Refine
Year of publication
Document Type
- Working Paper (1504)
- Part of Periodical (578)
- Article (207)
- Report (141)
- Book (100)
- Doctoral Thesis (70)
- Contribution to a Periodical (44)
- Conference Proceeding (21)
- Part of a Book (13)
- Periodical (12)
Is part of the Bibliography
- no (2719)
Keywords
- Deutschland (98)
- Financial Institutions (92)
- Capital Markets Union (67)
- ECB (67)
- Financial Markets (59)
- Banking Regulation (53)
- Banking Union (52)
- Household Finance (47)
- Monetary Policy (41)
- Banking Supervision (40)
Institute
- Wirtschaftswissenschaften (2719) (remove)
We model the decisions of young individuals to stay in school or drop out and engage in criminal activities. We build on the literature on human capital and crime engagement and use the framework of Banerjee (1993) that assumes that the information needed to engage in crime arrives in the form of a rumour and that individuals update their beliefs about the profitability of crime relative to education. These assumptions allow us to study the effect of social interactions on crime. In our model, we investigate informational spillovers from the actions of talented students to less talented students. We show that policies that decrease the cost of education for talented students may increase the vulnerability of less talented students to crime. The effect is exacerbated when students do not fully understand the underlying learning dynamics.
Discussions about the banking union have restarted. Its success so far is limited: national banking sectors are still overwhelmingly exposed to their own countries’ economies, cross border banking has not increased and capital and liquidity remain locked within national boundaries. The policy letter highlights that the current debate, centered on sovereign exposures and deposit insurance, misses critical underlying problems in the supervision and resolution frameworks. The ECB supervisors’ efforts to facilitate cross-border banking have been hampered by national ringfencing. The resolution framework is not up to its task: limited powers of the SRB, prohibitive access conditions and limited size of the Single Resolution Fund limit its effectiveness. A lack of a coherent European framework for insolvency unlevels the regulatory field and creates incentives to bypass European rules. The new Commission and European Parliament, with the new ECB leadership, provide a unique opportunity to address these shortcomings and make the banking union work.
We study the accuracy and usefulness of automated (i.e., machine-generated) valuations for illiquid and heterogeneous real assets. We assemble a database of 1.1 million paintings auctioned between 2008 and 2015. We use a popular machine-learning technique—neural networks—to develop a pricing algorithm based on both non-visual and visual artwork characteristics. Our out-of-sample valuations predict auction prices dramatically better than valuations based on a standard hedonic pricing model. Moreover, they help explaining price levels and sale probabilities even after conditioning on auctioneers’ pre-sale estimates. Machine learning is particularly helpful for assets that are associated with high price uncertainty. It can also correct human experts’ systematic biases in expectations formation—and identify ex ante situations in which such biases are likely to arise.
We examine the degree to which competition amongst lenders interacts with the cyclicality in lending standards using a simple measure, the average physical distance of borrowers from banks’ branches. We propose that this novel measure captures the extent to which lenders are willing to stretch their lending portfolio. Consistent with this idea, we find a significant cyclical component in the evolution of lending distances. Distances widen considerably when credit conditions are lax and shorten considerably when credit conditions become tighter. Next, we show that a sharp departure from the trend in distance between banks and borrowers is indicative of increased risk taking. Finally, we provide evidence that as competition in banks’ local markets increases, their willingness to make loans at greater distance increases. Since average lending distance is easily measurable, it is potentially a useful measure for bank supervisors.
Decisions under ambiguity depend on both the belief regarding possible scenarios and the attitude towards ambiguity. This paper exclusively investigates the belief formation and belief updating process under ambiguity, using laboratory experiments. The results show that half of the subjects tend to adopt a simple heuristic strategy when updating beliefs, while the other half seems to partially adopt the Bayesian updates. We recover beliefs, represented by distributions of the priors/posteriors. The recoverable initial priors mostly follow a uniform distribution. We also find that subjects on average demonstrate slight pessimism in an ambiguous environment.
We investigate the default probability, recovery rates and loss distribution of a portfolio of securitised loans granted to Italian small and medium enterprises (SMEs). To this end, we use loan level data information provided by the European DataWarehouse platform and employ a logistic regression to estimate the company default probability. We include loan-level default probabilities and recovery rates to estimate the loss distribution of the underlying assets. We find that bank securitised loans are less risky, compared to the average bank lending to small and medium enterprises.