Marius Hofert

Department of Statistics and Actuarial Science, University of Waterloo

Contact

Photo Marius Hofert Marius Hofert, Dr. rer. nat.
Assistant Professor in Statistics (tenure-track)
Department of Statistics and Actuarial Science
Faculty of Mathematics
University of Waterloo
200 University Avenue West, Waterloo, ON, N2L 3G1
Office: M3 4207
Email: firstname dot lastname at uwaterloo dot ca
URL: Departmental website
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Google Scholar: My profile

 

 

Publications

Articles

[32]
Cambou, M., Lemieux, C. and Hofert, M. (2016), Quasi-random numbers for copula models, Statistics and Computing, (to appear).
[31]
Hofert, M. and Hornik, K. (2016), How we R on Android, Linux Journal, 6(266), 90–121.
[30]
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.
[29]
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.
[28]
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 (to appear), doi:10.1111/jori.12059.
[27]
Hofert, M. and Mächler, M. (2015), Parallel and other simulations in R made easy: An end-to-end study, Journal of Statistical Software (to appear).
[26]
Górecki, J., Hofert, M. and Holeňa, M. (2015), An Approach to Structure Determination and Estimation of Hierarchical Archimedean Copulas and Its Application in Bayesian Classification, Journal of Intelligent Information Systems, 1–39, doi:10.1007/s10844-014-0350-3.
[25]
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.
[24]
Grothe, O. and Hofert, M. (2014), Construction and sampling of Archimedean and nested Archimedean Lévy copulas, Journal of Multivariate Analysis, doi:10.1016/j.jmva.2014.12.004.
[23]
Hofert, M. and Mächler, M. (2013), A graphical goodness-of-fit test for dependence models in higher dimensions, Journal of Computational and Graphical Statistics, 23(3), 700–716, doi:10.1080/10618600.2013.812518.
[22]
Hofert, M. and McNeil, A. J. (2015), Subadditivity of Value-at-Risk for Bernoulli random variables, Statistics & Probability Letters, 98, 79–88, doi:10.1016/j.spl.2014.12.016.
[21]
Embrechts, P. and Hofert, M. (2013), A note on generalized inverses, Mathematical Methods of Operations Research, 77(3), 423–432, doi:10.1007/s00186-013-0436-7.
[20]
Embrechts, P. and Hofert, M. (2013), Statistical inference for copulas in high dimensions: A simulation study, ASTIN Bulletin, 43(2), 81–95, doi:10.1017/asb.2013.6.
[19]
Hofert, M. (2013), On Sampling from the Multivariate t Distribution, The R Journal, 5(2), 129–136, PDF.
[18]
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.
[17]
Hofert, M. and Vrins, F. (2013), Sibuya copulas, Journal of Multivariate Analysis, 114, 318–337, doi:10.1016/j.jmva.2012.08.007.
[16]
Hofert, M., Mächler, M., and McNeil, A. J. (2013), Archimedean Copulas in High Dimensions: Estimators and Numerical Challenges Motivated by Financial Applications, Journal de la Société Française de Statistique, 154(1), 25–63, PDF.
[15]
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.
[14]
Hofert, M. (2012), Sampling exponentially tilted stable distributions, ACM Transactions on Modeling and Computer Simulation, 22(1), doi:10.1080/00949655.2011.574632.
[13]
Hofert, M. and Wüthrich, M. V. (2012), Statistical Review of Nuclear Power Accidents, Asia-Pacific Journal of Risk and Insurance, 7(1), doi:10.1515/2153-3792.1157.
[12]
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.
[11]
Embrechts, P. and Hofert, M. (2011), Comments on: Inference in multivariate Archimedean copula models, TEST, 20(2), 263–270, doi:10.1007/s11749-011-0252-4.
[10]
Embrechts, P. and Hofert, M. (2011), Practices and issues in operational risk modeling under Basel II, Lithuanian Mathematical Journal, 51(2), 180–193, doi:10.1007/s10986-011-9118-4.
[09]
Hofert, M. (2011), Efficiently sampling nested Archimedean copulas, Computational Statistics & Data Analysis, 55, 57–70, doi:10.1016/j.csda.2010.04.025.
[08]
Hofert, M. and Mächler, M. (2011), Nested Archimedean Copulas Meet R: The nacopula Package, Journal of Statistical Software, 39(9), 1–20, http://www.jstatsoft.org/v39/i09/.
[07]
Hofert, M. and Scherer, M. (2011), CDO pricing with nested Archimedean copulas, Quantitative Finance, 11(5), 775–787, doi:10.1080/14697680903508479.
[06]
Durante, F., Hofert, M., and Scherer, M. (2010), Multivariate Hierarchical Copulas with Shocks, Methodology and Computing in Applied Probability, 12(4), 681–694, doi:10.1007/s11009-009-9134-6.
[05]
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.
[04]
Hofert, M. (2010), Modeling defaults with nested Archimedean copulas, Blätter der DGVFM, 31(2), 213–224, doi:10.1007/s11857-010-0123-1.
[03]
Hofert, M. and Kohm, M. (2010), Scientific Presentations with LATEX, The PracTEX Journal, 2, URL.
[02]
Hofert, M., Scherer, M., and Zagst, R. (2010), Modeling the evolution of implied CDO correlations, Financial Markets and Portfolio Management, 24(3), 289–308, doi:10.1007/s11408-010-0136-8.
[01]
Hofert, M. (2008), Sampling Archimedean copulas, Computational Statistics & Data Analysis, 52, 5163–5174, doi:10.1016/j.csda.2008.05.019.

Book Contributions

[02]
Embrechts, P. and Hofert, M. (2012), Risk Measures and Dependence Modeling, Handbook of Insurance, ed. by Dionne, G., Springer.
[01]
Hofert, M. (2010), Construction and sampling of nested Archimedean copulas, Copula Theory and Its Applications, Proceedings of the Workshop held in Warsaw 25–26 September 2009, ed. by F. Durante, W. Härdle, P. Jaworski, and T. Rychlik, Springer, 147–160, doi:10.1007/978-3-642-12465-5_7.

Books

[01]
Hofert, M. (2010), Sampling Nested Archimedean Copulas with Applications to CDO Pricing, PhD thesis, Südwestdeutscher Verlag für Hochschulschriften AG & Co. KG, ISBN 978-3-8381-1656-3.

Miscellaneous

[01]
Hofert, M. and Schepsmeier, U. (2014), Guidelines for Statistical Projects: Coding and Typography, PDF.

 

Quantitative Risk Management

 

Software