Refine
Year of publication
Document Type
- Doctoral Thesis (858)
- Article (220)
- Book (115)
- Contribution to a Periodical (114)
- Working Paper (62)
- Part of a Book (39)
- Part of Periodical (26)
- Conference Proceedings (17)
- Other (16)
- Review (10)
Language
- German (870)
- English (537)
- French (82)
- Multiple languages (15)
- Russian (1)
Keywords
- Deutschland (95)
- Luxemburg (54)
- Schule (40)
- Stress (40)
- Schüler (35)
- Politischer Unterricht (30)
- Demokratie (29)
- Modellierung (29)
- Fernerkundung (25)
- Geschichte (24)
Institute
- Raum- und Umweltwissenschaften (213)
- Psychologie (212)
- Politikwissenschaft (139)
- Universitätsbibliothek (85)
- Rechtswissenschaft (77)
- Fachbereich 4 (68)
- Wirtschaftswissenschaften (66)
- Mathematik (65)
- Medienwissenschaft (57)
- Fachbereich 3 (54)
This dissertation examines the relevance of regimes for stock markets. In three research articles, we cover the identification and predictability of regimes and their relationships to macroeconomic and financial variables in the United States.
The initial two chapters contribute to the debate on the predictability of stock markets. While various approaches can demonstrate in-sample predictability, their predictive power diminishes substantially in out-of-sample studies. Parameter instability and model uncertainty are the primary challenges. However, certain methods have demonstrated efficacy in addressing these issues. In Chapter 1 and 2, we present frameworks that combine these methods meaningfully. Chapter 3 focuses on the role of regimes in explaining macro-financial relationships and examines the state-dependent effects of macroeconomic expectations on cross-sectional stock returns. Although it is common to capture the variation in stock returns using factor models, their macroeconomic risk sources are unclear. According to macro-financial asset pricing, expectations about state variables may be viable candidates to explain these sources. We examine their usefulness in explaining factor premia and assess their suitability for pricing stock portfolios.
In summary, this dissertation improves our understanding of stock market regimes in three ways. First, we show that it is worthwhile to exploit the regime dependence of stock markets. Markov-switching models and their extensions are valuable tools for filtering the stock market dynamics and identifying and predicting regimes in real-time. Moreover, accounting for regime-dependent relationships helps to examine the dynamic impact of macroeconomic shocks on stock returns. Second, we emphasize the usefulness of macro-financial variables for the stock market. Regime identification and forecasting benefit from their inclusion. This is particularly true in periods of high uncertainty when information processing in financial markets is less efficient. Finally, we recommend to address parameter instability, estimation risk, and model uncertainty in empirical models. Because it is difficult to find a single approach that meets all of these challenges simultaneously, it is advisable to combine appropriate methods in a meaningful way. The framework should be as complex as necessary but as parsimonious as possible to mitigate additional estimation risk. This is especially recommended when working with financial market data with a typically low signal-to-noise ratio.
Mixed-Integer Optimization Techniques for Robust Bilevel Problems with Here-and-Now Followers
(2025)
In bilevel optimization, some of the variables of an optimization problem have to be an optimal solution to another nested optimization problem. This specific structure renders bilevel optimization a powerful tool for modeling hierarchical decision-making processes, which arise in various real-world applications such as in critical infrastructure defense, transportation, or energy. Due to their nested structure, however, bilevel problems are also inherently hard to solve—both in theory and in practice. Further challenges arise if, e.g., bilevel problems under uncertainty are considered.
In this dissertation, we address different types of uncertainties in bilevel optimization using techniques from robust optimization. We study mixed-integer linear bilevel problems with lower-level objective uncertainty, which we tackle using the notion of Gamma-robustness. We present two exact branch-and-cut approaches to solve these Gamma-robust bilevel problems, along with cuts tailored to the important class of monotone interdiction problems. Given the overall hardness of the considered problems, we additionally propose heuristic approaches for mixed-integer, linear, and Gamma-robust bilevel problems. The latter rely on solving a linear number of deterministic bilevel problems so that no problem-specific tailoring is required. We assess the performance of both the exact and the heuristic approaches through extensive computational studies.
In addition, we study the problem of determining optimal tolls in a traffic network in which the network users hedge against uncertain travel costs in a robust way. The overall toll-setting problem can be seen as a single-leader multi-follower problem with multiple robustified followers. We model this setting as a mathematical problem with equilibrium constraints, for which we present a mixed-integer, nonlinear, and nonconvex reformulation that can be tackled using state-of-the-art general-purpose solvers. We further illustrate the impact of considering robustified followers on the toll-setting policies through a case study.
Finally, we highlight that the sources of uncertainty in bilevel optimization are much richer compared to single-level optimization. To this end, we study two aspects related to so-called decision uncertainty. First, we propose a strictly robust approach in which the follower hedges against erroneous observations of the leader's decision. Second, we consider an exemplary bilevel problem with a continuous but nonconvex lower level in which algorithmic necessities prevent the follower from making a globally optimal decision in an exact sense. The example illustrates that even very small deviations in the follower's decision may lead to arbitrarily large discrepancies between exact and computationally obtained bilevel solutions.
Partial differential equations are not always suited to model all physical phenomena, especially, if long-range interactions are involved or if the actual solution might not satisfy the regularity requirements associated with the partial differential equation. One remedy to this problem are nonlocal operators, which typically consist of integrals that incorporate interactions between two separated points in space and the corresponding solutions to nonlocal equations have to satisfy less regularity conditions.
In PDE-constrained shape optimization the goal is to minimize or maximize an objective functional that is dependent on the shape of a certain domain and on the solution to a partial differential equation, which is usually also influenced by the shape of this domain. Moreover, parameters associated with the nonlocal model are oftentimes domain dependent and thus it is a natural next step to now consider shape optimization problems that are governed by nonlocal equations.
Therefore, an interface identification problem constrained by nonlocal equations is thoroughly investigated in this thesis. Here, we focus on rigorously developing the first and second shape derivative of the associated reduced functional. In addition, we study first- and second-order shape optimization algorithms in multiple numerical experiments.
Moreover, we also propose Schwarz methods for nonlocal Dirichlet problems as well as regularized nonlocal Neumann problems. Particularly, we investigate the convergence of the multiplicative Schwarz approach and we conduct a number of numerical experiments, which illustrate various aspects of the Schwarz method applied to nonlocal equations.
Since applying the finite element method to solve nonlocal problems numerically can be quite costly, Local-to-Nonlocal couplings emerged, which combine the accuracy of nonlocal models on one part of the domain with the fast computation of partial differential equations on the remaining area. Therefore, we also examine the interface identification problem governed by an energy-based Local-to-Nonlocal coupling, which can be numerically computed by making use of the Schwarz method. Here, we again present a formula for the shape derivative of the associated reduced functional and investigate a gradient based shape optimization method.
Based on data collected from two surveys conducted in Germany and Taiwan, my first paper (Chapter 2) examines the impact of culture through language priming (Chinese vs. German or English) on individuals’ price fairness perception and attitudes towards government intervention and economic policy involving inequality. We document large cross-language differences: in both surveys, subjects who were asked and answered in Chinese demonstrated significantly higher perceived price fairness in a free market mechanism than their counterparts who completed the survey in German or English language. They were also more inclined to accept a Pareto improvement policy which increases social and economic inequality. In the second survey, Chinese language induced also a lower readiness to accept government intervention in markets with price limits compared to English language. Since language functions as a cultural mindset prime, our findings imply that culture plays an important role in fairness perception and preferences regarding social and economic inequality.
Chapter 3 of this work deals with patriotism priming. By conducting two online experimental studies conducted in Germany and China, we tested three different kinds of priming methods for constructive and blind patriotism respectively. Subjects were randomly distributed to one of three treatments motivated by previous studies in different countries: a constructive patriotism priming treatment, a blind patriotism priming treatment and a non-priming baseline. While the first experiment had a between-subject design, the second one enabled both a between-subject and within-subject comparison, since the level of patriotism of individuals was measured before and after priming respectively. The design of the second survey also enabled a comparison among the three priming methods for constructive and blind patriotism. The results showed that the tested methods, especially the national achievements as a priming mechanism, functioned well overall for constructive patriotism.
Surprisingly, the priming for blind patriotism did not work in either Germany or China and the opposite results were observed. Discussion and implications for future studies are provided at the end of the chapter.
Using data from the same studies as in Chapter 3, Chapter 4 examines the impact of patriotism on individuals’ fairness perception and preferences regarding inequality and on their attitudes toward economic policy involving inequality. Across surveys and countries, a positive and significant effect of blind patriotism on economic individualism was found. For China, we also found a significant relationship between blind patriotism and the agreement to unequal economic policy. In contrast to blind patriotism, we did not find an association of constructive patriotism to economic individualism and to attitudes toward economic policy involving inequality. Political and economic implications based on the results are discussed.
The last chapter (Chapter 5) studies the self-serving bias (when an individual’s perception about fairness is biased by self-interest) in the context of price setting and profit distribution. By analyzing data from four surveys conducted in six countries, we found that the stated appropriate product price and the fair allocation of profit was significantly higher, when the outcome was favorable to oneself. This self-serving bias in price fairness perception, however, differed across countries significantly and was significantly higher in Germany, Taiwan and China than in Vietnam, Estonia and Japan.
Although economic individualism and masculinity were found to have a significant negative effect on self-interest bias in price fairness judgment, they did not sufficiently explain the differences in self-interest bias between countries. Furthermore, we also observed an increase of self-interest bias in profit allocation over time in time-series data for one country (Germany) with data from 2011 to 2023.
The four papers are all co-authored with Prof. Marc Oliver Rieger, and the first paper has been accepted for publications in Review of Behavioral Economics.
The End of an Era? Embedding MONUSCO’s Withdrawal in the Current State of UN Peace Operations
(2024)