CMAP
PUBLICATIONS
PUBLISHED AND ACCEPTED PAPERS
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.
- Chataigner,
M., Cousin, A., Crépey, S., Dixon, M., and Gueye D. (2021). Beyond
surrogate modeling: Learning the local volatility via shape
constraints. accepted for publications, SIAM Journal of
Financial Mathematics.
- 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.
- Girard, S., Stupfler, G., and Usseglio-Carleve A. (2021b). Nonparametric extreme conditional expectile estimation, To appear in Scandinavian Journal of Statistics.
- Girard,
S., Stupfler, G., and Usseglio-Carleve A. (2021c). Functional
estimation of extreme conditional expectiles, To appear in
Econometrics and 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
- 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.
- 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.
PREPRINTS
2021
- Albanese,
C., Crépey, S., an Iabichino, S., (2021b). Reverse stress
testing.
- Allouche, M., Girard, S., and Gobet, E., (2021). Generative model for fBm with deep ReLU neural networks.
- Allouche,
M., Girard, S., and Gobet, E., (2021). EV-GAN:
Simulation of extreme events with ReLU neural networks.
- Aubin-Frankowski, P.-C., and Szabo Z., (2021). Handling hard affine SDP shape contraints in RKHSs, Technical reports.
- Barrera
D., Crépey, S., Gobet, E., and Nguyen, H.-D. (2021) Learning
value-at-risk and expected shortfall.
- Bastide,
D., Crépey, S., Drapeau, S., and Tadese, M., (2021). XVA
analysis of centrally cleared trading in a one-period model.
- 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.
- Bousebata M., Enjolras, G., and Girard, S., (2021). Extreme Partial Least-Squares regression.
- Girard, S., Stupfler, G., Usseglio-Carleve, A., (2021). On automatic bias reduction for extreme expectile estimation.
- 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.