Nowadays, as most of the countries in the world pledged to the low-carbon future, global energy problems become more acute. One of the most promising ways to address the growing problems of energy supply widely considered by the international community is use of alternative energy source such as the renewable or "clean" energy. Renewable energy is highly praised for its wide availability and environmental friendliness and its decisive advantage over traditional energy is that it is not subjected to depletion like the fossil fuel resources and that it does not lead to the increasing pollution. Th e paper examines how renewable energy sources can be made useful in the case of Ukraine. Our analysis run along two dimensions: the EU and world's global tendencies as well as the internal current situation. Our main focus is on whether the investing in renewable energy sources can be regarded as a country's "smart" power policy and what the outcomes of turning to the renewable resources might be like over some time period in the future. On the base of statistical approach we came to the conclusions for Ukraine: 1) renewable energy sources (RES) production has a potential to replace oil, gas and coal sources, however the same could not be said about nuclear power; 2) the "green tariff " indeed makes renewable energy projects more attractive for investments; 3) RES production opens new projects in industry thus the employment rate in industry can grow, but not such could be said about agriculture; 4) in analyzed years RES production had very low level of impact on the economic performance; 5) RES have positive eff ect on the environment in Ukraine in analyzed period; 5) RES consumption has a potential to boost positive eff ects of alternative energy resources,

The main goal of this research is to demonstrate how probabilistic graphs may be used for modeling and assessment of credit concentration risk. The destructive power of credit concentrations essentially depends on the amount of correlation among borrowers. However, borrower companies correlation and concentration of credit risk exposures have been difficult for the banking industry to measure in an objective way as they are riddled with uncertainty. As a result, banks do not manage to make a quantitative link to the correlation driving risks and fail to prevent concentrations from accumulating. In this paper, we argue that Bayesian networks provide an attractive solution to these problems and we show how to apply them in representing, quantifying and managing the uncertain knowledge in concentration of credits risk exposures. We suggest the stepwise Bayesian network model building and show how to incorporate expert-based prior beliefs on the risk exposure of a group of related borrowers, and then update these beliefs through the whole model with the new information. We then explore a specific graph structure, a tree-augmented Bayesian network and show that this model provides better understanding of the risk accumulating due to business links between borrowers. We also present two strategies of model assessment that exploit the measure of mutual information and show that the constructed Bayesian network is a reliable model that can be implemented to identify and control threat from concentration of credit exposures. Finally, we demonstrate that suggested tree-augmented Bayesian network is also suitable for stress-testing analysis, in particular, it can provide the estimates of the posterior risk of losses related to the unfavorable changes in the financial conditions of a group of related borrowers. C

The paper deals with the analysis and forecasting of energy security risk index for eleven European countries (the United Kingdom, Denmark, Norway, France, Germany, Poland, Spain, Italy, Norway, the Netherlands, and Ukraine for the period 1992-2016). Nowadays, energy security plays an important role in guaranteeing the national, political and economic security of the country. A literature review of different approaches to defining energy security gave the possibility to consider the regression model of energy security risk index assessment, which takes into account the levels of economic, technical and technological, ecological, social and resource components. This step was proceeded with clusterization of the analysed countries in three groups according to Energy Security Risk Index. Based on this approach resource-mining countries (Denmark, Germany, Norway and the UK) were grouped in Cluster I, while Ukraine occupied the last Cluster III. The next division in five clusters supported the indicated allocation. Finally, we calculated the forecasts of energy security risk index based on data of 1992-2014. It allowed realizing the perspectives of energy market for the nearest future, particularly for Ukraine, which needs development of a new strategy of energy security

ActualityThe concept of output gap plays an important role in traditional macroeconomic theory, applied research and monetary policy. GoalThe paper reveals analyses of the potential economic development in Ukraine and in some countries of the world under limited information. Thus, the practical goal is to consider the best modelling approach for the possibility to regulate GDP in Ukraine, as it has been experienced in other countries of the world. MethodThe research is realized with the help of economic-mathematical modelling of GDP gap based on the analysis of the production function, statistical methods of distinguishing the trend component, one-dimensional filtration, multidimensional filtration. ResultsPractical importance of the paper includes implementation of methods for estimating potential GDP and the GDP gap, in particular, the authors proposed to use an approach based on the production function for the potential growth of European countries modelling. The model reveals that for the Eurozone countries, in the short term, it is not expected that the economy will reach its potential level. The negative forecast is explained by the fact that the Eurozone has been severely affected by the debt crisis. There has been a significant increase in the gap in production volumes, which in turn led to deflation. Despite the uncertainty in the assessment of potential GDP and GDP gap for Ukraine the multidimensional method provided the best modelling result. Thus, it is disclosed that Ukraine is under the growing wave of the business cycle, but not in the synergy with the EU dynamics.

The modern trends of the international labor force migration and the main migration corridors are analyzed in the paper. According to the United Nations Department of Economic and Social Affairs, in 2017, the global stock of international migrants (including refugees) was an estimated 258 million. Inequality between supply and demand on the labor market is a main problem of modern international migration processes. It is possible to make a conclusion about the global increase of amounts of labor migration all over the world, in all the countries and regions. The main trends of remittance flows are also studied. The amount of remittances in 2018 was $689 billions. And $518 billions were sent to the developing countries. In relative terms, remittances in nine countries accounted for over a fifth of the gross domestic product (GDP) in 2018; in the case of Tonga, remittances represented 36 % of the country’s GDP. As a conclusion, it should be said that the amount of remittances is an important macro economical figure. Its inflow can ensure the increase of foreign currency’s supply in the country.