The study was conducted on a sample of 60 of the world’s biggest banks financing the largest fossil fuel entities. The aim is to identify the determinants of ESG ratings of these banks and to determine how relevant their actual credit and investment exposure is to this assessment. The indirect objective is also an examination of whether coal power financing affects ESG ratings. Two logistic regression models have been explored: one dedicated to the identification of high ESG risk banks and the second to predict low ESG risk, which thereafter were combined into one final model. The results indicate that an increase in the Sustainable Development Index (SDI) translates into a decline in the odds of being assigned to the high-risk ESG group relative to the probability of being qualified to the low- or medium-risk ESG group. This study is the first to analyse the impact of actual exposures of the world’s largest banks to the fossil fuels sector on their ESG ratings. The value added is the use of a unique database, the focus on actual rather than declared effects of banks’ policies, and the use of a two-stage logistic regression model construction. The proved relationships are important and of practical relevance to bank managers, regulators, and ESG rating providers. Since the research is conducted on the basis of ESG provided only by one rating agency verification of conclusions with the use of ratings of other agencies, confronting benefits from financing the fossil fuels sector with losses resulting from an increase in the cost of obtaining financing are only selected directions for further research.

Research background: The paper focuses on the research of investors? decisions with regard to shares of all 12 banks listed on the Warsaw Stock Exchange during the first half of 2020. It examines the behavior of a subindex reflecting bank shares? prices against the main WIG index WIG and 14 sector subindices. The authors identify groups of banks with different investors? responses. They also classify banks into separate groups on the basis of changes in the fundamental indicators describing their economic and financial performance. The study concentrates mainly on the verification of the stability of this attribution, explaining reasons for its modifications over time. Purpose of the article: To identify the characteristics of bank clusters determining different capital market responses to their listed shares and to explain the reasons for volatility in investors? behavior within the analyzed period. Methods: The methodology of the research can be described in three areas. The first is the statistical analysis with the emphasis on the use of a quarter range to capture changes in the volatility of share prices. The second area is the clustering k-means method based on the interpolated ? from quarterly to daily ? measures of the bank?s financial condition. This mathematical approach is a novelty in finance and economics. The last, third, area is forecasting with the use of linear regression analysis, which is the key factor in determining the abnormal rates of return. The indicated areas are combined through a generally understood correlation analysis. Findings & value added: Large retail banks have been less affected compared to medium-sized ones with relatively rich corporate portfolios. The initial market reaction reflected concern about the resistance to the crisis of poorly capitalized banks with mean liquidity buffers. Upon the announcement of government support, investors? approach to the shares of banks of differentiated economic and financial performance conformed accordingly. These findings are valuable in the long term especially from the perspective of supervision authorities? policy during external shocks. The presented study suggests designing flexible and tailor-made regulatory approach aligned with the defined bank clusters. Its value added also consists in proposing a new method of analysis, combining interpolation and automatic clustering, which has proved to be adequate for the study of a bank?s financial condition based on daily frequency data. Furthermore, assuming the same length of the estimation window, a close relationship is shown between the results of clustering and the forecasts based on different measures of rates of return.

The outbreak of war in Ukraine raises questions about its impact on the economies and financial systems of countries directly and indirectly involved in the war, but also on the economies of non-participating countries. The article aims to identify the channels through which the war in Ukraine has affected commercial banking in Poland. The original contribution of this paper to the literature dedicated to the impact of the outbreak of war on financial sector consists in the identification of channels of direct and indirect effects of war on commercial bank performance, adoption of unique empirical strategy to point out features of banks determining their sensitivity to the materialisation of geopolitical risk as well as groups of banks whose shares move along similar trajectories during the war. The conclusions emphasize that the long-term negative indirect effects of the war outweigh the short-term indirect ones. The policy implications consist in the benefits of introduction of new reporting standards for banks that focus on the exposure of their clients to the geopolitical risk, augmentation of scope of risks to be covered by regulatory capital by geopolitical risk as well as creation scenarios of stress tests that would include geopolitical risk. Facing war, the implications for commercial banks’ managers come down to the necessity to prioritize cyber risk management and implement a new paradigm of credit and market risk assessment, taking into account the impact of geopolitical factors.

Research background: It is not straightforward to identify the role of institutions for the economic growth. The possible unknown or uncertain areas refer to nonlinearities, time stability, transmission channels, and institutional complementarities. The research problem tackled in this paper is the analysis of the time stability of the relationship between institutions and economic growth and real economic convergence. Purpose of the article: The article aims to verify whether the impact of the institutional environment on GDP dynamics was stable over time or diffed in various subperiods. The analysis covers the EU28 countries and the 1995-2019 period. Methods: We use regression equations with time dummies and interactions to assess the stability of the impact of institutions on economic growth. The analysis is based on the partially overlapping observations. The models are estimated with the use of Blundell and Bond's GMM system estimator. The results are then averaged with the Bayesian Model Averaging (BMA) approach. Structural breaks are identified on the basis of the Hidden Markov Models (HMM). Findings & value added: The value added of the study is threefold. First, we use the HMM approach to find structural breaks. Second, the BMA method is applied to assess the robustness of the outcomes. Third, we show the potential of HMM in foresighting. The results of regression estimates indicate that good institution reflected in the greater scope of economic freedom and better governance lead to the higher economic growth of the EU countries. However, the impact of institutions on economic growth was not stable over time.

<!----> <!----> There are many different types of convergence within economics, as well as several methods to analyse each of them. This book addresses the concept of real economic convergence or the gradual levelling-off of GDP (gross domestic product) per capita rates across economies. In addition to a detailed, holistic overview of the history and theory, the authors include a description of two modern methods of assessing the occurrence and rate of convergence, BMA-based and HMM-based, as well as the results of the empirical analysis. Readers will have access not only to the conventional econometric approach of β convergence but also to an alternative one, allowing for the convergence issue to be expressed in the context of automatic pattern recognition. This approach is universal as it can be adapted to a variety of input data. The lowest aggregation level study investigates regional convergence through the case of Polish voivodships, where convergence towards the leader is tested. On a higher level of aggregation, the authors examine the existence of GDP convergence in such groups as the EU28, North Africa and the Middle East, sub-Saharan Africa, South America, Caribbean, South-East Asia, Australia and Oceania, or post-socialist countries. For each group, the real β convergence is tested using the two above-mentioned approaches. The results are widely discussed, broadly illustrated, interpreted, and compared. The analysis allows readers to draw interesting conclusions about the causes of convergence or the drivers behind divergence. The book will stimulate further research in the field, but the research was conducted from the point of view of individual countries. <!----> <!---->

The assessment of dependence between time series is a common dilemma, which is often solved by the use of the Pearson’s correlation coefficient. Unfortunately, sometimes, the results may be highly misleading. In this paper, an alternative measure is presented. It is based on hidden Markov models and Viterbi paths. The proposed method is in no way universal but seems to provide quite an accurate image of the similarities between time series, by disclosing the periods of convergence and divergence. The usefulness of this new measure is verified by specially crafted examples and real‑life macroeconomic data. There are some definite advantages to this method: the weak assumptions of applicability, ease of interpretation of the results, possibility of easy generalization, and high effectiveness in assessing the dependence of different time series of an economic nature. It should not be treated as a substitute for the Pearson’s correlation, but rather as a complementary method of dependence measure.