Bank Risk Capital and Its Effectiveness in Selected Euro Area Banking Sectors

Risk capital or capital at risk (CaR) refers to the amount of capital set aside and maintained by banks to cover different types of risk. For banks, it is used as a buffer against claims or expenses in the event that ordinary capital is not enough to cover them. Thereby, risk capital can also be recognized as risk-bearing capital or surplus funds. Risk capital may generate very high costs, but on the other hand it protects against insolvency. That’s why a bank needs to find the ‘Gold mean’—the optimal value of risk capital that will not lower its efficiency, but still ensure financial security. The main objective of the study is identification of interdependencies between bank risk capital and effectiveness of the aggregated Eurozone banking sector and selected national banking sectors of the euro area. The paper tries to answer the research question whether the risk capital supports or lowers banks’ operational effectiveness. The adopted research hypothesis stated that there is a positive correlation between profitability and size of bank risk capital. To verify the hypothesis regression models were used. The results indicate that the size and structure of bank capital impact on the credit institutions’ effectiveness in the analyzed banking sectors, however with different intensity. Thereby, the article fulfils a research gap in the field of research studies that take into account how capital at risk and specific capital adequacy regulations may impact on a bank’s efficiency.

PENGARUH RISIKO KREDIT DAN TINGKAT KECUKUPAN MODAL TERHADAP PROFITABILITAS PERBANKAN YANG LISTING DI BEI PERIODE 2015-2019

Abstrak: Pengaruh Risiko Kredit Dan Tingkat Kecukupan Modal Terhadap Profitabilitas Perbankan Yang Listing Di BEI Periode 2015-2019. Penelitian bertujuan untuk mengetahui pengaruh Risiko Kredit dan Tingkat Kecukupan Modal Terhadap Profitabilitas Perbankan yang Listing Di BEI Periode 2015-2019 dan ada 43 perbankan listing di BEI. Sample diambil 10 perusahaan perbankan. Teknik sampling yang digunakan purposive sampling. Data di analisis dengan analisis risiko kredit, analisis tingkat kecukupan modal, analisis profitabilitas, uji asumsi klasik, uji parsial dan uji serempak. Secara parsial dan serempak hasil menunjukan resiko kredit dan tingkat kecukupan modal berpengaruh signifikan terhadap profitabilitas. sedangkan secara serempak menyatakan bahwa ada pengaruh yang signifikan resiko kredit dan tingkatkan kecukupan modal terhadap profitabilitas.Kata kunci: Profitabilitas, Risiko Kredit, Tingkat Kecukupan Modal.Abstract: Effect of Credit Risk and Capital Adequacy Levels Profitability of Banks Listing on the IDX for the 2015-2019 Period. This study aims to determine the effect of Credit Risk and Capital Adequacy Level on the Profitability of Banks Listed on the IDX for the 2015-2019 period, and there are 43 banks listed on the IDX. Samples were taken from 10 banking companies. The sampling technique used was purposive sampling. The data were analyzed by credit risk analysis, capital adequacy level analysis, profitability analysis, classical assumption test, partial test and simultaneous test. Partially and simultaneously the results show that credit risk and the level of capital adequacy have a significant effect on profitability. while simultaneously stating that there is a significant effect of credit risk and increasing capital adequacy on profitability.Keywords: Profitabilitas, Credit Risk, Capital Adequacy Level.

Modern Look at Uncertainty in Conceptual Geological Modelling. Development of the Decision Support System for Petroleum Exploration

At the early stages of field life, the subsurface project team operates under lack of information. Due to the high uncertainties, decisions at the exploration and appraisal stages are often influenced by cognitive distortion that leads to overestimation or underestimation of hydrocarbon reserves and, as a result, to suboptimal investment decisions. World practice allows us to identify the most common causes of cognitive bias: the team focus on the most provable according to their view scenario and may ignore data that contradicts the chosen scenario,the opinions of the team members differ in the choice of the most likely scenario,the team members work with geological and geophysical (G&G) data performing separate tasks and may miss important connections between various sources of information. The consequences of these cognitive distortions cause an increase in risk capital, the duration of exploration activities, and the choice of suboptimal field developmentstrategy resulting in a decrease in the effectiveness of the exploration program and the project as a whole. To reduce such risks, it is possible to attract subject matter experts with extensive experience to support the project team. But the amount of experts is limited and this approach cannot be implemented for the entire portfolio of exploration projects. As result of a research project of Gazpromneft in a partnership with IBM Research, an innovative approach was developed for the objective integration of geological and geophysical data. The main idea of this approach is to support the geologist's decisions by an intelligent assistant working on the principles of the modern theory of knowledge engineering. Using the generalized expert knowledge, the intelligent assistant impartially integrates disparate geological information into a set of conceptual geological models (scenarios, objectively evaluates their probabilities, and helps to plan optimal exploration/appraisal activities.

A comparative analysis on the entrepreneurial ecosystem of BRICS club countries: practical emphasis on South Africa

The effectiveness of entrepreneurial activities is not only determined by the quality of entrepreneurs but also by the ecosystem of entrepreneurship. The entrepreneurial ecosystem (EE) that nurtures low-quality “moppets” to highly impactful “gazelles” is being widely debated and on-demand in literature. This study, therefore, is aimed to advance the discussion and make a comparative analysis of the entrepreneurial ecosystem, which has been given a little attention, of BRICS club countries with an especial focus on South Africa, Brazil, and India. Various entrepreneurship-economic growth-related measures including Global Entrepreneurship Index (GEI), Global Competitiveness Index (GCI), Index Economic Freedom (IEF), and Legatum Prosperity Index (LPI) are used to compare the countries’ entrepreneurial ecosystem. Especially, the data set (2012–2018) of GEI was utilized for the analysis. According to GEI and GCI of 2018, China is leading BRICS club in terms of growth and entrepreneurial ecosystem. On the other side, LPI, IEF, and GEI put South Africa’s entrepreneurial ecosystem in a favorable position as compared to Brazil and India. South Africa performs poorly in startup skills, while both the latter ones are better and stand at the same level. This shows that South Africa’s tertiary education, coupled with low skill perception, is less effective in equipping the population to be entrepreneurs as compared to India and Brazil. Whereas Brazil and India are at their worst in internationalizing the country’s entrepreneurs and technological absorption, respectively. South Africa is more like India in product innovation and risk acceptance. On the other side, it is more like Brazil in risk capital, technological absorption, opportunity perception, and in their sluggish economic growth. Overall, South Africa (57th/140 as of 2018) is categorized among those poorly performing countries in terms of start-up skills, networking, technology absorption, human Capital, and risk capital pillars. The government of South Africa needs to primarily work on these bottle-neck pillars to improve its EE. To increase GEI by 5%, it should invest 77% of its extra resource on start-up skills, 18% on risk capital, and 5% on technology absorption. Applying GEI set up, this paper claims to have uniquely contributed to how to make a country comparison on the EE. Further empirical research can be done including all BRICS countries to bolster their development effort and on how to promote EE by tackling the underlying bottlenecks.

Critical Evaluation Capital (CEC): A New Tool for Applying Critical Race Theory to the Evaluand

This article presents a new tool called Critical Evaluation Capital (CEC) designed to address issues of equity and social justice in program evaluation. CEC is grounded in the tenants of critical race theory and inspired by Yosso’s work on community cultural wealth which raises critical issues of positionality and access. CEC is a system for identifying, quantifying, and disrupting the impact of different kinds of power and privilege (named here as capital) that influence the evaluation process and may distort its findings and/or alter its impact. CEC is not meant to be an entirely new evaluation framework or approach, but rather it is designed to be used as a “tool” in conjunction with other contemporary evaluation methodologies, specifically those that reposition the role of the evaluation from an “objective” outsider to an engaged stakeholder. I introduce and describe herein seven foundational categories of CEC, including framing capital, identity capital, connectivity capital, inquiry capital, risk capital, symbolic capital, and dissemination capital, along with a series of accompanying critical questions to guide reflective practice for each capital. I also describe how CEC can be applied across the evaluand—both proactively and retrospectively. I conclude with some key opportunities and challenges CEC presents for evaluators and other key stakeholder groups in the evaluand.

Covariance Principle for Capital Allocation: A Time-Varying Approach

The covariance allocation principle is one of the most widely used capital allocation principles in practice. Risks change over time, so capital risk allocations should be time-dependent. In this paper, we propose a dynamic covariance capital allocation principle based on the variance-covariance of risks that change over time. The conditional correlation of risks is modeled by means of a dynamic conditional correlation (DCC) model. Unlike the static approach, we show that in our dynamic capital allocation setting, the distribution of risk capital allocations can be estimated, and the expected future allocations of capital can be predicted, providing a deeper understanding of the stochastic multivariate behavior of risks. The methodology presented in the paper is illustrated with an example involving the investment risk in a stock portfolio.

Banks' Backtesting Exceptions during the COVID-19 Crash: Causes and Consequences

Banks' numerous and simultaneous backtesting exceptions in March 2020, during the COVID-19-related market crash, would have amplified their already-large spike in market risk capital requirements in the absence of regulatory intervention. This note provides background on how backtesting exceptions affect capital requirements generally, the source of those exceptions during the COVID-19 crash, and how regulators exercised discretion to mitigate the unintended capital increase.