Debt security forecasting models

Issue: № 12/2, 2021

Doi: https://doi.org/10.37634/efp.2021.12(2).5

The paper examines models for predicting Ukraine's debt security. The main purpose of the paper is to reveal the theoretical and practical component of debt security forecasting of various types of forecast models that can be used to predict debt parameters. In the course of the study, attention is focused on debt forecasting as a factor in minimizing financial and debt shocks. The interpretation of the definition of debt security in a broad and narrow sense is given. The linear trend model of forecasting debt indicators is studied and it is concluded that it acts as a fundamental element in the process of debt forecasting, but cannot guarantee high quality planning of future indicators. A study was also conducted in the context of medium-term forecasting using ARIMA and multivariate regression models, which showed satisfactory qualitative characteristics in the process of simulating future debt security parameters. Forecasting of financial and debt parameters using ARIMA models, in the context of econometric and statistical quality, showed a sufficient level of adequacy of regression equations. The ARIMA model for simulating future values of debt parameters is characterized by an approximate value of the integral debt security indicator to the regulatory level. Based on the obtained models, a comparative characteristic of the studied models is carried out with a comparison of the obtained results of the integral debt security indicator and calculation of the absolute financial strength margin indicator based on them. Based on the results of the study, it can be concluded that the use of ARIMA models and multiple regressions is the optimal method for predicting debt with a high level of adequacy of the simulated values and rather low labor costs in the procedural context. Priority areas of future research are highlighted.

Keywords : debt security, debt forecasting, debt indicators, integral debt security indicator

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