Daniel Kraus

Technical University of Munich

Chair of Mathematical Statistics (Prof. Klüppelberg)

Postal address

Postal:
Boltzmannstr. 3
85748 Garching b. München

Place of employment

Chair of Mathematical Statistics (Prof. Klüppelberg)

Work:
Parkring 11-13(8101)/II
85748 Garching b. München


Research Interests

  • Dependence modeling using (vine) copulas
  • Quantile regression
  • Testing for the simplifying assumption
  • Model selection and model discrimination

Publications

Schallhorn, N., Kraus, D., Nagler, T. and Czado, C. (2017)
D-vine quantile regression with discrete variables
submitted for publication
[preprint]

Fischer, M., Kraus, D., Pfeuffer, M. and Czado, C. (2017)
Stress Testing German Industry Sectors: Results from a Vine Copula Based Quantile Regression
Risks 2017, 5(3), 38
[preprint]

Kraus, D. and Czado, C. (2017)
Growing simplified vine copula trees: improving Dißmann's algorithm
submitted for publication
[preprint]

Killiches, M., Kraus, D. and Czado, C. (2017)
Using model distances to investigate the simplifying assumption, goodness-of-fit and truncation levels for vine copulas
submitted for publication
[preprint]

Killiches, M., Kraus, D. and Czado, C. (2017)
Examination and visualization of the simplifying assumption for vine copulas in three dimensions
Australian and New Zealand Journal of statistics, 59, 95-117
[preprint]

Kraus, D. and Czado, C. (2017)
D-vine copula based quantile regression
Computational Statistics and Data Analysis, 110, 1-18
[preprint]

Killiches, M., Kraus, D. and Czado, C. (2017)
Model distances for vine copulas in high dimensions
Statistics and Computing, doi:10.1007/s11222-017-9733-y
[preprint]


Theses

Kraus, D. (2013)
Estimating default risk in the banking sector using financial stress indicators and regime switching models
Master's thesis, Technische Universität München
[link]

Kraus, D. (2011)
Multivariate Normal and t-Distributionsand their Application in Finance
Bachelor's thesis, Technische Universität München
[link]


Talks

Growing simplified vine copula trees: challenging Dißmanns algorithm.
31st European Meeting of Statisticians (EMS), University of Helsinki, Finland, July 24-28, 2017

Stress Testing and CoVaR-Prediction using D-Vine Quantile Regression.
Innovations in Insurance, Risk- and Asset Management, Technische Universität München, Germany, April 5-7, 2017

Predicting conditional quantiles using D-Vine copulas.
9th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2016), University of Seville, Spain, December 9-11, 2016

D-vine copula based quantile regression.
Dependence Modeling in Finance, Insurance and Environmental Science, Technische Universität München, Germany, May 17-19, 2016

D-vine copula based quantile regression.
8th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2015), University of London, UK, December 12-14, 2015

Pair-Copula constructions of multivariate copulas with applications.
European Actuarial Journal (EAJ) Educational Workshop, Vienna University of Technology, Austria, September 8-9, 2014


Teaching

SS 2017: Ferienakademie 2017: Kurs 10
SS 2017: Statistical Analysis of Copulas [MA5408] (Tutorials)
SS 2017: Statistik: Grundlagen [MA2402] (Tutorials)
SS 2017: Functional Data Analysis (Seminar)
WS 2016/2017: Mathematical Introduction to Neural Networks (Seminar)
WS 2016/2017: Generalized Linear Models [MA3403] (Tutorials)
SS 2016: Ferienakademie 2016
SS 2016: Computational Statistics [MA3402] (Tutorials)
WS 2015/2016: Generalized Linear Models [MA3403] (Tutorials)
WS 2015/2016: Nonparametric Statistical Methods (Seminar)
SS 2015: Statistical Modelling with Copulas [MA5408] (Tutorials)
SS 2015: Statistik:Grundlagen [MA2402] (Tutorials)
SS 2015: Learning Gaussian Bayesian Networks (Seminar)
WS 2014/2015: Mathem. Behandlung der Natur- und Wirtschaftswissenschaften 1 (Tutorials)
SS 2014: Survival Analysis [MA5412] (Tutorials)
SS 2014: Developing statistical models for large surveys with application to health insurance and global economic activity (Seminar)
SS 2014: Using R for Statistical Data Analysis II