Eigenverantwortung — Berufliche Leistung und Anforderung

Ohne Zusammenfassung     

Asymptotic Representations of the Multivariate Empirical Distribution Function and Applications

Often, many complicated statistics used as estimators or test statistics take the form of the (multivariate) empirical distribution function evaluated at a random vector (V_n). Denote such statistics by S_n. This paper describes methods for the study of various asymptotic properties of S_n. First, under minimal assumptions, a weak asymptotic representation for S_n is derived. This result may be used to show the asymptotic normality of S_n. Second, under slightly more stringent regularity conditions, an almost sure representation of S_n, with suitable order (as.) of the remainder term is studied and then a law of the iterated logarithm for S_n, is derived. In this context, strong convergence results from a sequential point of view are also studied. Finally, weak convergence to a Brownian motion process is established. As an application, we show the limiting normality of S_n, for a random number of summands.     

Stochastic orders and non-Gaussian risk factor models

The main results of this paper are monotonicity statements about the risk measures value-at-risk (VaR) and tail value-at-risk (TVaR) with respect to the parameters of single and multi risk factor models, which are standard models for the quantification of credit and insurance risk. In the context of single risk factor models, non-Gaussian distributed latent risk factors are allowed. It is shown that the TVaR increases with increasing claim amounts, probabilities of claims and correlations, whereas the VaR is in general not monotone in the correlation parameters. To compare the aggregated risks arising from single and multi risk factor models, the usual stochastic order and the increasing convex order are used in this paper, since these stochastic orders can be interpreted as being induced by the VaR-concept and the TVaR-concept, respectively. To derive monotonicity statements about these risk measures, properties of several further stochastic orders are used and their relation to the usual stochastic order and to the increasing convex order are applied.     

A tractable model of process fairness under risk

This paper proposes a social utility model of individual preferences for process fairness that complements the Fehr–Schmidt model for outcome fairness. The model assumes that the outcome generating process rather than the actual outcomes influences fairness perceptions, and that process fairness is evaluated through comparison of expected payoffs. The process model successfully predicts data from bargaining games involving risky payoffs that neither outcome-based nor reciprocity models can explain. In a theoretical application, Machina’s parental example for non-expected utility in a distributional problem (Machina’s Mom) is analyzed by incorporating individual level process fairness preferences under expected utility.     

A generalization of the matching distribution

The Matching Distribution converges to a Poisson Distribution with λ=1 as the parameter n converges to infinity. A generalization of the Matching Distribution is proposed. The properties of this Generalized Matching Distribution (GMD) turn out to be analogical to the case with λ=1.     

环境税改革与绿色增长:欧洲视角

欧洲的环境税改革可以追溯到20世纪90年代初,但自2001年起却经历了一个持续的发展低谷阶段,而最近,日益严厉的碳减排目标和众多欧洲政府通过税收来减少公共赤字的需要相结合,正在促成人们对碳/能源税和环境税改革兴趣的再度提升。本文的基本观点是,环境税改革是一种促进绿色增长的政策,也就是带来在欧洲和其他地方的环境可持续的经济增长,而欧洲范围内的环境税改革是欧盟实现2020年减排目标十分有效而经济的手段,尤其是在全球性气候合作的背景下。此外,它还可以用来提高一般意义上的资源利用效率。环境税改革是欧洲应对全球气候变化的一个巨大政策机遇,它不仅代价较低,而且有助于就业、技术革新和可以长期经济发展的新工业,而这些都是来自公共政策的巨大收益。因而,环境税改革理应成为实现温室气体大幅度削减目标的政策工具组合中的重要组成部分。当然,这有赖于欧盟政治家和决策者找到应对环境税改革可能引致的诸多政治挑战的方法,从而代表欧洲实现这一改革的预期收益。