CMAP
PUBLICATIONS
PUBLISHED AND ACCEPTED PAPERS
2024
- Allouche, M., Gobet, E., Lage, C. et al. Structured dictionary learning of rating migration matrices for credit risk modeling. Comput Stat (2024). https://doi.org/10.1007/s00180-023-01449-y
2023
- 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. - M. Allouche, S. Girard, and E. Gobet, Estimation of extreme
quantiles from heavy-tailed distributions with
neural networks, Statistics and Computing (2023). https://hal.science/hal-03751980. - 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. - J. Arifovic, A. Grimaud, I. Salle, and G. Vermandel, Social
Learning and Monetary Policy
at the Effective Lower Bound, Journal of Money, Credit and Banking (2023), 79. https://dx.doi.org/10.2139/ssrn.3728108. - 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. - 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. - 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. - 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. - 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. - 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. - 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.
2022
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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
- 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.
- 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.
- 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.
- 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)
- C. Albanese, S. Cr´epey, and S. Iabichino, Quantitative Reverse stress testing, Bottom Up, preprint (2023). https://hal.science/hal-03910136/.
- M. Allouche, E. Gobet, C. Lage, and E. Mangin, Structured Dictionary Learning of Rating Migration Matrices for Credit Risk Modeling, in revision for Computational Statistics (2022). https://hal.archives-ouvertes.fr/hal-03715954v1.
- D. Barrera, S. Crepey, E. Gobet, H.-D. Nguyen, and B. Saadeddine, Learning value-at-risk and expected shortfall, preprint (2022). https://arxiv.org/abs/2209.06476.
- D. Barrera and E. Gobet, Generalization bounds for nonparametric regression with β-mixing samples, preprint (2021). https://hal.archives-ouvertes.fr/hal-03311506.
- D. Bastide, S. Crepey, S. Drapeau, and M. Tadese, XVA analysis of centrally cleared trading in a one-period model, preprint (2021). https://arxiv.org/pdf/2202.03248.pdf.
- , Resolving a clearing member’s default: A Radner equilibrium approach, preprint (2023).
- F. Bourgey, S. De Marco, P.K. Fritz, and P. Pigato, Local volatility under rough volatility, preprint (2022). https://arxiv.org/abs/2204.02376.
- 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).
- 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.
- C. Crofils, E. Gallic, and G. Vermandel, The Dynamic Effects of Weather Shocks on Agricultural Production, submitted (2023).
- C. Escribe, J. Garnier, and E. Gobet, A Mean Field Game Model for Renewable Investment under Long-Term Uncertainty and Risk Aversion, preprint (2023). https://hal.archives-ouvertes.fr/hal-04055421.
- 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.
- E. Gobet and W. Wang, Improved convergence rate for Reflected BSDEs by penalization method, preprint (2023). https://hal.archives-ouvertes.fr/hal-04020304v2.
- A. Grimaud, I. Salle, and G. Vermandel, Social Learning Expectations : Microfoundations and a Dynare Toolbox, preprint (2023). https://dx.doi.org/10.2139/ssrn.4437177.
- Y. Jiao and N. Kolliopoulos, Well-posedness of a system of SDEs driven by jump random measures, in revision for Stochastics and Dynamics (2023). https://arxiv.org/abs/2102.03918.
- A. Mingone and C. Martini, Explicit no arbitrage domain for sub-SVIs via reparametrization, preprint (2021). https://arxiv.org/abs/2106.02418.
- T. Moins, J. Arbel, A. Dutfoy, and S. Girard, on the use of a local ˆR to improve MCMC convergence diagnostic, preprint (2023). https://arxiv.org/abs/2205.06694.
- C. Poirier and G. Vermandel, Reallocation Dynamics in Production Networks With Heterogeneous Elasticities, submitted (2023), 60. https://dx.doi.org/10.2139/ssrn.4429307.