Associate Professor

Marius HofertContact Information:
Marius Hofert

Marius Hofert's personal website

Research interests

Marius Hofert's research interests are Computational Statistics and Data Science (data visualization, parallel computing, software development in R), Dependence Modeling with Copulas (high dimensional problems, hierarchical models, random number generation, computational aspects, graphical approaches) and Quantitative Risk Management (risk aggregation, risk measures, computational challenges).  Furthermore, he is an active developer, contributor and maintainer of statistical software in R.


After completing a diploma in Mathematics and Management at University of Ulm and a masters degree in Mathematics at Syracuse University, Marius Hofert obtained his PhD in Mathematics from University of Ulm in 2010. He then held a postdoctoral research position (Willis Research Fellow) at RiskLab, ETH Zurich, under the supervision of Professor Paul Embrechts. After a guest professorship (W2) in the Department of Mathematics at Technische Universität München and a visiting assistant professorship in the Department of Applied Mathematics at University of Washington, he joined the Department of Statistics and Actuarial Science at University of Waterloo in July 2014.

Selected publications

  • Hofert, M., Oldford, R. W. (2017), Visualizing Dependence in High-dimensional Data: An Application to S&P 500 Constituent Data, Econometrics and Statistics, (in press), doi:10.1016/j.ecosta.2017.03.007.
  • Hofert, M., Memartoluie, A., Saunders, D., Wirjanto, T. (2017), Improved Algorithms for Computing Worst Value-at-Risk, Statistics & Risk Modeling, 34(1-2), 13–31, doi:10.1515/strm-2015-0028.
  • Cambou, M., Lemieux, C. and Hofert, M. (2016), Quasi-random numbers for copula models, Statistics and Computing, 27(5), 1307–1329, doi:10.1007/s11222-016-9688-4.
  • Hofert, M. and Hornik, K. (2016), How we R on Android, Linux Journal, 6(266), 90–121.
  • Hofert, M. and Mächler, M. (2016), Parallel and other simulations in R made easy: An end-to-end study, Journal of Statistical Software, 69(4), doi:10.18637/jss.v069.i04.
  • Embrechts, P., Hofert, M. and Wang. R. (2016), Bernoulli and Tail-Dependence Compatibility, The Annals of Applied Probability, 26(3), 1636–1658, doi:10.1214/15-AAP1128.
  • Chavez-Demoulin, V., Embrechts, P. and Hofert, M. (2015), An extreme value approach for modeling operational risk losses depending on covariates, Journal of Risk and Insurance 83(3), 735–776, doi:10.1111/jori.12059.
  • Hofert, M. (2013), On Sampling from the Multivariate t Distribution, The R Journal, 5(2), 129–136.
  • Hofert, M. and Pham, D. (2013), Densities of nested Archimedean copulas, Journal of Multivariate Analysis, 118, 37–52, doi:10.1016/j.jmva.2013.03.006.
  • Hofert, M. and Vrins, F. (2013), Sibuya copulas, Journal of Multivariate Analysis, 114, 318–337, doi:10.1016/j.jmva.2012.08.007.
  • Hofert, M. (2012), A stochastic representation and sampling algorithm for nested Archimedean copulas, Journal of Statistical Computation and Simulation, 82(9), 1239–1255, doi:10.1080/00949655.2011.574632.
  • Hofert, M., Mächler, M., and McNeil, A. J. (2012), Likelihood inference for Archimedean copulas in high dimensions under known margins, Journal of Multivariate Analysis, 110, 133–150, doi:10.1016/j.jmva.2012.02.019.
  • Hofert, M. (2011), Efficiently sampling nested Archimedean copulas, Computational Statistics & Data Analysis, 55, 57–70, doi:10.1016/j.csda.2010.04.025.
  • Hofert, M. and Scherer, M. (2011), CDO pricing with nested Archimedean copulas, Quantitative Finance, 11(5), 775–787, doi:10.1080/14697680903508479.
University of Waterloo
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