PUBLICATIONS


PUBLISHED AND ACCEPTED PAPERS

2025

  1. Y. Gu, C. Rey, Deterministic computation of quantiles in a Lipschitz framework, Journal of Computational and Applied Mathematics, 48 (2025). https://doi.org/10.1016/j.cam.2024.116344.

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.

IN PROGRESS (PREPRINTS, SUBMITTED PAPERS)


  1. M. Allouche, S. Girard, and E. Gobet, Learning out-of-sample expected shortfall and conditional tail moments with neural networks. Application to cryptocurrency data., preprint (2024). https://hal.science/hal-04347859.
  2. M. Allouche, S. Girard, and E. Gobet, On the simulation of extreme events with neural networks., preprint (2024). https://hal.science/hal-04416809.
  3. D. Barrera, S. Crepey, E. Gobet, H.-D. Nguyen, and B. Saadeddine, Statistical of value-at-risk and expected shortfall, preprint (2024). https://arxiv.org/abs/2209.06476.
  4. D. Barrera and E. Gobet, Generalization bounds for nonparametric regression with β-mixing samples, preprint (2021). https://hal.archives-ouvertes.fr/hal-03311506.
  5. D. Bastide and S. Crepey, Provisions and economic capital for credit losses, preprint (2024). arXiv:2401.07728.
  6. D. Bastide, S. Crepey, S. Drapeau, and M. Tadese, Resolving a clearing member’s default: A Radner equilibrium approach, In minor revision at Mathematics and Financial Economics (2023). arXiv:2310.02608.
  7. G. Benmir, I. Jaccard, and G. Vermandel, Green asset pricing, In minor revision at Journal of Finance (2023).
  8. A. Bezat, E. Gobet, C. Guivarch, Y. Jiao, and E. Ndiaye, Optimal business model adaptation plan for a
    company under a transition scenario, preprint (2024). https://hal.science/hal-04682824/.
  9. J. Bullard, A. Grimaud, I. Salle, and G. Vermandel, Soft Landing and Inflation Scares, preprint (2024).
  10. F. Bourgey and S. De Marco, Small-time asymptotics for American options in local volatility models, preprint (2020).
  11. F. Bourgey, E. Gobet, and Y. Jiao, An Efficient SSP-based Methodology for Assessing Climate Risks of a
    Large Credit Portfolio, preprint (2024). https://hal.science/hal-04665712/.
  12. L. Chamakh, E. Gobet, and J.P. Lemor, Non-asymptotic comparison of covariance matrix inputs in dynamic minimum variance portfolio, in revision for Frontiers in Mathematical Finance (2022).
  13. L. Chamakh, E. Gobet, and W. Liu, Orlicz norms and concentration inequalities for α-heavy tailed random variables, in revision for Bernoulli (2022). https://hal.science/hal-03175697v3.
  14. C. Crofils, E. Gallic, and G. Vermandel, The Dynamic Effects of Weather Shocks on Agricultural Production, in revision for Journal of Environmental and Management. (2024). https://dx.doi.org/10.2139/ssrn.4724174.
  15. L. Dubois, J.G. Sahuc, and G. Vermandel, A General Equilibrium Approach to Carbon Permit Banking, in revision for Journal of Environmental and Management. (2024). https://dx.doi.org/10.2139/ssrn.4649914.
  16. S. Girard and J. El Methni, A refined extreme quantile estimator for Weibull tail-distributions., to appear in REVSTAT - Statistical Journal (2024). https://hal.science/hal-04022982v3.
  17. S. Girard, T. Opitz, and A. Usseglio-Carleve, ANOVEX: ANalysis of Variability for heavy-tailed EXtremes., preprint (2024). https://hal.science/hal-04200300.
  18. S. Girard and E. Gobet, Estimation of the largest tail-index and extreme quantiles from a mixture of heavy-tailed distributions, preprint (2022). https://theses.hal.science/MIAI/hal-03235031v1.
  19. S. Girard, E. Gobet, and J. Pachebat, Deep generative modeling of multivariate dependent extremes, preprint
    (2024). https://hal.science/hal-04700084/.
  20. E. Gobet, D. Metivier, and S. Parey, Interpretable Seasonal Hidden Markov Model for spatio-temporal stochastic rain generation in France, preprint (2024). https://hal.science/hal-04621349.
  21. E. Gobet, M. Lerasle, and D. Métivier, Mean estimation for Randomized Quasi Monte Carlo method, in revision for Journal of Complexity (2022). https://hal.archives-ouvertes.fr/hal-03631879v2.
  22. E. Gobet and M. Grangereau, Federated stochastic control of numerous heterogeneous energy storage systems, preprint (2021). https://hal.archives-ouvertes.fr/hal-03108611.
  23. Y. Gu and C. Rey, Deterministic computation of quantiles in a lipschitz framework, preprint (2024). https://hal.archives-ouvertes.fr/hal-04582532v1.
  24. C. Hillairet and A. Réveillac, On the chaotic expansion for counting processes., preprint (2022). https://arxiv.org/abs/2209.01972.
  25. E. Jondeau, G. Levieuge, J.G. Sahuc, and G. Vermandel, Environmental Subsidies to Mitigate Net-Zero Transition Costs., to appear in American Economic Journal: macroeconomics (2024).
  26. A. Mingone and C. Martini, Explicit no arbitrage domain for sub-SVIs via reparametrization, preprint (2021). https://arxiv.org/abs/2106.02418.
  27. T. Moins, J. Arbel, A. Dutfoy, and S. Girard, on the use of a local ˆR to improve MCMC convergence diagnostic, to appear in Bayesian Analysis (2024). https://arxiv.org/abs/2205.06694.
  28. G. Perrin, V. Chabridon, and J. Reygner, Enhancing Reliability Analysis with Limited Observations: A Statistical Framework for System Safety Margins, to appear in SIAM-JUQ (2024).
  29. G. Perrin and R. Le Riche, Bayesian optimization with derivatives acceleration., to appear in TMLR (2023). https://hal.science/hal-04259693.
  30. C. Poirier and G. Vermandel, Reallocation and Heterogeneous Elasticities in Production Networks., preprint (2024). https://dx.doi.org/10.2139/ssrn.4429307.
  31. J.G. Sahuc, F. Smets, and G. Vermandel, The new keynesian climate model., preprint (2024).