One of the pressing problems in the modern development of the world financial system is an excessive increase in state debt, which has many negative consequences for the financial system of any country. At the same time, special attention should be paid to developing an effective state debt management system based on its forecast values. The paper is aimed at determining the level of persistence and forecasting future values of state debt in the short term using time series analysis, i.e., an ARIMA model. The study covers the time series of Ukraine’s state debt data for the period from December 2004 to November 2020. A visual analysis of the dynamics of state debt led to the conclusion about the unstable debt situation in Ukraine and a significant increase in debt over the past six years. Using the Hurst exponent, the paper provides the calculated value of the level of persistence in time series data. Based on the obtained indicator, a conclusion was made on the confirmation of expediency to use autoregressive models for predicting future dynamics of Ukraine’s state debt. Using the EViews software, the procedure for forecasting Ukraine’s state debt by utilizing the ARIMA model was illustrated, i.e., the series was tested for stationarity, the time series of monthly state debt data were converted to stationary, the model parameters were determined and, as a result, the most optimal specification of the ARIMA model was selected.

An excessive increase in public debt characterizes the contemporary development of the global economic and financial system. The paper aims to examine the short- and long-run impact of state debt on economic growth in Nigeria. The model was estimated using an autoregressive distributed lag (ARDL) bounds testing method to co-integration for the long-run investigation. At the same time, the contemporaneous dynamics were explored using an unrestricted error correction model. The data were collected from the Central Bank of Nigeria’s statistical bulletins and annual reports, and it spanned the years from 1990 to 2020. The study uncovers evidence of a long-term link between the study variables. In addition, the study finds that all the explanatory is statistically significant. Specifically, economic growth is significant and negatively responsive to changes in external debt by 0.19% and debt servicing by 0.07%, contrary to its positive response to changes in domestic debt and exchange rate by 0.27% and 0.18%, respectively. The paper, therefore, recommends that government may consider more domestic borrowings to foreign borrowings that should only be resorted to when it is indispensable. Moreover, the government should also strive to balance loan servicing and the economic sustainability.

The implementation of international standards for the bank risk assessment and market risk, in particular, in Ukrainian banking practice is aimed at achieving common standards for regulating banking activities in different countries. This should help to increase the banking sector stability in Ukraine and, accordingly, increase the interest of foreign investors.The article deals with the methodological approaches to assessing the bank market risk (in particular, SA, IMA and R-SbM approaches) recommended by the Basel Committee on Banking Supervision in terms of standardization and unification of the normative framework of capital requirements for Ukrainian banks. Considering the analysis results, it was determined that the choice and implementation of an optimal approach in the context of Ukrainian banking practice can be carried out in one of two alternative scenarios: 1) a simplified version of a sensitivity based method (R-SbM); and 2) a recalibrated version of the Basel II standardized approach. In this case, the Basel II recalibrated version is more acceptable for use by banks, since it is most relevant to volume and complexity of transactions carried out by Ukrainian banks.The obtained results are aimed at improving the existing methodology for calculating the adequacy ratio of banks' regulatory capital (N2), which currently considers only the needs for credit risk coverage, and at refining the methodology in terms of considering banks' market-risk coverage needs.

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 paper explores theoretical and practical aspects of forecasting the government debt in Ukraine. A visual analysis of changes in the amount of government debt was conducted, which has made it possible to conclude about the deepening of the debt crisis in the country. The autoregressive integrated moving average (ARIMA) is considered as the basic forecasting model; besides, the model work and its diagnostics are estimated. The EViews software package illustrates the procedure for forecasting the Ukrainian government debt for the ARIMA model: the series for stationarity was tested, the time series of monthly government debt was converted into stationary by making a number of transformations and determining model parameters; as a result, the most optimal specification for the ARIMA model was chosen.Based on the simulated time series, it is concluded that ARIMA tools can be used to predict the government debt values.