Marius Hofert

Department of Statistics and Actuarial Science

The University of Hong Kong

Contact Positions Publications QRM Software

Contact

Photo Marius Hofert Marius Hofert, Dr. rer. nat.
Associate Professor of Statistics
Department of Statistics and Actuarial Science
Faculty of Science
The University of Hong Kong
Room 228, Run Run Shaw Building
Office: M3 4207
Email: mhofert at hku dot hk
URL: Departmental website
Google Maps: How to find me
Google Scholar: My profile

 

My research interests are dependence modeling, computational statistics, data science and quantitative risk management. I am an Associate Editor of:

 

I am also the Director of HKU's Master of Data Science (MDASC) program and Academic Adviser in Risk Management. For any inquiries concerning MDASC, please contact our supporting staff Clara Lian or Jacey Yeung directly (see here).

 

Positions

I have a PhD position available. I am looking for a mathematically strong, independent student interested in probability or statistics. Knowledge of Git and LaTeX is required. If you are interested, please send me your CV, transcripts (Bachelor's, Master's; including a GPA for each degree) and some of your written work (for example your Master's thesis).

 

Publications

Articles

[72]
Liu, Y., Hofert, M. (2024), Policy optimization by looking ahead for model-based offline reinforcement learning, 41st IEEE Conference on Robotics and Automation (ICRA 2024), accepted.
[71]
Liu, Y., Hofert, M. (2024), Implicit and explicit policy constraints for offline reinforcement learning, Proceedings of Machine Learning Research, accepted.
[70]
Herrmann, K., Hofert, M., Sadr, N. (2023), Index-mixed copulas, submitted (see also here).
[69]
Koike, T., Hofert, M. (2023), Comparison of correlation-based measures of concordance in terms of asymptotic variance, Journal of Multivariate Analysis, accepted.
[68]
Górecki, J., Hofert, M. (2023), Composite pseudo-likelihood estimation for pair-tractable copulas such as Archimedean, Archimax and related hierarchical extensions, Journal of Statistical Computation and Simulation, 93(13), 2321–2355, doi.org/10.1080/00949655.2023.2180511.
[67]
Hofert, M. (2023), Assessing ChatGPT's proficiency in quantitative risk management, Risks, 11(9), 166, doi.org/10.3390/risks11090166.
[66]
Hofert, M. (2023), Correlation pitfalls with ChatGPT: Would you fall for them?, Risks, 11(7), 115, doi.org/10.3390/risks11070115.
[65]
Hofert, M. (2023), The philosophy of copula modeling: A conversation with ChatGPT, Journal of Data Science, 21(4), 619–637, doi.org/10.6339/23-JDS1114.
[64]
Hofert, M., Prasad, A., Zhu, M. (2023), Dependence model assessment and selection with DecoupleNets, Journal of Computational and Graphical Statistics, 32(4), 1272–1286, doi.org/10.1080/10618600.2022.2157835.
[63]
Hofert, M., Prasad, A., Zhu, M. (2022), RafterNet: Probabilistic predictions in multi-response regression, The American Statistician, 77(4), 406–416, doi:10.1080/00031305.2022.2141857.
[62]
Koike, T., Hofert, M. (2022), Matrix compatibility and correlation mixture representation of generalized Gini's gamma, The Canadian Journal of Statistics, 51(4), 1111–1125, doi.org/10.1002/cjs.11748.
[61]
Koike, T., Kato, S., Hofert, M. (2023), Measuring non-exchangeable tail dependence using tail copulas, ASTIN Bulletin, 53(2), 466–487, doi.org/10.1017/asb.2023.4.
[60]
Coblenz, M., Grothe, O., Herrmann, K., Hofert, M. (2022), Smooth bootstrapping of copula functionals, Electronic Journal of Statistics, 16(1), 2550–2606, doi.org/10.1214/22-EJS2007.
[59]
Hintz, E., Hofert, M. and Lemieux, C. (2022), Multivariate Normal Variance Mixtures in R: The R Package nvmix, Journal of Statistical Software, 102(2), 1–31, doi.org/10.18637/jss.v102.i02.
[58]
Hintz, E., Hofert, M., Lemieux, C., Taniguchi, Y. (2022), Single-Index Importance Sampling with Stratification, Methodology and Computing in Applied Probability, doi.org/10.1007/s11009-022-09970-1.
[57]
Hintz, E., Hofert, M., Lemieux, C. (2022), Computational challenges of t and related copulas, Journal of Data Science, 20(1), 95–110, doi.org/10.6339/22-JDS1034.
[56]
Hofert, M., Prasad, A., Zhu, M. (2022), Multivariate time-series modeling with generative neural networks, Econometrics and Statistics, 23, 147–164, doi:10.1016/j.ecosta.2021.10.011.
[55]
Hofert, M. (2021), Right-truncated Archimedean and related copulas, Insurance: Mathematics and Economics, 99, 79–91, doi:10.1016/j.insmatheco.2021.03.009.
[54]
Hintz, E., Hofert, M., Lemieux, C. (2021), Normal variance mixtures: Distribution, density and parameter estimation, Computational Statistics & Data Analysis, 157, doi:10.1016/j.csda.2021.107175.
[53]
Koike, T., Hofert, M. (2021), Modality for scenario analysis and maximum likelihood allocation, Insurance: Mathematics and Economics, 97, doi:10.1016/j.insmatheco.2020.12.004.
[52]
Górecki, J., Hofert, M., Holeňa, M. (2020), Hierarchical Archimedean Copulas for MATLAB: The HACopula Toolbox, Journal of Statistical Software, 93, 10, doi:10.18637/jss.v093.i10.
[51]
Hofert, M., Ziegel, J. (2020), Matrix-tilted Archimedean copulas, Risks, 9(4), 68, doi:10.3390/risks9040068.
[50]
Hofert, M., Prasad, A., Zhu, M. (2021), Quasi-random sampling for multivariate distributions via generative neural networks, Journal of Computational and Graphical Statistics, 30(3), 647–670, doi:10.1080/10618600.2020.1868302.
[49]
Górecki, J., Hofert, M., Okhrin, O. (2020), Outer power transformations of hiearchical Archimedean copulas: Construction, sampling and estimation, Computational Statistics & Data Analysis, 155, 107109, doi:10.1016/j.csda.2020.107109.
[48]
Hofert, M., Oldford, R. W. (2019), Zigzag expanded navigation plots in R: The R package zenplots, Journal of Statistical Software, 95(4), doi:10.18637/jss.v095.i04.
[47]
Hintz, E., Hofert, M., Lemieux, C. (2020), Grouped Normal Variance Mixtures, Risks, 8(4), 103, doi:10.3390/risks8040103.
[46]
Hofert, M. (2020), Random number generators produce collisions: Why, how many and more, The American Statistician, 75(4), 394–402, doi:10.1080/00031305.2020.1782261.
[45]
Hofert, M. (2020), Implementing the Rearrangement Algorithm: An Example from Computational Risk Management, Risks, 8(2), 47, doi:10.3390/risks8020047.
[44]
Koike, T., Hofert, M. (2020), Markov Chain Monte Carlo Methods for Estimating Systemic Risk Allocations, Risks, 8(1), 6, doi:10.3390/risks8010006.
[43]
Herrmann, K., Hofert, M., Mailhot, M. (2019), Multivariate geometric tail- and range-value-at-risk, ASTIN Bulletin, 50(1), 265–292, doi:10.1017/asb.2019.31.
[42]
Hofert, M., Koike, T. (2019), Compatibility and attainability of matrices of correlation-based measures of concordance, ASTIN Bulletin, 49(3), 885–918, doi:10.1017/10.1017/asb.2019.13.
[41]
Hofert, M., Oldford, R. W., Prasad, A., Zhu, M. (2019), A framework for measuring association of random vectors via collapsed random variables, Journal of Multivariate Analysis, 172, 5–27, doi:10.1016/j.jmva.2019.02.012.
[40]
Hofert, M., Oldford, R. W. (2018), Visualizing Dependence in High-dimensional Data: An Application to S&P 500 Constituent Data, Econometrics and Statistics, 8, 161–183, doi:10.1016/j.ecosta.2017.03.007.
[39]
Hofert, M., Huser, R., Prasad, A. (2018), Hierarchical Archimax copulas, Journal of Multivariate Analysis, 167, 195–211, doi:10.1016/j.jmva.2018.05.001.
[38]
Herrmann, K., Hofert, M., Mailhot, M. (2018), Multivariate geometric expectiles, Scandinavian Actuarial Journal, 2018(7), 629–659, doi:10.1080/03461238.2018.1426038.
[37]
Górecki, J., Hofert, M., Holeňa, M. (2017), Kendall's tau and agglomerative clustering for structure determination of hierarchical Archimedean copulas, Dependence Modeling, 5(1), 75–87, doi:10.1515/demo-2017-0005.
[36]
Górecki, J., Hofert, M., Holeňa, M. (2017), On structure, family and parameter estimation of hierarchical Archimedean copulas, Journal of Statistical Computation and Simulation, 87(17), 3261–3324, doi:10.1080/00949655.2017.1365148.
[35]
Hofert, M., Schepsmeier, U. (2017), International Chinese Statistical Association Bulletin, Guidelines for statistical projects: Coding and Typography (Part III), 29(2), 113–122, link.
[34]
Hofert, M., Schepsmeier, U. (2017), International Chinese Statistical Association Bulletin, Guidelines for statistical projects: Coding and Typography (Part II), 29(1), 52–58, link.
[33]
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.
[32]
Cambou, M., Lemieux, C., Hofert, M. (2016), Quasi-random numbers for copula models, Statistics and Computing, 27(5), 1307–1329, doi:10.1007/s11222-016-9688-4.
[31]
Hofert, M., Hornik, K. (2016), How we R on Android, Linux Journal, 6(266), 90–121.
[30]
Hofert, M., 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., Wang. R. (2016), Bernoulli and Tail-Dependence Compatibility, The Annals of Applied Probability, 26(3), 1636–1658, doi:10.1214/15-AAP1128.
[28]
Hofert, M., Schepsmeier, U. (2016), International Chinese Statistical Association Bulletin, Guidelines for statistical projects: General Aspects (Part I), 28(2), 110–116, link.
[27]
Chavez-Demoulin, V., Embrechts, P., 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.
[26]
Górecki, J., Hofert, M., 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., 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., 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., 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., 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., 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., 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., 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., 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., 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., 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., 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.,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., 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., Mächler, M. (2011), Nested Archimedean Copulas Meet R: The nacopula Package, Journal of Statistical Software, 39(9), 1–20, https://www.jstatsoft.org/v39/i09/.
[07]
Hofert, M., Scherer, M. (2011), CDO pricing with nested Archimedean copulas, Quantitative Finance, 11(5), 775–787, doi:10.1080/14697680903508479.
[06]
Durante, F., Hofert, M., 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., 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., Kohm, M. (2010), Scientific Presentations with LATEX, The PracTEX Journal, 2, link.
[02]
Hofert, M., Scherer, M., 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

[07]
Embrechts, P., Hofert, M. (2012), Risk Measures and Dependence Modeling, Handbook of Insurance, edition 3, ed. by Dionne, G., Springer, to appear in 2024.
[06]
Hintz, E., Hofert, M., Lemieux, C. (2022), Quasi-random sampling with black box or acceptance-rejection inputs, Advances in Modeling and Simulation, ed. by Botev, Z., Keller, A., Lemieux, C., Tuffin, B., Springer, 261–281.
[05]
Arbenz, P., Cambou, M., Hofert, M., Lemieux, C., Taniguchi, Y. (2018), Importance Sampling and Stratification for Copula Models, Contemporary Computational Mathematics – a celebration of the 80th birthday of Ian Sloan, ed. by Dick, J., Kuo, F. Y., Woźniakowski, H.
[04]
Górecki, J., Hofert, M., Holeňa, M. (2014), On the consistency of an estimator for hierarchical Archimedean copulas, 32nd International Conference on Mathematical Methods in Economics, ed. by Talašová, J., Stoklasa, J., Talášek, T., Palacký University, Olomouc, 239–244, link.
[03]
Hering, C., Hofert, M. (2012), Goodness-of-fit tests for Archimedean copulas in high dimensions, Innovations in Quantitative Risk Management, ed. by Glau, K., Scherer, M., Zagst, R., Springer, 357–373.
[02]
Embrechts, P., Hofert, M. (2012), Risk Measures and Dependence Modeling, Handbook of Insurance, edition 2, ed. by Dionne, G., Springer, 135–166.
[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 Durante, F., Härdle, W., Jaworski, P., Rychlik, T., Springer, 147–160, doi:10.1007/978-3-642-12465-5_7.

Books

[04]
Embrechts, P., Hofert, M., Chavez-Demoulin, V. (2023), Risk Revealed: Cautionary Tales, Understanding and Communication, Cambridge University Press, to appear in mid 2024.
[03]
Hofert, M., Frey, R., McNeil, A. J. (2020), The Quantitative Risk Management Exercise Book, Princeton University Press, ISBN 9780691206707.
For the solution manual, see here and for the solution to R exercises, see here.
Cite the book via: 

@Book{,
    publisher =	 {Princeton University Press},
    year =	 2020,
    isbn =         {9780691206707},
    url =          {https://assets.press.princeton.edu/releases/2019-12-03_The_QRM_Exercise_Book.pdf},
    title =	 {{T}he {Q}uantitative {R}isk {M}anagement {E}xercise {B}ook},
    author =	 {Hofert, M., Frey, R., McNeil, A. J.},
}
[02]
Hofert, M., Kojadinovic, I., Mächler, M., Yan, J. (2018), Elements of Copula Modeling with R, Springer Use R! Series, ISBN 978-3-319-89635-9.
For the code and more, see here. For the Use R! Springer book page, see here.
Cite the book via: 

@Book{,
    publisher =	 {Springer Use R! Series},
    year =	 2018,
    isbn =         {978-3-319-89635-9},
    url =          {https://www.springer.com/de/book/9783319896342},
    title =	 {{E}lements of {C}opula {M}odeling with \textsf{R}},
    author =	 {Hofert, M., Kojadinovic, I., Maechler, M., Yan, J.},
}
[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., Schepsmeier, U.Guidelines for Statistical Projects: Coding and Typography, PDF.

 

QRM

 

Software

The software (including book-length manuals, examples, vignettes, demos, tests, etc.) is provided for free. Feel free to use it and cite the software properly (see below). Note that we are not required to provide support (such as solving concrete assignment problems, writing Master's or PhD theses or providing modeling support and engines for companies). The software is open source, so you can also study the source code to solve your problem. Alternatively, ask on major mailing lists (e.g., R help) or software-related forums (e.g., Stack Overflow). If you are convinced you found a bug, write to maintainer() and provide a minimal working example.

© 2022 Marius Hofert | Department of Statistics and Actuarial Science, The University of Hong Kong