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We outline a procedure for consistent estimation of marginal and joint default risk in the euro area financial system. We interpret the latter risk as the intrinsic financial system fragility and derive several systemic fragility indicators for euro area banks and sovereigns, based on CDS prices. Our analysis documents that although the fragility of the euro area banking system had started to deteriorate before Lehman Brothers' file for bankruptcy, investors did not expect the crisis to affect euro area sovereigns' solvency until September 2008. Since then, and especially after November 2009, joint sovereign default risk has outpaced the rise of systemic risk within the banking system.
We outline a procedure for consistent estimation of marginal and joint default risk in the euro area financial system. We interpret the latter risk as the intrinsic financial system fragility and derive several systemic fragility indicators for euro area banks and sovereigns, based on CDS prices. Our analysis documents that although the fragility of the euro area banking system had started to deteriorate before Lehman Brothers' file for bankruptcy, investors did not expect the crisis to affect euro area sovereigns' solvency until September 2008. Since then, and especially after November 2009, joint sovereign default risk has outpaced the rise of systemic risk within the banking system.
In more and more situations, artificially intelligent algorithms have to model humans’ (social) preferences on whose behalf they increasingly make decisions. They can learn these preferences through the repeated observation of human behavior in social encounters. In such a context, do individuals adjust the selfishness or prosociality of their behavior when it is common knowledge that their actions produce various externalities through the training of an algorithm? In an online experiment, we let participants’ choices in dictator games train an algorithm. Thereby, they create an externality on future decision making of an intelligent system that affects future participants. We show that individuals who are aware of the consequences of their training on the pay- offs of a future generation behave more prosocially, but only when they bear the risk of being harmed themselves by future algorithmic choices. In that case, the externality of artificially intelligence training induces a significantly higher share of egalitarian decisions in the present.
With Big Data, decisions made by machine learning algorithms depend on training data generated by many individuals. In an experiment, we identify the effect of varying individual responsibility for the moral choices of an artificially intelligent algorithm. Across treatments, we manipulated the sources of training data and thus the impact of each individual’s decisions on the algorithm. Diffusing such individual pivotality for algorithmic choices increased the share of selfish decisions and weakened revealed prosocial preferences. This does not result from a change in the structure of incentives. Rather, our results show that Big Data offers an excuse for selfish behavior through lower responsibility for one’s and others’ fate.
The pressure on tax haven countries to engage in tax information exchange shows first effects on capital markets. Empirical research suggests that investors do react to information exchange and partially withdraw from previous secrecy jurisdictions that open up to information exchange. While some of the economic literature emphasizes possible positive effects of tax havens, the present paper argues that proponents of positive effects may have started from questionable premises, in particular when it comes to the effects that tax havens have for emerging markets like China and India.
Are sanctions sustainable?
(2022)
We investigate the relationship between anchoring and the emergence of bubbles in experimental asset markets. We show that setting a visual anchor at the fundamental value (FV) in the first period only is sufficient to eliminate or to significantly reduce bubbles in laboratory asset markets. If no FV-anchor is set, bubble-crash patterns emerge. Our results indicate that bubbles in laboratory environments are primarily sparked in the first period. If prices are initiated around the FV, they stay close to the FV over the entire trading horizon. Our insights can be related to initial public offerings and the interaction between prices set on pre-opening markets and subsequent intra-day price dynamics.
In this exploratory article, we consider the future of Deutsche Bank and Commerzbank and develop a new approach to the topic: instead of a merger of DB and CB we propose to consider a partial merger of the IT and related back office functions in order to create the basis for an Open Banking platform in Germany. Such a platform would act as a cross-institutional infrastructure company in which the participating banks develop a common data and IT platform (while respecting the data protection regulations). Significant parts of the transaction processes would be pooled by the institutions and executed by the Open Banking platform. Moreover, the institutions remain legally independent and compete with each other at the level of products and services that are developed and produced using just this common data and IT platform – “national champions” would not be created.
But such an “Open Banking Platform” could become even the nucleus of a European Banking platform that could be competitive with existing global data platforms from the USA and China which are already offering financial services and are likely to expand their offerings in the foreseeable future. The proposed model of an open data platform for banks prevents the emergence of national champions and supports the main goal of the banking union: creation of a financial system, in which single banks can be resolved without provoking a systemic crisis and forcing taxpayers to finance bailouts.