Gabriel Frahm Livres

This book is refreshing, innovative, and important for several reasons. It seeks to reconcile game theory with one-person decision theory by viewing a game as a collection of individual decision problems. While this approach may seem natural, few game theory texts truly implement it. This work stands out by smoothly transitioning between decision theory and game theory, demonstrating that they can and must coexist. The careful exposition, numerous illustrative examples, critical assessments of traditional game theory concepts, and enlightening comparisons with a subjectivistic approach make it an engaging read and essential for anyone interested in the foundations of these theories. The author presents a bold synthesis of decision theory and game theory from a Bayesian perspective, distinguishing between one-person games and those involving multiple players. However, he argues that this distinction is not always necessary, as both types can be analyzed within a unified theoretical framework. The dynamics of choice are modeled across various settings, including scenarios with complete or incomplete information and perfect or imperfect knowledge. The book includes provocative examples that highlight the benefits of a unified theory of rational decision-making, making it a valuable resource for scholars and practitioners alike.

In this paper, we derive two shrinkage estimators for the global minimum variance portfolio that dominate the traditional estimator with respect to the out-of-sample variance of the portfolio return. The presented results hold for any number of observations d + 2 and number of assets d 4. The small-sample properties of the shrinkage estimators as well as their large-sample properties for fixed d but n -> ∞ but n/d -> q ∞ are investigated. Furthermore, we present a small-sample test for the question of whether it is better to completely ignore time series information in favor of naive diversification. -- Covariance matrix estimation ; global minimum variance portfolio ; James-Stein estimation : naive diversification : shrinkage estimator

Das Buch stellt Methoden zur Messung von Unternehmensrisiken sowie die Grundlagen einer wertorientierten Unternehmenssteuerung aus der Sicht des Risikomanagements vor. Eine kapitelweise Lernkontrolle ermöglicht den Lernerfolg. Anhand von Fallstudien wird die Praxisrelevanz eines holistischem Enterprise Risk Managements veranschaulicht.