The Ukrainian PFTS stock index volatility reaction as a whole and its constituent economic sectors (“Basic Materials”, “Financials”, “Industrials”, “Oil & Gas”, “Telecommunications”, “Utilities”) to seven non-monetary US information signals (“Consumer price index”, “Personal spending”, “Unemployment rate”, “Gross domestic product”, “Industrial production”, “Consumer confidence”, “Housing starts”) was carried out for the period 2000–2017 on the basis of closing stock quotations in the trading day format. To assess the “surprise” component direct influence nature of the USA selected non-monetary information signals on (...) the PFTS stock index, an AR-GARCH econometric modelling device was used. The results achieved clearly indicate the presence of some PFTS stock index economic sectors heterogeneous reaction to the United States individual non-monetary information signals announcement. For example, such economic sectors as “Basic Materials”, “Financials”, and “Oil & Gas” volatility response to the US non-monetary information signal “Consumer price index” “surprise” components the opposite of the overall PFTS stock index reaction. It can also be concluded that the United States non-monetary information signals influence on the Ukrainian stock market volatility depends not only on the financial cycle phase and data frequency, but also on the PFTS stock index economic sector. (shrink)

Авторами розглянуто вплив провідних світових кредитних рейтингових агентств на фінансову безпеку емітентів цінних паперів. Обґрунтовано перформативний характер діяльності кредитних рейтингових агентств з позицій моделі «Les economies de la grandeur» нового соціологічного французького інституціоналізму, що дає змогу розв’язати протиріччя щодо сильної опозиції стосовно їх функціонування з одночасним збереженням діючої системи нормативного використання кредитних рейтингів.

A method of identifying banks’ business models and studying the features of their risk profile, considering the system of indicators featuring the structure of assets, liabilities, income, expenses, and other qualitative indicators based on monthly statistical reporting. Kohonen's self-organizing maps (SOM) are used to process large data sets, revealing objects’ hidden features by forming homogeneous groups according to similar values of a large system of indicators. The choice of the system of indicators that play the most significant role in describing (...) the business models of modern banks is substantiated. The proposed method makes it possible to group banks with homogeneous characteristics into so-called structural-functional groups and studies the change in the characteristics of groups of banks over time to compare their behavior during periods of active development of the system and during a crisis. That approach is useful for studying the banking system at the macro level, as it provides a quantitative measure of its financial stability. The more banks are in groups with negative values of parameters, increased risks, and unprofitable performance, the worse the general state of the system. The method also allows studying the features of each structural and functional group and the business models’ features at the meso-level. The number and composition of banks inherent in any group change dynamically, which characterizes the features of the relevant business model in a particular period. The averages of each group reflect the objective changes in the banking system structure. In addition, the SOM trajectory can be built for each individual bank determining the development of its strategy, features of a particular business model, and risk profile. At the micro-level, it allows comparing the features of individual banks within the SFGB and models ways to improve efficiency and financial stability by forecast values for SOM. An extensive system of indicators used to form structural and functional groups of banks allows to quickly respond to changes in the banking system, identify areas of increased risk and explore the adequacy and effectiveness of banks’ business models. (shrink)