Risk aversion in the bond market - the case of redemption lottery bonds

Evidence on the magnitude of risk aversion is essential in understanding the behavior of asset prices. Both the equity premium puzzle and the credit spread puzzle address the problem of a reasonable size of agents’ risk aversion. The estimation of risk aversion is, however, impeded by the fact that observed prices depend on risk preferences and probability beliefs. The market for German redemption lottery bonds (Tilgungsanleihen) constitutes an exceptionally clean environment to study investors’ risk preferences independent of subjective probability beliefs as the probabilities of price changes caused by redemption lotteries are objectively known. The focus of this thesis is to analyze the systematic redemption risk of lottery bonds. Our contribution to the literature is twofold. On the theoretical side, we develop a fully specified dynamic equilibrium model to price redemption lottery bonds. On the empirical side, we employ the valuation model to estimate implied risk aversion coefficients from transaction prices of German lottery bonds. Our most important findings are threefold: estimated relative risk aversion coefficients are significantly positive, of moderate magnitude, and time dependent

Generation of Paths in a Maze using a Deep Network without Learning

Trajectory- or path-planning is a fundamental issue in a wide variety of applications. Here we show that it is possible to solve path planning for multiple start- and end-points highly efficiently with a network that consists only of max pooling layers, for which no network training is needed. Different from competing approaches, very large mazes containing more than half a billion nodes with dense obstacle configuration and several thousand path end-points can this way be solved in very short time on parallel hardware

Chaotic duality in string theory

We investigate the general features of renormalization group flows near superconformal fixed points of four dimensional N=1 gauge theories with gravity duals. The gauge theories we study arise as the world-volume theory on a set of D-branes at a Calabi-Yau singularity where a del Pezzo surface shrinks to zero size. Based mainly on field theory analysis, we find evidence that such flows are often chaotic and contain exotic features such as duality walls. For a gauge theory where the del Pezzo is the Hirzebruch zero surface, the dependence of the duality wall height on the couplings at some point in the cascade has a self-similar fractal structure. For a gauge theory dual to P 2 blown up at a point, we find periodic and quasiperiodic behavior for the gauge theory couplings that does not violate the a conjecture. Finally, we construct supergravity duals for these del Pezzos that match our field theory beta functions

Data-Driven Modeling and Prediction of Complex Spatio-Temporal Dynamics in Excitable Media

Spatio-temporal chaotic dynamics in a two-dimensional excitable medium is (cross-) estimated using a machine learning method based on a convolutional neural network combined with a conditional random field. The performance of this approach is demonstrated using the four variables of the Bueno-Orovio-Fenton-Cherry model describing electrical excitation waves in cardiac tissue. Using temporal sequences of two-dimensional fields representing the values of one or more of the model variables as input the network successfully cross-estimates all variables and provides excellent forecasts when applied iteratively

A probabilistic particle tracking framework for guided and Brownian motion systems with high particle densities

This paper presents a new framework for particle tracking based on a Gaussian Mixture Model (GMM). It is an extension of the state-of-the-art iterative reconstruction of individual particles by a continuous modeling of the particle trajectories considering the position and velocity as coupled quantities. The proposed approach includes an initialization and a processing step. In the first step, the velocities at the initial points are determined after iterative reconstruction of individual particles of the first four images to be able to generate the tracks between these initial points. From there on, the tracks are extended in the processing step by searching for and including new points obtained from consecutive images based on continuous modeling of the particle trajectories with a Gaussian Mixture Model. The presented tracking procedure allows to extend existing trajectories interactively with low computing effort and to store them in a compact representation using little memory space. To demonstrate the performance and the functionality of this new particle tracking approach, it is successfully applied to a synthetic turbulent pipe flow, to the problem of observing particles corresponding to a Brownian motion (e.g., motion of cells), as well as to problems where the motion is guided by boundary forces, e.g., in the case of particle tracking velocimetry of turbulent Rayleigh-Bénard convection