Most standard asset-pricing models assume that all shocks to consumption are permanent. We relax this assumption and allow also for non-permanent shocks. In our specification, the long-run mean of consumption growth is constant; consumption levels are subject to short-run deviations from their long-run trend. The implications of our model are dramatically different from those obtained in the prior literature. A canonical and parsimonious asset pricing model with CRRA preferences and non-permanent shocks can reproduce the equity premium, high return volatility and return predictability with a coefficient of relative risk aversion below ten. This finding suggests that non-permanent shocks can play an important role in explaining asset pricing puzzles.
Many community detection algorithms have been developed to uncover the mesoscopic properties of complex networks. However how good an algorithm is, in terms of accuracy and computing time, remains still open. Testing algorithms on real-world network has certain restrictions which made their insights potentially biased: the networks are usually small, and the underlying communities are not defined objectively. In this study, we employ the Lancichinetti-Fortunato-Radicchi benchmark graph to test eight state-of-the-art algorithms. We quantify the accuracy using complementary measures and algorithms' computing time. Based on simple network properties and the aforementioned results, we provide guidelines that help to choose the most adequate community detection algorithm for a given network. Moreover, these rules allow uncovering limitations in the use of specific algorithms given macroscopic network properties. Our contribution is threefold: firstly, we provide actual techniques to determine which is the most suited algorithm in most circumstances based on observable properties of the network under consideration. Secondly, we use the mixing parameter as an easily measurable indicator of finding the ranges of reliability of the different algorithms. Finally, we study the dependency with network size focusing on both the algorithm's predicting power and the effective computing time.
Prof. Dr. Reto Eberle (Audit Partner, KPMG) und Dr. Daniel Lengauer (Partner, Kellerhals Carrard) haben sich für den Zürcher Kommentar in den vergangenen Jahren intensiv mit den rechtlichen Vorschriften zur Revision in der Schweiz beschäftigt. In diesem Beitrag stellen die beiden Autoren wichtige Erkenntnisse für das Audit Committee vor.
We analyze whether and how young researchers’ (inter)national mobility affects their later appointment success. We use data on 330 researchers from business and economics in Germany, Austria and the German-speaking part of Switzerland and measure appointment success by (a) the time it takes a young researcher to get tenure and by (b) whether the researcher succeeded in getting tenure at a highly ranked institution. We find that international mobility is positively related to the likelihood of getting tenure at a highly ranked institution whereas pre-tenure national mobility is negatively related to both measures of appointment success. The latter effect stems from the period when post-doctoral pre-tenure national mobility was uncommon and created a negative stigma -- an effect that vanishes after the introduction of Juniorprofessorships.
