Title: Dynamic connectedness measures via MIDAS SVAR

Authors: Andrianos E. Tsekrekos; Konstantinos I. Vasileiadis

Addresses: Department of Accounting and Finance, Athens University of Economics and Business, Greece ' Department of Accounting and Finance, Athens University of Economics and Business, Greece

Abstract: We employ mixed data sampling structural vector autoregressive (MIDAS-SVAR) models for the calculation and comparison of various connectedness measures based on structural forecast error variance decompositions. We demonstrate the validity and significance of our proposed mixed data sampling connectedness measures in the context of oil shock propagation to the stock markets of oil-exporting and oil-importing countries from January 1998 to September 2017. Our results suggest that by enriching (via the MIDAS-SVAR approach) the information set from which connectedness measures are estimated, we can improve our understanding of dynamic spillover effects and time-varying relationships. The full exploitation of the underlying information structure in time series that might be sampled at different frequencies, enables the MIDAS-SVAR approach to generate more accurate connectedness results and enhance its efficiency in a statistically and economically significant manner, with obvious implications for risk management and financial stability.

Keywords: oil price shocks; connectedness; mixed data sampling; structural vector autoregressive models; MIDAS-SVAR.

DOI: 10.1504/IJBAAF.2026.155771

International Journal of Banking, Accounting and Finance, 2026 Vol.16 No.1, pp.95 - 134

Received: 16 Apr 2024
Accepted: 26 May 2025
Published online: 13 Aug 2026 *

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