Associate Professor of Statistics Research interests
  • Risk mitigation
  • Extreme value analysis 
  • Semi- and non-parametric statistics
  • M-estimation 
  • Missing data frameworks 
  • Hidden Markov models
Bureau 255 Téléphone +33 (0)2 99 05 32 55 Email Adresse ENSAI
Campus de Ker Lann
51 Rue Blaise Pascal
BP 37203
35172 BRUZ Cedex

My main area of research is extreme value analysis. Much of my recent work in this direction has focused on how to measure and estimate extreme risk, particularly in actuarial and financial contexts.

My other interests include nonparametric statistics and especially regression methods, as well as in the extreme value analysis of missing data models.

I am also ENSAI's Sustainable Development Officer and I oversee the double MSc degrees ENSAI awards. If you are interested in pursuing a double degree, please feel free to send me an email! 

My full academic CV, containing information about my qualifications, work experience, past and present teaching activities and research, can be found here. My personal webpage can be found here, and you can also follow my Twitter feed here.



(Most recent first)

  1. Girard, S., Stupfler, G., Usseglio-Carleve, A. (2022). On automatic bias reduction for extreme expectile estimation, Statistics and Computing, to appear.
  2. Davison, A.C., Padoan, S.A., Stupfler, G. (2022). Tail risk inference via expectiles in heavy-tailed time seriesJournal of Business and Economic Statistics, to appear.
  3. Kaibuchi, H., Kawasaki, Y., Stupfler, G. (2022). GARCH-UGH: A bias-reduced approach for dynamic extreme Value-at-Risk estimation in financial time seriesQuantitative Finance 22(7): 1277-1294.
  4. Padoan, S.A., Stupfler, G. (2022). Joint inference on extreme expectiles for multivariate heavy-tailed distributions, Bernoulli 28(2): 1021-1048.
  5. Girard, S., Stupfler, G., Usseglio-Carleve, A. (2022). Nonparametric extreme conditional expectile estimationScandinavian Journal of Statistics 49(1): 78-115.
  6. Girard, S., Stupfler, G., Usseglio-Carleve, A. (2022). Functional estimation of extreme conditional expectilesEconometrics and Statistics 21: 131-158. 
  7. Stupfler, G., Usseglio-Carleve, A. (2021). Composite bias-reduced Lp-quantile-based estimators of extreme quantiles and expectiles, Canadian Journal of Statistics, to appear. 
  8. Thompson, A., Southon, N., Fern, F., Stupfler, G., Leach, R. (2021). Efficient empirical determination of maximum permissible error in coordinate metrology, Measurement Science and Technology 32: 105013.
  9. Girard, S., Stupfler, G., Usseglio-Carleve, A. (2021). Extreme conditional expectile estimation in heavy-tailed heteroscedastic regression models, Annals of Statistics 49(6): 3358-3382.
  10. Falk, M., Stupfler, G. (2021). The min-characteristic function: characterizing distributions by their min-linear projections, Sankhya A 83(1): 254-282.
  11. Church, O., Derclaye, E., Stupfler, G. (2021). Design litigation in the EU Member States: Are overlaps with other intellectual property rights and unfair competition problematic and are SMEs benefitting from the EU design legal framework?, European Law Review 46(1): 37-60. The published version (behind the Westlaw paywall) is available here.
  12. Daouia, A., Gijbels, I., Stupfler, G. (2021). Extremile regression, Journal of the American Statistical Association, to appear.
  13. Daouia, A., Girard, S., Stupfler, G. (2021). ExpectHill estimation, extreme risk and heavy tails, Journal of Econometrics 221(1): 97-117.
  14. Gardes, L., Girard, S., Stupfler, G. (2020). Beyond tail median and conditional tail expectation: extreme risk estimation using tail Lp-optimisation, Scandinavian Journal of Statistics 47(3): 922-949.
  15. Mitchell, E.G., Crout, N.M.J., Wilson, P., Wood, A.T.A., Stupfler, G. (2020). Operating at the extreme: estimating the upper yield boundary of winter wheat production in commercial practice, Royal Society Open Science 7(4): 191919.
  16. Daouia, A., Girard, S., Stupfler, G. (2020). Tail expectile process and risk assessment, Bernoulli 26(1): 531-556. 
  17. Stupfler, G. (2019). On a relationship between randomly and non-randomly thresholded empirical average excesses for heavy tails, Extremes 22(4): 749-769.
  18. Daouia, A., Gijbels, I., Stupfler, G. (2019). Extremiles: A new perspective on asymmetric least squares, Journal of the American Statistical Association 114(527): 1366-1381.
  19. Falk, M., Stupfler, G. (2019). On a class of norms generated by nonnegative integrable distributions, Dependence Modeling 7(1): 259-278.
  20. Stupfler, G. (2019). On the study of extremes with dependent random right-censoring, Extremes 22(1): 97-129.
  21. Church, O., Derclaye, E., Stupfler, G. (2019). An empirical analysis of the design case law of the EU Member States, International Review of Intellectual Property and Competition Law 50(6): 685-719.
  22. Gardes, L., Stupfler, G. (2019). An integrated functional Weissman estimator for conditional extreme quantiles, REVSTAT: Statistical Journal 17(1): 109-144.
  23. Daouia, A., Girard, S., Stupfler, G. (2019). Extreme M-quantiles as risk measures: From L1 to Lp optimization, Bernoulli 25(1): 264-309. A version of the supplementary material containing corrections to certain proofs and the statement of Lemma 1 (the main results of the paper remain valid) is available here. [With thanks to Antoine Usseglio-Carleve]
  24. El Methni, J., Stupfler, G. (2018). Improved estimators of extreme Wang distortion risk measures for very heavy-tailed distributions, Econometrics and Statistics 6: 129-148.
  25. Daouia, A., Girard, S., Stupfler, G. (2018). Estimation of tail risk based on extreme expectiles, Journal of the Royal Statistical Society: Series B 80(2): 263-292.
  26. Stupfler, G., Yang, F. (2018). Analyzing and predicting CAT bond premiums: a Financial Loss premium principle and extreme value modeling, ASTIN Bulletin 48(1): 375-411.
  27. El Methni, J., Stupfler, G. (2017). Extreme versions of Wang risk measures and their estimation for heavy-tailed distributions, Statistica Sinica 27(2): 907-930.
  28. Girard, S., Stupfler, G. (2017). Intriguing properties of extreme geometric quantiles, REVSTAT: Statistical Journal 15(1): 107-139.
  29. Falk, M., Stupfler, G. (2017). An offspring of multivariate extreme value theory: the max-characteristic function, Journal of Multivariate Analysis 154: 85-95.
  30. Stupfler, G. (2016). On the weak convergence of the kernel density estimator in the uniform topology, Electronic Communications in Probability 21(17): 1-13.
  31. Stupfler, G. (2016). Estimating the conditional extreme-value index under random right-censoring, Journal of Multivariate Analysis 144: 1-24.
  32. Girard, S., Stupfler, G. (2015). Extreme geometric quantiles in a multivariate regular variation framework, Extremes 18(4): 629-663.
  33. Meintanis, S.G., Stupfler, G. (2015). Transformations to symmetry based on the probability weighted characteristic function, Kybernetika 51(4): 571-587.
  34. Goegebeur, Y., Guillou, A., Stupfler, G. (2015). Uniform asymptotic properties of a nonparametric regression estimator of conditional tails, Annales de l'Institut Henri Poincaré (B): Probability and Statistics 51(3): 1190-1213.
  35. Gardes, L., Stupfler, G. (2015). Estimating extreme quantiles under random truncation, TEST 24(2): 207-227. An erratum, also published in TEST, is available here.
  36. Guillou, A., Loisel, S., Stupfler, G. (2015). Estimating the parameters of a seasonal Markov-modulated Poisson process, Statistical Methodology 26: 103-123.
  37. Stupfler, G. (2014). On the weak convergence of kernel density estimators in Lp spaces, Journal of Nonparametric Statistics 26(4): 721-735.
  38. Gardes, L., Stupfler, G. (2014). Estimation of the conditional tail index using a smoothed local Hill estimator, Extremes 17(1): 45-75.
  39. Girard, S., Guillou, A., Stupfler, G. (2014). Uniform strong consistency of a frontier estimator using kernel regression on high order moments, ESAIM: Probability and Statistics 18: 642-666.
  40. Stupfler, G. (2013). A moment estimator for the conditional extreme-value index, Electronic Journal of Statistics 7: 2298-2343.
  41. Guillou, A., Loisel, S., Stupfler, G. (2013). Estimation of the parameters of a Markov-modulated loss process in insurance, Insurance: Mathematics and Economics 53(2): 388-404.
  42. Girard, S., Guillou, A., Stupfler, G. (2013). Frontier estimation with kernel regression on high order moments, Journal of Multivariate Analysis 116: 172-189.
  43. Girard, S., Guillou, A., Stupfler, G. (2012). Estimating an endpoint with high order moments in the Weibull domain of attraction, Statistics and Probability Letters 82(12): 2136-2144.
  44. Girard, S., Guillou, A., Stupfler, G. (2012). Estimating an endpoint with high-order moments, TEST 21(4): 697-729.