Marius Hofert

Assistant Professor

Marius HofertContact Information:
Marius Hofert

Marius Hofert's personal website

Research interests

Marius Hofert is mainly interested in the development of mathematical, statistical and computational tools in copula modeling. He is known for his work on high-dimensional and hierarchical copula models. Marius is also interested in various aspects of computational statistics in a broader sense and in applications to quantitative risk management. Furthermore, he is an active developer, contributor and maintainer of statistical software in R.

Education/biography

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

  • Embrechts, P. and Hofert, M. (2014), Statistics and Quantitative Risk Management for Banking and Insurance, Annual Review of Statistics and Its Application, 1, 492–514, doi:10.1146/annurev-statistics-022513-115631.
  • Hofert, M. and Mächler, M. (2013), A graphical goodness-of-fit test for dependence models in higher dimensions, Journal of Computational and Graphical Statistics, doi:10.1080/10618600.2013.812518.
  • 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.
  • Hering, C., Hofert, M., Mai, J.-F., and Scherer, M. (2010), Constructing nested Archimedean copulas with Lévy subordinators, Journal of Multivariate Analysis, 101, 1428–1433, doi:10.1016/j.jmva.2009.10.005.

Selected software

R package copula. A well-known R package which covers a wide range of tasks in copula modeling. View the current stable version of R package copula or see the latest development version of R package copula.

R package simsalapar. A package for conducting large-scale simulation studies in R (including error/warning catching, run time measurement, parallel computing, and more).

R package QRM. A package addressing tasks from Quantitative Risk Management. View the current stable version of R package QRM, or see the latest development version of R package QRM.

Affiliation: 
University of Waterloo
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