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Title State debt assessment and forecasting: time series analysis
Authors Zhuravka, Fedir Oleksandrovych  
Filatova, Hanna Petrivna  
Šuleř, P.
Wołowiec, T.
Keywords debt
debt security
time series analysis
persistence
ARIMA model
Ukraine
Type Article
Date of Issue 2021
URI https://essuir.sumdu.edu.ua/handle/123456789/83330
Publisher LLC “СPС “Business Perspectives”
License Creative Commons Attribution 4.0 International License
Citation Fedir Zhuravka, Hanna Filatova, Petr Šuleř and Tomasz Wołowiec (2021). State debt assessment and forecasting: time series analysis. Investment Management and Financial Innovations, 18(1), 65-75. doi:10.21511/imfi.18(1).2021.06
Abstract 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.
Appears in Collections: Наукові видання (ННІ БіЕМ)

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