In this chapter we discuss alternative explanations of the vast international di®er- ences in bank fees. We are able to show that bank fees are positively related to the level of loan applicants' misperception of their skills (`noise'). We argue that in countries with a higher level of `noise' banks tend to charge higher fees in order to defend their bottom lines against the lower implied average quality of borrowers. We further show that banks tend to charge higher fees when their creditworthiness testing technology is of a lesser quality. Finally, we also illustrate how the results about fees depend on the wealth level and wealth inequality in a given country.

In this chapter we modify the Broecker (1990) model of interbank competition with costless testing by introducing credit bureau services. Unlike Broecker, we show that there exists a symmetrical pure strategy Nash equilibrium in which all banks fully use the credit bureau services. We show that this pure strategy equilibrium can be interpreted as a competition softening (tacit collusion) outcome because credit bureaus allow banks to coordinate on loan rates independent on marginal costs. Compared to the existing models, our results are not based on the existence of informational rents and thus they hold even under transactional banking. The higher is the number of banks the less likely is the competition softening outcome (the conditions for the competition softening equilibrium are less likely to be sat- is¯ed) and thus there exists the optimal number of banks in the market under the conditions of free entry.

In this paper, we introduce a new linear programming second-order stochastic dominance (SSD) portfolio efficiency test for portfolios with scenario approach for distribution of outcomes and a new SSD portfolio inefficiency measure. The test utilizes the relationship between CVaR and dual second-order stochastic dominance, and contrary to tests in Post [14] and Kuosmanen [7], our test detects a dominating portfolio which is SSD efficient. We derive also a necessary condition for SSD efficiency using convexity property of CVaR to speed up the computation. The efficiency measure represents a distance between the tested portfolio and its least risky dominating SSD efficient portfolio. We show that this measure is consistent with the second-order stochastic dominance relation. We find out that this measure is convex and we use this result to describe the set of SSD efficient portfolios. Finally, we illustrate our results on a numerical example.

By its nature, Data Envelopment Analysis (DEA) leaves no room for uncertainty in data such as measurement errors. To improve this fact, we consider $\alpha$-stochastic efficiency concept, and we relate this problem to the stochastic programming problem. Probability inequalities are employed for introducing new criteria, and two special cases for normal and for general distribution are discussed. The strengths of new criteria are illustrated with a numerical example.

By its nature, Data Envelopment Analysis (DEA) leaves no room for uncertainty in data such as measurement errors. To improve this fact, we consider $\alpha$-stochastic efficiency concept, and we relate this problem to the stochastic programming problem. Two types of probability inequalities are employed for introducing new criteria for efficiency.

In this paper, there is proposed a model of unemployment policy for institution that has to reduce unemployment's costs, but it cannot change the governmental policy. This institution has also to pay every month for subsidized workplaces otherwise it has to compensate the worker for releasing her/him. The model uses a "socially critical" rate of unemployment, the rate which should be reduced at all costs. This problem can be seen as the problem of multistage stochastic programming because the compensation of the worker can be cheaper than paying the subsidy every month. The model is demonstrated on the Most region (the Czech region facing the worst unemployment situation), and the four-stage model is solved using scenario-based method in the GAMS language on NEOS system.