Financial security of a country is an integral part of its economic security and the basis of national security. The paper aims to assess and forecast the level of Ukraine’s financial security using two methodological approaches (the existing one and the authors’ elaboration) to choose the best alternative. The first one is based on the Methodology of the Ministry of Economy of Ukraine. The alternative one has been developed as a multiplicative model of non-linear convolution of relevant direct and indirect impact indicators, considering the opportunity and risk, which is based on a combination of a power function and the Harrington method. A database of input indicators was formed with further differentiation according to their impact on Ukraine’s financial security. The research results demonstrated that during 2013–2019 Ukraine’s financial security integrated index was cyclical and constantly changing. A comparison of the existing methodology and the developed model demonstrated a certain discrepancy between the obtained results. It was substantiated that the proposed multiplicative non-linear convolution model for assessing and forecasting the state’s financial security is more relevant, includes current indicators sorted by their direct and indirect impact, and adjusts them according to the risk of impact on overall security in the country.

The stronger the level of economic integration between countries, the greater the need to study the formation patterns of the stock market reaction to the financial information signals. This concerns the Ukrainian stock market, which is now in its infancy, and which reaction to financial information signals is sometimes ambiguous. The research aims to identify the formation patterns of return and volatility indicators of the Ukrainian stock market reaction to the US financial information signals. To assess the direct nature of US financial information signals effect on the PFTS stock index, the GARCH econometric modeling toolkit was applied. The research information base is the PFTS stock index and the Federal Reserve System financial information signals at the discount rate for 2000–2019. The fetch is divided into intervals corresponded to the ascent and decline phases of the financial cycle. It was found that an unforeseen increase in the discount rate at the financial cycle decline phase by 25 basis points decreases the PFTS stock index return, on average by 2.9%. Besides, the hypothesis about the general change stabilizing effect in the discount rate on the Ukrainian stock market volatility at the financial cycle growth phase was confirmed. Nevertheless, for investors, the most essential is the regulator’s monetary signals in the discount rate at the financial cycle decline phases rather than at the ascent phases because there is a more significant increase in the volatility level.

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.

The article analyzes changes in the business models of Ukrainian banks using the author's method of structural and functional groups of banks (SFGB). The method’s basis is the processing, systematization, and visualization of the system’s values of banks’ financial indicators using Kohonen’s self-organizing map (SOM). Depending on the level distribution of a large number of indicators that characterize the structure of assets, liabilities, income, expenses, and other qualitative indicators that describe the business models of each bank on successive reporting dates, homogeneous groups of banks are formed. The purpose of this study is to compare the key features of the banking system as of January 1 and September 1, 2022, and the corresponding changes in business models.Over the eight months of 2022, the number of banks with corporate lending increased slightly, but the resource base of these banks gradually changed. The number of banks with retail financing decreased at the expense of banks with current resources. During an increase in the discount rate and in the price of refinancing loans, banks’ business model that attracts resources on the interbank market and places them in securities has shrunk. At the same time, the number of banks with an increased level of securities in assets and corporate financing increased. The quality of the portfolio indicates the increased credit risks of the respective large state banks.The drawback of the proposed method is the dependence of conclusions on official banks 'statements that do not always reflect nuisances of financial position. Within small banks, we can sometimes observe that current changes in clients' account balances affect the position in SFGB. The SFGB method can be applied to analyze trends and estimate the probability of subsequent structural changes. For each bank, one can observe the trajectory change on the map and investigate the reasons for the change in business strategy.

number of observations-4436). We used the toolkit of vector autoregressive modelling to determine the sources of Ukrainian stock index PFTS response to the US non-monetary information signals, which is based on the decomposition of changes in stock market excess return through the channels of economic transmission ("expected future dividends", "real interest rate" and "risk premium") and takes into account the unexpected values of the informational context of selected non-monetary signals. Target time series are stationary according to the KPSS and ADF criteria. The results show that four of the six selected non-monetary information signals of the USA do not have a significant effect on the response of endogenous variables of econometric model. The existence of significant direct influence of US non-monetary informational signals "Personal Spending" and "Consumer Confidence" on the response of the excess return of Ukrainian stock index PFTS has been established. It is substantiated that the actual and forecast state of the USA national economy is considered by the participants of the local stock markets, in particular in Ukraine, as one of the most important sources of macroeconomic information while making strategic and tactical investment decisions. Thus, the increasing importance of the component of "surprise" of such non-monetary information signals of the USA is considered as "positive" news for the domestic stock market by investors, which increases the excess return of the stock index PFTS.