Investigating the Factors Influencing Shadow Banking in EU Member States

ABSTRACT

This paper investigates the driving forces of shadow banking in 27 EU Member States, using annual data for 1999–2020. To account for heterogeneity, the panel is split into two sub-groups labeled “old” Europe and Eastern Europe. The estimations provide evidence that bank assets, insurance corporation assets, interest rate spreads, and regulatory quality significantly determine shadow banking growth. Financial development also has a considerable influence. The strong link between shadow banking entities and insurance corporations highlights the need to create a framework to test the interconnectedness of financial institutions at the EU level.

KEYWORDS:

• Financial development
• panel data models
• shadow banking
• traditional banking

JEL CLASSIFICATION:

• G21
• E44
• E50

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. Austria, Belgium, Bulgaria, Croatia, the Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, the Netherlands, Poland, Portugal, Romania, the Slovak Republic, Slovenia, Spain, and Sweden.

2. The financial crises periods were selected based on the new European financial crises database (please refer to Duca, 2017, for the underlying paper describing the methodology).

3. Hsiao (2014) convincingly provides the justification for the panel data analysis. It provides several benefits: (1) the use of panel data enables control for individual heterogeneity; (2) panels provide more informative data, more variability, less collinearity among the variables, greater degree of freedom, and higher efficiency; (3) with panel data, one is better equipped to study the dynamics of adjustment; (4) panel data are more suitable for identifying and measuring effects that are simply not detectable in pure cross-sections or pure time-series data; and (5) panel data models allow for constructing and testing more complicated behavioral models than pure cross-section or time data models do.

Additional information

Notes on contributors

Mihail Petkovski

Mihail Petkovski, PhD, Professor at Faculty of Economics, Ss. Cyril and Methodius University, Blvd. Goce Delchev 9V1000 Skopje, Republic of Macedonia affiliation: financial markets and institutions, international finance, and macroeconomics.

Aleksandar Stojkov

Aleksandar Stojkov, PhD, Full Professor of Economics, Iustinianus Primus Law Faculty, Ss. Cyril and Methodius University, Blvd. Goce Delchev 9b, 1000 Skopje

Jordan Kjosevski

Jordan Kjosevski, PhD (corresponding author),Pitu Guli 5, Ohrid, Macedonia, S/N Broker. Scientific affiliation: macroeconomics; financial management.