Deep Reinforcement Learning (DRL) has shown remarkable success in solving complex tasks across various research fields. However, transferring DRL agents to the real world is still challenging due to the significant discrepancies between simulation and reality. To address this issue, we propose a robust DRL framework that leverages platform-dependent perception modules to extract task-relevant information and train a lane-following and overtaking agent in simulation. This framework facilitates the seamless transfer of the DRL agent to new simulated environments and the real world with minimal effort. We evaluate the performance of the agent in various driving scenarios in both simulation and the real world, and compare it to human players and the PID baseline in simulation. Our proposed framework significantly reduces the gaps between different platforms and the Sim2Real gap, enabling the trained agent to achieve similar performance in both simulation and the real world, driving the vehicle effectively.

We consider the classical factor model of Jöreskog (1969) within a change point detection framework with the aim of discovering intervals of local homogeneity of the model. Our tests for structural breaks in the variance (homogeneity in variance) as well as both in the mean and the variance (complete homogeneity) are based on a maximum statistic of sequential generalized likelihood ratios. We approximate the small-sample distribution by means of a multiplier bootstrap. To handle the high-dimensional parameter problem, we suggest a novel bias correction for the multiplier bootstrap. Simulations show that the tests perform very well in terms of size and power. In our empirical application, we study structural breaks for moderately sized equity portfolios

Vine copulas have become increasingly popular in modeling multivariate data. Their key building blocks are bivariate copulas selected from a limited number of parametric families. Apart from the Student's t copula none of these families is particularly good at modeling heteroskedastic data. In this regard, certain mixture copulas have been shown to perform better. We develop a feasible algorithm to determine the optimal bivariate mixture model. We demonstrate that the direct maximum likelihood optimization is comparative to an EM-type algorithm and can even outperform it. Furthermore, we highlight the advantages of copula mixture models that mix with the independence copula. Since the search for the best mixture model is time-consuming, we also present a simple a priori check to determine whether a mixture copula might yield significant improvements.

In the talk, we discuss a complex problem of recognizing, detecting, and estimating stochastically relevant (significant) changepoints within in a time series of specific functional profiles (the option market implied volatility smiles ) while the main focus is on changes caused by various exogenous effects (meaning that the observed changes are not due the market itself and its dynamics but rather because of some human-made interactions).

The standard implied volatility tool (commonly used for the market analysis by practitioners) is shown to be insufficient for a proper detection and analysis of this type of risk because exogenous changes are typically dominated by endogenous effects coming from a specific trading mechanism or a natural market dynamics.

We propose a whole methodological approch (statistical theory, computational algorithms, and practical recommendations) based on "artificial options" that always have a constant (over time) maturity and formal statistical tests for detecting significant changepoints are proposed under different theoretical, computational, as well as applicational scenarios.