- Website Launching (1.Dec.2022)
- Meetings
- Courses
To register for the Tutorials please click here.
Three independent tutorials will take place. Details are provided below.
Dates: 13-15 December 2023.
Venue: HTW Berlin, University of Applied Sciences, Wilhelminenhof campus.
Address: Wilhelminenhofstrasse 75A, 12459 Berlin, Germany.
Room: Rooms 007, ground floor, Building G. The coffee breaks will take place at Room 008 (download details here). For virtual access, see below.
HiTEc offers the possibility of applying for grants (see below).
A link with some material will be provided to the students in due course.
Bayesian semiparametric regression.
Presenters: Thomas Kneib, Hannes Riebl, Paul Wiemann.
Email: Contact
Dates: December 13th and 14th morning, 2023.
Semiparametric regression models overcome some of the restrictions of classical forms of regression models such as (i) the linearity of covariate effects, (ii) the independence of observations, or (iii) the focus on specific types of response distributions and on modelling the conditional mean alone. In this sense, semiparametric regression forms an overarching model class comprising various special cases such as generalized additive models (GAMs), models with random effects, spatial regression models, or generalized additive models for location, scale, and shape (GAMLSS). Bayesian inference based on Markov chain Monte Carlo (MCMC) simulation techniques provides a particularly attractive way of statistically treating such models and developing extensions. This is, for example, due to the modularity of MCMC that allows to flexibly combine different blocks and algorithms for different types of model parameters.
This tutorial will combine lectures with practical exercises on the implementation of Bayesian semiparametric regression utilizing the novel probabilistic programming environment Liesel (https://liesel-project.org). Liesel is developed with the aim of supporting research on Bayesian inference based on MCMC simulation in general and semiparametric regression in particular. Its three main components are (i) an R interface (RLiesel) for the configuration of initial semiparametric regression models, (ii) a graph-based model-building library where the initial model graph can be manipulated to incorporate new research ideas, and (iii) an MCMC library for designing modular inference algorithms combining multiple types of well-tested MCMC kernels.
In the tutorial, we will build on Liesel and discuss (i) general principles of Bayesian inference with MCMC, (ii) Bayesian additive models, and (iii) Bayesian distributional regression. We will combine theoretical background information with hands-on work on applications for all course parts. For participating, knowledge of the principles of Bayesian inference, familiarity with linear and generalized models, and some experience in statistical programming with Python or R will be beneficial.
The instructors for the tutorial will be Thomas Kneib (University of Göttingen), Paul Wiemann (Texas A&M University), and Hannes Riebl (University of Göttingen). Thomas Kneib is a Professor of Statistics and has contributed to the field of Bayesian semiparametric regression with new statistical methodology, as well as the development of software and applications in various contexts. Paul Wiemann and Hannes Riebl are postdoctoral researchers and Liesel’s leading developers.
Risk management with vine copula based dependence models.
Prof. Claudia Czado, Oezge Sahin, Karoline Bax, Technical University of Munich, Germany.
Email: Contact
Dates: December 14th afternoon and 15th morning, 2023.
In the complex risk management landscape, exploring dependency structures becomes paramount. Copulas are key tools in such exploration. However, since standard copula models do not provide flexible dependence and tail patterns, vine copula models (vine-copula.org) were designed to increase the flexibility of these models and overcome their limitations, such as allowing for asymmetric tail dependence. Delving into vine copula-based dependence models, we introduce the fundamental concepts of copulas and vine copulas in the first part of the tutorial. This foundation aids in understanding and using the diverse R software tools available for vine copula analysis, like rvinecopulib of Nagler and Vatter (2023). Progressing further in the second part, we will explore how vine copulas can be used to give insight into multivariate time series data. In this context, we will discuss the integration of univariate ARMA-GARCH models with vines and explore how vine copula models can change how we manage risk. Further, we will look at stress testing using vine copulas. We will provide an overview of R libraries useful in the analysis of multivariate time series: the portvine package of Sommer (2023) is key for analyzing portfolios, especially when Expected Shortfall (ES) and Value at Risk (VaR) are to be estimated. Additionally, we will look at stress testing using D-vine copula-based regressions implemented in the vinereg of Nagler and Kraus (2022) package.
Using real financial data, we will show an implementation, and participants will have the opportunity to get hands-on experience. The tutorial will offer a combination of lectures with practical exercises using the R software.
The instructors for the tutorial will be Claudia Czado (Technical University of Munich), Özge Şahin (Delft University of Technology), and Karoline Bax (Technical University of Munich). Claudia Czado is Associate Professor of Applied Mathematical Statistics and has contributed to the field of copulas and vine copulas. Özge Şahin is an Assistant Professor working on statistical learning and dependence models. Karoline Bax is a Postdoctoral Researcher focusing on Sustainable Finance.
References:
Czado, C., & Nagler, T. (2022). Vine copula based modeling. Annual Review of Statistics and Its Application, 9, 453-477.
Czado, C., Bax, K., Sahin, Ö., Nagler, T., Min, A., & Paterlini, S. (2022). Vine copula based dependence modeling in sustainable finance. The Journal of Finance and Data Science.
Sommer, E., Bax, K., & Czado, C. (2022). Vine Copula based portfolio level conditional risk measure forecasting. arXiv preprint arXiv:2208.09156. Accepted for publication in CSDA.
Network econometrics.
Prof. Monica Billio, University Ca' Foscari of Venice, Italy.
Email: Contact
Dates: December 15th afternoon 2023.
Wednesday, 13 December 2023
Thursday, 14 December 2023
Friday, 15 December 2023
| Standard registration until 5 October 2023 | Late registration until 20 November 2023 | Last minute registration after 20 November 2023 | ||
| HiTEc members (or grantees) | 0€ | 275€ | 300€ | |
| Non-HiTEc members | Tutorial I Registration | 160€ | 170€ | 200€ |
| Tutorial II Registration | 130€ | 150€ | 180€ | |
| Tutorial III Registration | 75€ | 85€ | 120€ | |
Co-organized by the HiTEc COST Action CA21163
Sponsored by COST