CMAP
PUBLICATIONS
PUBLISHED AND ACCEPTED PAPERS
2022
- Allouche
M. , Girard S., and Gobet E., Generative
model for fBm with deep ReLU neural networks, to appear
in Journal of Complexity (2022). - Allouche M. , Girard S., and Gobet E., EV-GAN: Simulation of extreme events with ReLU neural networks, to appear in Journal of Machine Learning Research (2022).
- Chataigner, M., Cousin, A., Crépey, S., Dixon, M., and Gueye D. (2022). Beyond surrogate modeling: Learning the local volatility via shape constraints. accepted for publications, SIAM Journal of Financial Mathematics.
- Cousin
A., Jiao Y., Robert C.Y., and Zerbib O.D., Optimal
asset allocation subject to withdrawal risk and
solvency constraints, Risks 10 (2022), no. 1, 15. - Girard
S., Stupfler G., and Usseglio-Carleve A., Functional
estimation of extreme conditional expectiles.,
Econometrics and Statistics 49 (2022), no. 1, 78–115. - Girard S., Stupfler G., and Usseglio-Carleve A., Nonparametric extreme conditional expectile estimation, Scandinavian Journal of Statistics 49 (2022), no. 1, 78–115.
- Girard S., Stupfler G., and Usseglio-Carleve A.,On automatic bias reduction for extreme expectile estimation, to appear in Statistics and Computing (2022).
- Y.
Jiao, C. Ma, S. Scotti, and C. Zhou, The
Alpha-Heston stochastic volatility model, Mathematical
Finance
31 (2021), no. 3, 943–978.
2021
- Alabanese
A., Crépey S., et Iabichino S. (2021a) Capital and collateral
simulation for reverse stress testing. In T. Bellini, M.,
Eichborn, and D., Mayenberg (EDs), Reverse Stress Testing in
Banking, Chapter 17, 349-359. De Gruyter.
- Daouia, A., Girard, S., et Stupfler, G. (2021) ExpectHill estimation, extreme risk and heavy tails. J. Econometrics, 221(1), pp.97-117. https://doi.org/10.1016/j.jeconom.2020.02.003
- Drees, H., Sabourin A. (2021) Principal Component Analysis for Multivariate Extremes. Electron. J. Statist. 15(1) pp. 908-943. https://doi.org/10.1214/21-EJS1803
- Gardes, L.,, Girard, S. (2021). On the estimation of the variability in the distribution tail. Test, 1-24. https://doi.org/10.1007/s11749-021-00754-2
- Girard,
S., Stupfler, G., and Usseglio-Carleve A. (2021a). Extreme
conditional expectile estimation in heavy-tailed
heteroscedastic regression models, Annals of Statistics,
Institute of mathematical Statistics.
2020
- Ahmad, A.A., Deme, E.H., Diop, A., Girard, S., Usseglio-Carleve, A. (2020). Estimation of extreme quantiles from heavy-tailed distributions in a location- dispersion regression model. Electron. J. Stat., 14(2), pp.4421-4456. https://doi.org/10.1214/20-EJS1779
- Albanese, C., Armenti, Y., Crepey, S. (2020). XVA metrics for CCP optimization. Stats. Risk Model., 37(1-2), pp.25-53. https://doi.org/10-1515/strm-2017-0034
- Albanese, C., Crepey, S., Iabichino, S. (2020). Reverse Stress Testing. To appear in Reverse Stress Testing in Banking. https://dx.Doi.org/10.2139/ssrn.3544866
- Albert, C., Dutfoy, A., Girard, S. (2020). Asymptotic behavior of the extrapolation error associated with the estimation of extreme quantiles. Extremes, 23(2), pp.349-380. https://doi.org/10.1007/s10687-019-00370-2
- Arnaudon, M., Del Moral, P. (2020). A second order analysis of McKean-Vlasov semigroups. Ann. Appl. Probab., 30(6), pp.2613-2664. https://doi.org/10.1214/20-AAP1568
- Bourgey, F., De Marco, S., Gobet E., Zhou, A. (2020). Multilevel Monte-Carlo methods and lower-upper bounds in initial margin computations. Monte Carlo Methods Appl., 26(2), pp. 131-161. https://doi.org/10.1515/mcma-2020-2062
- Bourgey, F., Gobet, E., Rey, C. (2020). Metamodel of a large credit risk portfolio in the Gaussian copula model. SIAM J., Financial Math., 11(4), pp. 1098-1136. https://doi.org/10-1137/19M12922084
- Chamakh, L., Gobet, E., Stazhynski, U. (2020). Orlicz random Fourier features. J. Mach. Learn. Res, 21, paper No.145, 37.
- Crepey, S., Fort, G., Gobet, E., Stazhynski, U. (2020). Uncertainty quantification for stochastic approximation limits using chaos expansion. SIAM/ASA J. Uncertain Quantif., 8(3), pp. 1061-1089. https://doi.org/10.1137/18M1178517
- Crepey, S., Dixon, M.F. (2020). Gaussian process regression for derivative portfolio modeling and application to credit valuation adjustement computations. Journal of computational Finance, 24(1). https://doi.org/10.21314/JCF.2020.386
- Daouia, A., Girard, S., Stupfler, G. (2020). Tail expectile process and risk assessment. Bernouilli, 26(1), pp. 531-556. https://doi.org/10.3150/19-BEJ1137.
- Gardes, L., Girard, S., Stupfler, G. (2020). Beyond tail median and conditional tail expectation: Extreme risk estimation using tail Lp-optimization. Scand. J. Stat., 47 (3), pp. 922-949. https://doi.org/10/1111/sjos.12433
- Girard, S., Stupfler, G., Usseglio-Carleve, A. (2020). Nonparametric extreme conditional expectile estimation. Scandinavian Journal of Statistics. https://doi.org/10.1111/sjos.12502
2019
- Ahmad, A.A., Deme, E.H., Diop, A., Girard, S. (2019). Estimation of the tail-index in a conditional location-scale family of heavy-tailed distributions. Dependence Modeling, De Gruyter, 2019, 7, pp.349-417. https://doi.org/10.1515/demo-2019-0021
- Arbel, J., Crispino, M., Girard, S. (2019). Dependence properties and Bayesian inference for asymmetric multivariate corpulas. J.Multivariate Anal., 174, 104530, 20. https://doi.org/10.1016/j.jmva.2019.06.008
- Arnaudon, M., Del Moral, P. (2019). A variational approach to nonlinear and interacting diffusions, Stochastic Analysis and Applications, 37 (5), pp717-748. https://doi.org/10.1080/07362994.2019.1609985
- Barrera, D., Gobet, E., (2019). Quantitative bounds for concentration of measure inequalities and empirical regression: the independent case, Journal of Complexity 52, pp. 45-81. https://doi.org/10.1016/j.jco.2019.003
- Del Moral, P., Singh S.S., (2019). A forward-backward stochastic analysis of diffusion flows. [Research Report] INRIA
WORKSHOP PRESENTATION
2022
- M.
Chataigner, A. Cousin, S. Crépey, M. Dixon, and D. Gueye, Beyond
surrogate modeling: Learning the
local volatility via shape constraints, SIAM Conference on Financial Mathematics and Engineering, Jun. 2022..
2021
- C.
Albanese, S. Crépey, and S. Iabichino, Reverse stress testing,
Journée de rentrée du LPSM, Université de
Paris, Sep. 2021. - C. Albanese, S. Crépey, and S. Iabichino, Reverse stress testing, Reverse Stress testing 3.0 - Third Annual Masterclass, Nov. 2021.
- M.
Allouche, S. Girard, and E. Gobet, Generative model
for fbm with deep ReLU neural networks, Bernoulli-IMS 10th World
Congress in Probability and Statistics, Jul. 2021.
- M.
Allouche, S. Girard, and E. Gobet, On the approximation of
extreme quantiles with ReLU neural networks,
12th International Conference on Extreme Value Analysis, Jun. 2021. - M. Bousebata, G. Enjolras, and S. Girard, Extreme partial least-squares regression, 12th International Conference on Extreme Value Analysis, Jun. 2021.
- J. El-Methni and S. Girard, A bias-reduced version of the Weissman extreme quantile estimator, 12th International Conference on Extreme Value Analysis, Jun. 2021.
- A.
Usseglio-Carleve, S. Girard, and G. Stupfler, Extreme expectile
regression: theory and applications, 12th
International Conference on Extreme Value Analysis, Jun. 2021.
2020
- Aubin-Frankowski, P.-C., Szabo, Z. (2020). Hard shape constrained kernel machines. Advances in Neural Information Processing Systems (neurIPS-2020), Dec 2020, Vancouver (Virtual), Canada.
- C. Albanese, S. Crépey, and S. Iabichino, Reverse stress testing, StressTest-2021 : International Workshop on Stress Test and Risk Management, Paris, Dec. 20
2019
- Girard, S., Stupfler G., Estimation of high-dimensional extreme conditional expectiles. CRoNoS & MDA 2019 - Final CRoNoS meeting and 2nd workshop on Multivariate Data Analysis, Apr 2019, Limassol, Cyprus
- Girard, S., Stupfler G., Usseglio-Carleve A., Nonparametric extreme conditional expectile estimation. EVA(2019) -11th International Conference on Extreme Value Analysis, Jul 2019, Zagreb, Croatia
- Albert, C., Dutfoy, A., Girard, S. (2019). Etude l'erreur relative à l'exploitation associée à l'estimateur de Weissmann pour les quantiles extrëmes, JdS 2019 - 51ème Journées de Statistique, Société Française de Statistique, Jun 2019, Nancy, France. pp.1-6.
PREPRINTS
2022
- Albanese, C., Crépey, S., an Iabichino, S., (2021b). Reverse stress testing.
- Allouche M. , El-methni J., Girard S., (2022). A refined Weissman estimator for extreme quantiles.
- Barrera D., Crépey, S., Gobet, E., Nguyen, H.-D. and Saadeddine B. (2022) Learning value-at-risk and expected shortfall.
- Bastide, D., Crépey, S., Drapeau, S., and Tadese, M., (2022). XVA analysis of centrally cleared trading in a one-period model.
- Bousebata M., Enjolras, G., and Girard, S., (2022). Extreme Partial Least-Squares regression.
- Jiao
Y., Salhi Y., and Wang S., (2022) Dynamic Bivariate
Mortality Modelling.
2021
- Aubin-Frankowski, P.-C., and Szabo Z., (2021). Handling hard affine SDP shape contraints in RKHSs, Technical reports.
- Bourgey,
F., De Marco, S. (2021). Multilevel Monte Carlo simulation for
VIX options in the rough Bergomi model.
- Bourgey,
F., De Marco, S., Gobet, E. (2021). Weak approximations and VIX
options prices expansions in rough Bergomi models.
- Bourgey, F., Gobet, E., Rey, C. (2021). A comparative study of polynomial-type chaos expansions for indicator functions.
- Bourgey, F., Gobet, E., Rey, C. (2021). Polynomial chaos expansion and meta-model of large sums of dependent random variables.
- Girard, S., Stupfler, G., Usseglio-Carleve, A., (2021). Extreme L^p - quantile kernel regression.
- Lambert,
A., Parekh, S., Szabo, Z., and Alché-Buc, F., (2021). Emotion
transfert using vector-valued infinite task learning. Technical
reports, submitted to a special track of Machine Learning.
2020
- Aubin-Frankowski,
P.-C., and Szabo Z.,(2020). Hard Shape constrained
Kernel Machines.
- Bourgey,
F., De Marco, S. (2020). Small-time asymptotic for American
options in local volatility models.
- De
Marco, S. (2020) On the harmonic mean
representation of the implied volatility
- Del
Moral, P., Singh, S.S. (2020). Backward
It{ô}-Ventzell and stochastic interpolation formulae.
arXiv:1906.09145v4
2019
- Barrera, D., Gobet, E. (2019). Generalization bounds for nonparametric regression with B-mixing samples.