Quantitative Finance and Economics, 2018, 2(4): 798-820. doi: 10.3934/QFE.2018.4.798

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Application of systemic risk measurement methods: A systematic review and meta-analysis using a network approach

Viktorija Dičpinigaitienė, Lina Novickytė

1 Faculty of Economics and Business Administration, Vilnius University, Sauletekio ave. 9 (II bldg.), LT–10221 Vilnius, Lithuania
2 Lithuanian Institute of Agrarian Economics, V. Kudirkos st. 18–2, LT–01113 Vilnius, Lithuania

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Special Issues: Systemic Risk Measurement

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This article presents an analysis of the literature on systemic risk measurement methods. Only the recent global crisis has particularly attracted the attention of researchers on systemic risk measurement. Global challenges such as Big Data, AI, IoF, etc. also have an impact on expanding the systemic risk measurement capabilities. The growing number of publications in the last decade opens the door to deeper insights into the systemic risk measurement features, summarizing the contribution of research and analyse the mainstream research on systemic risk, identify the strengths and weaknesses of the studies. Therefore, the main objective of this study is to provide a framework to address the relevant gaps in the current discussion on systemic risk measurement by conducting a wide search in Scopus database to identify the studies that used different systemic risk measurement in the period from 2009 to January 2018. A meta-analysis of scientific articles is performed based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method and using network approach presents the main interconnection of the methods used to measure systemic risk. A critical analysis of these articles addresses some important key issues. The results of this review are important: they will help researchers to develop better research methods and models around systemic risk measurement. Based on the results, it has allowed us to identify the key issues in choosing a method to assess systemic risk and to help researchers avoid pitfalls in using these methods.
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