Klaus Herrmann

Klaus Herrmann

Department of Mathematics
Faculty of science
Université de Sherbrooke
2500, boul. de l'Université
Sherbrooke (Québec)
Canada J1K 2R1

Publications

Journal Publications

  1. Coblenz, M., Grothe, O., Herrmann, K., Hofert, M. (2022). Smoothed bootstrapping of copula functionals. Electronic Journal of Statistics, 16(1), 2550–2606.
  2. Herrmann, K., Hofert, M., Mailhot, M. (2020). Multivariate geometric tail- and range-value-at-risk. ASTIN Bulletin: The Journal of the IAA, 50(1), 265–292.
  3. Gijbels, I., Herrmann, K. (2018). Optimal Expected-Shortfall Portfolio Selection With Copula-Induced Dependence. Applied Mathematical Finance, 25(1), 66–106.
  4. Herrmann, K., Hofert, M., Mailhot, M. (2018). Multivariate geometric expectiles. Scandinavian Actuarial Journal, 2018(7), 629–659.
  5. Gijbels, I., Herrmann, K. (2014). On the distribution of sums of random variables with copula-induced dependence. Insurance: Mathematics and Economics, 59, 27–44.

Book Chapters

  1. Beirlant, J., Schoutens, W., De Spiegeleer, J., Reynkens, T., Herrmann, K. (2016). Hunting for Black Swans in the European Banking Sector Using Extreme Value Analysis. In: Kallsen, J., Papapantoleon, A. (eds) Advanced Modelling in Mathematical Finance: In Honour of Ernst Eberlein. Springer Proceedings in Mathematics & Statistics, vol 189, Springer, 147–166.
  2. Beirlant, J., Herrmann, K., Teugels, J.L. (2016). Estimation of the Extreme Value Index. In: Longin, F. (ed) Extreme Events in Finance: A Handbook of Extreme Value Theory and its Applications. John Wiley & Sons, Inc. , 97–115.
  3. Gijbels, I., Herrmann, K., Sznajder, D. (2015). Flexible and Dynamic Modeling of Dependencies via Copulas. In: Antoniadis, A., Poggi, J.M., Brossat, X. (eds) Modeling and Stochastic Learning for Forecasting in High Dimensions. Lecture Notes in Statistics, vol 217, Springer, 117–146.

Other

  1. Herrmann, K. (2020). Conference report of the 2019 CRM-SSC address ‘Tales of tails, tiles and ties in dependence modeling’ by Johanna G. Nešlehová. Le Bulletin du CRM, vol 26(1). Centre de recherches mathématiques, 14–14.
  2. Gijbels, I., Herrmann, K., Verhasselt, A. (2013). Discussion on ‘Large covariance estimation by thresholding principal orthogonal components’. Journal of the Royal Statistical Society, Series B, vol 75. Wiley-Blackwell, 662–663.