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Networks of volatility spillovers among stock markets

Eduard Baumohl (), Evžen Kočenda (), Štefan Lyócsa () and Tomáš Výrost ()

Physica A: Statistical Mechanics and its Applications, 2018, vol. 490, issue C, 1555-1574

Abstract: In our network analysis of 40 developed, emerging and frontier stock markets during the 2006–2014 period, we describe and model volatility spillovers during both the global financial crisis and tranquil periods. The resulting market interconnectedness is depicted by fitting a spatial model incorporating several exogenous characteristics. We document the presence of significant temporal proximity effects between markets and somewhat weaker temporal effects with regard to the US equity market — volatility spillovers decrease when markets are characterized by greater temporal proximity. Volatility spillovers also present a high degree of interconnectedness, which is measured by high spatial autocorrelation. This finding is confirmed by spatial regression models showing that indirect effects are much stronger than direct effects; i.e., market-related changes in ‘neighboring’ markets (within a network) affect volatility spillovers more than changes in the given market alone, suggesting that spatial effects simply cannot be ignored when modeling stock market relationships. Our results also link spillovers of escalating magnitude with increasing market size, market liquidity and economic openness.

Keywords: Volatility spillovers; Stock markets; Shock transmission; Granger causality network; Spatial regression; Financial crisis (search for similar items in EconPapers)
JEL-codes: C31 C58 F01 G01 G15 (search for similar items in EconPapers)
Date: 2018
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Working Paper: Networks of Volatility Spillovers among Stock Markets (2017) Downloads
Working Paper: Networks of volatility spillovers among stock markets (2016) Downloads
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