Gobet, M. Lerasle, and D. Metivier, Accelerated convergence of error quantiles using robust randomized
quasi Monte Carlo methods, Journal of Complexity 92 (2026).
[13] E. Gobet, Y. Liu, and G. Vermandel, Meta-modelling paths of simple climate models using neural networks
and dirichlet polynomials: an application to DICE, European Actuarial Journal (2025), 1–45.
[14] E. Gobet, D. Metivier, and S. Parey, Interpretable Seasonal Hidden Markov Model for spatio-temporal stochas-
tic rain generation in France, Advances in Statistical Climatology, Meteorology and Oceanography 9 (2025),
DOI 10.5194/ascmo-11-159-2025.
[15] A. Grimaud, I. Salle, and G. Vermandel, A Dynare toolbox for social learning expectations, Journal of Eco-
nomic Dynamics and Control 172 (2025), 104984.
[16] Y. Gu and C. Rey, Deterministic computation of quantiles in a Lipschitz framework, Journal of Computational
Mathematics 458 (2025), 116344.
[17] C. Hillairet and A. Reveillac, Explicit correlations for the Hawkes processes, Stochastics, posted on 2025,
1–29, DOI 10.1080/17442508.2025.2603365.
[18] T. Moins, J. Arbel, A. Dutfoy, and S. Girard, On the use of a local ˆR to improve MCMC convergence
diagnostic, Bayesian Analysis (2025). preprint hal-03600407.
[19] E. Ndiaye, A. Bezat, E. Gobet, C. Guivarch, and Y. Jiao, Optimal business model adaptation plan for a
company under a transition scenario, Mathematics and Financial Ecoomics 9 (2025+).
[20] G. Perrin, J. Reygner, and V. Chabridon, Enhancing reliability analysis with limited observations: A statistical
framework for system safety margins, Structural Safety 119 (2025), 102670.
[21] C. Poirier and G. Vermandel, Reallocation and Heterogeneous Elasticities in Production Networks, Working
Paper (2025). https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4429307.
[22] G. Vermandel, Environmental subsidies to mitigate net zero transition costs, American Economic Journal:
Macroeconomics (2026). forthcoming.
[23] G. Vermandel, G. Ascari, J. Bullard, and I. Salle, Soft Landing and Inflation Scares, Journal of Monetary
Economics (2026). forthcoming.
[24] G. Vermandel, G. Benmir, and I. Jaccard, Green Asset Pricing, ECB Working Paper (2024). https://www.
ecb.europa.eu/pub/pdf/scpwps/ecb.wp2477~e636f9c496.en.pdf.

IN PROGRESS (Preprints, Submitted papers)

[25] A. Boumezoued, Y. Cherkaoui, and C. Hillairet, Cyber Risk Frequency Modelling Using Hawkes Processes :
Calibration on Attack and Vulnerability Data (2025). preprint hal-05305048.
[26] F. Bourgey, S. Crépey, D. Essaket, N. Frikha, and G. Vermandel, On the carbon tax in Golosov et al.’s 2014
DGSE central planning model (2025). Work in progress.
[27] S. Crépey, XVA Analysis: Probabilistic, Risk Measure, and Machine Learning Issues, Taylor & Francis, New
York. Chapman & Hall/CRC Financial Mathematics Series (forthcoming).
[28] S. Crépey, S. Drapeau, and M. Tadese, Comparison of Tax and Cap-and-Trade Carbon Pricing Schemes
(2025). preprint hal-al-05316079/.
[29] S. Crépey, M. Tadese, and G. Vermandel, Sensitivity Analysis of Emission Markets: A Radner Equilibrium
Approach in Discrete Time (2025). preprint hal-04775054v2/.
[30] J. El Methni and S. Girard, Approximate Bayesian Computation of reduced-bias extreme risk measures from
heavy-tailed distributions (2025). preprint hal-04965629.
[31] J. El Methni, S. Girard, and P. Laveur, A new family of inequality indices: axioms, inference and tail properties
(2025). preprint hal-05153188.
[32] A. Franchini, S. Girard, and A. Dutfoy, Adaptive confidence intervals for extreme quantiles from heavy-tailed
distributions (2025). preprint hal-05322341.
[33] S. Girard, T. Opitz, A. Usseglio-Carleve, and C. Yan, Changepoint identification in heavy-tailed distributions
(2025). preprint hal-05044135.
[34] S. Girard and C. Pakzad, Functional Extreme-PLS (2025). preprint hal-04775054.
[35] G. Perrin, M. Dumon, and B. Lebental, A variance-based framework for robust variable selection under
correlated and incomplete observations (2026). preprint hal-05316079/.
[36] G. Perrin, J. Jorge Do Marco, C. Funfschilling, and C. Soize, Estimating Intractable Posterior Distri-
butions through Gaussian Process regression and Metropolis-adjusted Langevin procedure (2025). preprint
hal-05316079/.
[37] M. Temple-Boyer, E. Cussenot, G. Perrin, V. Chabridon, J. Pelamatti, E. Remy, and B. Iooss, Risk measures
in reliability engineering: comparison, duality and decision-making (2025). preprint hal-05316079/.
[38] M. Yachouti, G. Perrin, and J. Garnier, Towards history-aware sensitivity analysis for time series, accepted
in ESAIM: Probability and Statistics (2026).

2024

  1. M. Allouche, E. Gobet, C. Lage, and E. Mangin, Structured Dictionary Learning of Rating Migration Matrices for Credit Risk Modeling, Computational Statistics (2024).https://doi.org/10.1007/s00180-023-01449-y.
  2. M. Allouche, S. Girard, and E. Gobet, Estimation of extreme quantiles from heavy-tailed distributions with neural networks, Statistics and Computing 34 (2024), no. 12, 1-35. https://doi.org/10.1007/s11222-023-10331-2.
  3. M. Allouche, J. El Methni, and S. Girard, Reduced-bias estimation of the extreme conditional tail expectation for Box-Cox transforms of heavy-tailed distributions., Journal of Statistical Planning and Inference 33 (2024), 106-189. https://doi.org/10.1016/j.jspi.2024.106189.
  4. J. Arbel, S. Girard, and H. Lorenzo, Shrinkage for Extreme Partial Least Squares, Statistics and Computing
    34 (2024), no. 181. https://doi.org/10.1007/s11222-024-10490-w.
  5. C. Escribe, J. Garnier, and E. Gobet, A mean field game model for renewable investment under
    long-term uncertainty and risk aversion., Dynamic Games and Applications 14 (2024), 1093-1130.
    https://doi.org/10.1007/s13235-024-00554-x.
  6. G. Perrin and C. Soize, Reconstruction of Random Fields Concentrated on an Unknown Curve using Irregularly Sampled Data., Methodology and Computing in Applied Probability 26 (2024), no. 9. https://doi.org/10.1007/s11009-024-10079-w.
  7. A. Grimaud, I. Salle, and G. Vermandel, Social Learning expectations: microfoundations and a Dynare Toolbox., Journal of Economics Dynamic and Control. (2024). https://dx.doi.org/10.2139/ssrn.4437177.
  8. J. Arifovic, A. Grimaud, I. Salles, and G. Vermandel, Social Learning and Monetary Policy at the Effective Lower Bound., Journal of Money, Credit and Banking. (2024). https://doi.org/10.1111/jmcb.13133.

2023

  1. C. Albanese, S. Crepey, and S. Iabichino, Quantitative reverse stress testing, bottom-up, Quantitative Finance
    23 (2023), no. 5, 863-875. https://doi.org/10.1080/14697688.2023.2187315.
  2. J. Arbel, S. Girard, H. Nguyen, and A. Usseglio-Carleve, Multivariate expectile-based distribution : properties,
    Bayesian inference, and applications., Journal of Statistical Planning and Inference 225 (2023), 146-170. https://doi.org/10.1016/j.jspi.2022.12.001.
  3. D. Bastide, S. Crepey, S. Drapeau, and M. Tadese, Derivatives Risks as Costs in a One-Period Network
    Model, Frontiers of Mathematical Sciences (2023). https://doi.org/10.3934/fmf.2023014.
  4. C. Benezet, E. Gobet, and R. Targino, Transform MCMC schemes for sampling intractable factor
    copula models, Methodology and Computing in Applied Probability 25 (2023), no. 13. https://doi.org/10.1007/s11009-023-09983-4.
  5. G. Benmir, I. Jaccard, and G. Vermandel, Optimal monetary policy in an estimated SIR model, European
    Economic Review 156 (2023). https://doi.org/10.1016/j.euroecorev.2023.104502.
  6. F. Bourgey, S. De Marco, P.K. Fritz, and P. Pigato, Local volatility under rough volatility, Mathematical Finance 33 (2023), no. 4, 1119-11145. https://doi.org/10.1111/mafi.12392.
  7. F. Bourgey, S. De Marco, and E. Gobet, Weak approximations and VIX option price expansions
    in forward variance curve models, Quantitative Finance 23 (2023), no. 9, 1259–1283. https://doi.org/10.1080/14697688.2023.2227230.
  8. M. Bousebata, G. Enjolras, and S. Girard, Extreme Partial Least-Squares, Journal of Multivariate Analysis
    194 (2023), 105-101. https://doi.org/10.1016/j.jmva.2022.105101.
  9. E. Gobet and C. Lage, Optimal ecological transition path of a credit portfolio distribution,
    based on multidate Monge-Kantorovich formulation, Annals of Operations Research (2023). https://doi.org/10.1007/s10479-023-05385-4.
  10. T. Moins, J. Arbel, S. Girard, and A. Dutfoy, Reparameterization of extreme value framework
    for improved Bayesian workflow, Computational Statistics & Data Analysis 187 (2023). https://doi.org/10.1016/j.csda.2023.107807.
  11. Y. Jiao and N. Kolliopoulos, Well-posedness of a system of SDEs driven by jump random measures, Stochastics and Dynamics 23 (2023), no. 04. https://doi.org/10.1142/S0219493723500284.

2022

  1. M. Allouche, S. Girard, and E. Gobet, A generative model for fBm with deep ReLU neural networks, Journal of Complexity 73 (2022), 101–667. https://doi.org/10.1016/j.jco.2022.101667.
  2.  M. Allouche, S. Girard, and E. Gobet,, EV-GAN : Simulation of extreme events with ReLU neural networks, Journal of Machine Learning Research 23 (2022), no. 150, 1–39. https://jmlr.org/papers/v23/21-0663.html.
  3. M. Allouche, J. El Methni, and S. Girard, A refined Weissman estimator for extreme quantiles, Extremes (2022). https://doi.org/10.1007/s10687-022-00452-8.
  4. P.-C. Aubin-Frankowski and Z. Szabo, Handling hard affine SDP shape constraints in RKHSs, Journal of Machine Learning Research 23 (2022), no. 297, 1–54. https://jmlr.org/papers/v23/21-0007.html.
  5. F. Bourgey and S. De Marco, Multilevel Monte Carlo simulation for VIX options in the rough Bergomi model, Journal of Computational Finance 26 (2022), no. 2, 30. https://doi.org/10.21314/JCF.2022.023.
  6. F. Bourgey, E. Gobet, and Y. Jiao, Bridging socioeconomics pathways of CO2 emission and credit risk, Annals of Operations Research (2022). https://doi.org/10.1007/s10479-022-05135-y.
  7. F. Bourgey, E. Gobet, and C. Rey, A comparative study of polynomial-type chaos expansions for indicator functions, SIAM/ASA Journal on Uncertainty Quantification 10 (2022), no. 4, 1350–1383. https://doi.org/10.1137/21M1413146.
  8.  A. Cousin, Y. Jiao, C.Y. Robert, and O.D. Zerbib, Optimal asset allocation subject to withdrawal risk and solvency constraints, Risks 10 (2022), no. 1, 15. https://doi.org/10.3390/risks10010015.
  9. S. Girard, G. Stupfler, and A. Usseglio-Carleve, Nonparametric extreme conditional expectile estimation, Scandinavian Journal of Statistics 49 (2022), no. 1, 78–115. https://doi.org/10.1111/sjos.12502.
  10. S. Girard, G. Stupfler, and A. Usseglio-Carleve, Functional estimation of extreme conditional expectiles, Econometrics and Statistics  21 (2022),  no.  1,  131–158.  https://doi.org/10.1016/j.ecosta.2021.05.006.
  11. S. Girard, G. Stupfler, and A. Usseglio-Carleve, On automatic bias reduction for extreme expectile estimation, Statistics and Computing 32 (2022), 64. https://doi.org/10.1007/s11222-022-10118-x.
  12. E. Gobet and M. Grangereau, Newton method for stochastic control problems, SIAM Journal on Control and Optimization 60 (2022), no. 5, 2996–3025. https://doi.org/10.1137/21M1408567.
  13. E. Gobet and M. Grangereau, Extended McKean-Vlasov optimal stochastic control applied to smart grid management, ESAIM:COCV 28 (2022), no. 40, 37. https://doi.org/10.1051/cocv/2022034.
  14. Y. Jiao, Y. Salhi, and S. Wang, Dynamic Bivariate Mortality Modelling, Methodol. Comput. Appl. Probab. 24 (2022), 917–938. https://doi.org/10.1007/s11009-022-09955-0.
  15. C. Martini and A. Mingone, No arbitrage SVI, SIAM Journal on Financial Mathematics 13 (2022), no. 1, 227–261. https://doi.org/10.1137/20M1351060.

2021

  1. C. Albanese, S. Crepey, and S. Iabichino, Chapter 17 : Capital and collateral simula- tion for reverse stress testing, Reverse Stress Testing in Banking, 2021, pp. 349–360. https://doi.org/10.1515/9783110647907-017.
  2. M. Chataigner, A. Cousin, S. Crepey, M. Dixon, and D. Gueye, Short communication : Beyond surrogate modeling: Learning the local volatility via shape constraints, SIAM Journal on Financial Mathematics 12 (2021), no. 3, SC58-SC69. https://doi.org/10.1137/20M1381538.
  3. A. Daouia, S. Girard, and G. Stupfler, ExpectHill estimation, extreme risk and heavy tails, Journal of Econometrics 221 (2021), no. 1, 97–117. https://doi.org/10.1016/j.jeconom.2020.02.003.
  4. S. De Marco, On the harmonic mean representation of the implied volatility, SIAM J. Finan. Math. 12 (2021), no. 2, 551–565. https://doi.org/10.1137/20M1352120.
  5. H. Drees and A. Sabourin, Principal Component Analysis for Multivariate Extremes, Electron. J. Statist. 15 (2021), no. 1, 908–943. https://doi.org/10.1214/21-EJS1803.
  6. L. Gardes and S. Girard, On the estimation of the variability in the distribution tail, Test 30 (2021), no. 2, 884–907. https://doi.org/10.1007/s11749-021-00754-2.
  7. S. Girard, G. Stupfler, and A. Usseglio-Carleve, Extreme Conditional Expectile Estima- tion in Heavy-Tailed Heteroscedastic Regression Model, Annals of Statistics 49 (2021), no. 6, 3358–3382. https://doi.org/10.1214/21-AOS2087.
  8. S. Girard, G. Stupfler, and A. Usseglio-Carleve, Extreme Lp - quantile kernel regression, Advances in contemporary Statistics and Econometrics (2021), 197–219. https://doi.org/10.1007/978-3-030-73249-3 11.
  9. Y. Jiao, C. Ma, S. Scotti, and C. Zhou, The Alpha-Heston stochastic volatility model, Mathematical Finance 31 (2021), no. 3, 943–978. https://doi.org/10.1111/mafi.12306.
  10. A. Lambert, S. Parekh, Z. Szabo, and F. Alche-Buc, Emotion transfer using vector-valued infinite task learning, CoRR (2021). https://doi.org/10.13140/RG.2.2.27032.93442.

2020

  1. A. Ahmad, E. Deme, A. Diop, S. Girard, and A. Usseglio Carleve, Estimation of extreme quantiles from heavy-tailed distributions in a location-dispersion regression model, Electronic Journal of Statistics 14 (2020), no. 2, 4421–4456. https://doi.org/10.1214/20-EJS1779.
  2. C. Albanese, Y. Armenti, and S. Crepey, XVA Metrics for CCP optimisation, Statistics & Risk Modeling 37 (2020), no. 1-2, 25–53. https://doi.org/10.1515/strm-2017-0034.
  3. C. Albert, A. Dutfoy, and S. Girard, Asymptotic behavior of the extrapolation error associated with the estimation of extreme quantiles, Extremes 23 (2020), no. 2, 349–380. https://doi.org/10.1007/s10687-019-00370-2.
  4. M. Arnaudon and P. Del Moral, A second order analysis of McKean-Vlasov semigroups, Annals of Applied Probability 30 (2020), no. 6, 2613–2664. https://doi.org/10.1214/20-AAP1568.
  5. P.-C. Aubin-Frankowski and Z. Szabo, Hard shape-constrained kernel machines, Advances in Neural Information Processing Systems (NeurIPS), December 2020, pp. 384–395. https://proceedings.neurips.cc/paper files/paper/2020.
  6. P.-C. Aubin-Frankowski and Z. Szabo, Hard Shape-Constrained Kernel Regression, Joint Structures and Common Foundations of Statistical Physics, Information Geometry and Inference for Learning (SPIG-2020), Jul. 2020. https://franknielsen.github.io/SPIG-LesHouches2020/Aubin-SPIGL2020.pdf.
  7. P.-C. Aubin-Frankowski, N. Petit, and Z. Szabo, Kernel Regression for Vehicle Trajectory Reconstruction under Speed and Inter-vehicular Distance Constraints, IFAC-PapersOnLine 53 (2020), no. 2, 15084-15089. https://doi.org/10.1016/j.ifacol.2020.12.2030.
  8. F. Bourgey, E. Gobet, and C. Rey, Meta-model of a large credit risk portfolio in the Gaussian copula model, SIAM Journal on Financial Mathematics 11 (2020), no. 4, 1098-1136. https://doi.org/10.1137/19M1292084.
  9. F. Bourgey, S. De Marco, E. Gobet, and A. Zhou, Multilevel Monte-Carlo methods and lower–upper bounds in Initial Margin computations, Monte Carlo methods and Applications 2 (2020), no. 26, 131–161. https://doi.org/10.1515/mcma-2020-2062.
  10. L. Chamakh, E. Gobet, and Z. Szabo, Orlicz Random Fourier Features, Journal of Machine Learning Research 21 (2020), no. 145, 1–37. https://jmlr.org/papers/v21/19-1031.html.
  11. S. Crepey and M. Dixon, Gaussian Process Regression for Derivative Portfolio Modeling and Application to CVA Computations, Journal of Computational Finance 24 (2020), no. 1, 47–81. https://doi.org/10.21314/JCF.2020.386.
  12. S. Crepey, G. Fort, E. Gobet, and U. Stazhynski, Uncertainty Quantification for Stochas- tic Approximation Limits Using Chaos Expansion, SIAM/ASA Journal on Uncertainty Quantification 8 (2020), no. 3, 1061–1089. https://doi.org/10.1137/18M1178517.
  13. A. Daouia, S. Girard, and G. Stupfler, Tail expectile process and risk assessment, Bernoulli 26 (2020), no. 1, 531–556. https://doi.org/10.3150/19-BEJ1137.
  14. L. Gardes, S. Girard, and G. Stupfler, Beyond tail median and conditional tail expectation: extreme risk estimation using tail Lp-optimisation, Scandinavian Journal of Statistics 47 (2020), no. 3, 922–949. https://doi.org/10.1111/sjos.12433.

2019

  1. J. Arbel, M. Crispino, and S. Girard, Dependence properties and Bayesian inference for asymmetric multivariate copulas, Journal of Multivariate Analysis 174 (2019), 104–530. https://doi.org/10.1016/j.jmva.2019.06.008.
  2. M. Arnaudon and P. Del Moral, A variational approach to nonlinear and interacting diffusions, Stochastic Analysis and Applications 37 (2019), no. 5, 717–748. https://doi.org/10.1080/07362994.2019.1609985.
  3. D. Barrera and E. Gobet, Quantitative bounds for concentration-of-measure inequalities and empirical regression: the independent case, Journal of Complexity 52 (2019), 45–81. https://doi.org/10.1016/j.jco.2019.01.003.
  4. P. Del Moral and S.S. Singh, A forward-backward stochastic analysis of diffusion flows, Research Report INRIA (2019). https://hal.inria.fr/hal-02161914v5.