State debt assessment and forecasting: time series analysis
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 manageme...
Main Authors: | , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
LLC "CPC "Business Perspectives"
2021-01-01
|
Series: | Investment Management & Financial Innovations |
Subjects: | |
Online Access: | https://businessperspectives.org/images/pdf/applications/publishing/templates/article/assets/14618/IMFI_2021_01_Zhuravka.pdf |
id |
doaj-a9946fc02f5f4b0682aefeace694d649 |
---|---|
record_format |
Article |
spelling |
doaj-a9946fc02f5f4b0682aefeace694d6492021-04-08T13:06:31ZengLLC "CPC "Business Perspectives"Investment Management & Financial Innovations 1810-49671812-93582021-01-01181657510.21511/imfi.18(1).2021.0614618State debt assessment and forecasting: time series analysisFedir Zhuravka0https://orcid.org/0000-0001-8368-5743Hanna Filatova1https://orcid.org/0000-0002-7547-4919Petr Šuleř2https://orcid.org/0000-0001-7562-0659Tomasz Wołowiec3https://orcid.org/0000-0002-7688-4231Doctor of Economics, Professor, Department of the International Economic Relations, Sumy State UniversityPh.D. Student, Department of the International Economic Relations, Sumy State UniversityPh.D., School of Expertness and Valuation, Institute of Technology and Business in Ceske BudejoviceDr., Hab,. Associate Professor, Department of Administration and Social Sciences, University of Economics and Innovation in Lublin (WSEI)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.https://businessperspectives.org/images/pdf/applications/publishing/templates/article/assets/14618/IMFI_2021_01_Zhuravka.pdfARIMA modeldebtdebt securitypersistencetime series analysisUkraine |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Fedir Zhuravka Hanna Filatova Petr Šuleř Tomasz Wołowiec |
spellingShingle |
Fedir Zhuravka Hanna Filatova Petr Šuleř Tomasz Wołowiec State debt assessment and forecasting: time series analysis Investment Management & Financial Innovations ARIMA model debt debt security persistence time series analysis Ukraine |
author_facet |
Fedir Zhuravka Hanna Filatova Petr Šuleř Tomasz Wołowiec |
author_sort |
Fedir Zhuravka |
title |
State debt assessment and forecasting: time series analysis |
title_short |
State debt assessment and forecasting: time series analysis |
title_full |
State debt assessment and forecasting: time series analysis |
title_fullStr |
State debt assessment and forecasting: time series analysis |
title_full_unstemmed |
State debt assessment and forecasting: time series analysis |
title_sort |
state debt assessment and forecasting: time series analysis |
publisher |
LLC "CPC "Business Perspectives" |
series |
Investment Management & Financial Innovations |
issn |
1810-4967 1812-9358 |
publishDate |
2021-01-01 |
description |
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. |
topic |
ARIMA model debt debt security persistence time series analysis Ukraine |
url |
https://businessperspectives.org/images/pdf/applications/publishing/templates/article/assets/14618/IMFI_2021_01_Zhuravka.pdf |
work_keys_str_mv |
AT fedirzhuravka statedebtassessmentandforecastingtimeseriesanalysis AT hannafilatova statedebtassessmentandforecastingtimeseriesanalysis AT petrsuler statedebtassessmentandforecastingtimeseriesanalysis AT tomaszwołowiec statedebtassessmentandforecastingtimeseriesanalysis |
_version_ |
1721535196409364480 |