Debt management evaluation through Support Vector Machines: on the example of Italy and Greece

Andrey Zahariev (), Mikhail Zveryаkov (), Stoyan Prodanov (), Galina Zaharieva (), Petko Angelov (), Silvia Zarkova and Mariana Petrova ()
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Andrey Zahariev: D. A. Tsenov Academy of Economics, Bulgaria
Mikhail Zveryаkov: Odessa National University of Economics, Ukraine
Stoyan Prodanov: D. A. Tsenov Academy of Economics, Bulgaria
Galina Zaharieva: D. A. Tsenov Academy of Economics, Bulgaria
Petko Angelov: D. A. Tsenov Academy of Economics, Bulgaria
Mariana Petrova: St. Cyril and St. Methodius University of Veliko Turnovo, Bulgaria

Entrepreneurship and Sustainability Issues, 2020, vol. 7, issue 3, 2382-2393

Abstract: The focus of this research paper is on sovereign debt management evaluation. During the first decade of the 21st century, the PIIGS countries in the EU28 were the main generator of risks in in the public finance sector, thus creating a threat for cross-border economic shocks. In 2018, Greece and Italy had the worst debt-to-GDP ratios and were earmarked as a benchmark for countries with sovereign debt problems. Greece is an example of a country with a non-systematic risk for the EU due to its low share of EU28’s GDP of 1.16% (as of 2018) despite its record debt ratio of 176%. However, Italy is not only among the top 4 EU28 economies with a share of its national GDP in that of the EU28 of 11.1%, but also has a record debt ratio of 131%, which is significant for one of the top economies in the EU28 group. In view of the above, the paper is structured into three main sections. Section One presents an analysis of the efficiency of sovereign debt management as a key element of public finance management in the 28 EU Member States. Section Two presents a justification of the use of the Support Vector Machines (SVM) method for econometric analysis of macroeconomic data. Section Three presents groups and empirically tested internal and external indicators that affect the debt ratio of Italy and Greece. The analysis was conducted with quarterly time series of data for the period 2000-2018 using support vector regression (SVR) for sovereign debt testing calculated using software for interactive and functional programming - Python. The test results and their vector distribution in terms of SVR are presented as histograms. The main conclusion is that both for Greece and for Italy, there is a strong correlation between the SVM support vectors obtained through the algorithm, which is also due of the strict selection of indicators whose correlation is reformatted by the model algorithm, limiting its negative significance on the final result.

Keywords: Support Vector Machines (SVM); support vector regression (SVR); public debt to GDP ratio; debt management (search for similar items in EconPapers)
JEL-codes: C52 E62 G28 H63 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:ssi:jouesi:v:7:y:2020:i:3:p:2382-2393

DOI: 10.9770/jesi.2020.7.3(61)

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