Networks of volatility spillovers among stock markets

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. We show significant temporal proximity effects between markets and somewhat weaker temporal effects with regard to the US equity marketvolatility spillovers decrease when markets are characterized by greater temporal proximity. Terms of use: Documents inVolatility spillovers also present a high degree of interconnectedness. Our results also link spillovers of escalating magnitude with increasing market size, market liquidity and economic openness.JEL-Codes: C310, C580, F010, G010, G150.Keywords: volatility spillovers, stock markets, shock transmission, Granger causality network, spatial regression, financial crisis. We benefited from helpful comments we received from two anonymous referees and participants at several presentations. Eduard Baumöhl University of Economics in Bratislava

Quantile coherency networks of international stock markets

“…In our analysis, we find that (a) European developed markets are the most connected markets; (b) emerging and frontier markets are (apart from a few exceptions) still not strongly connected, even after the GFC; (c) geographical proximity matters, especially in propagating negative shocks; and (d) stock market connectedness increased after the GFC, from both short-and long-term perspectives and for extreme positive and extreme negative returns. In general, our results are in line with extant literature on stock market networks (e.g., Coelho et al, 2007;Baumöhl et al, 2018;Wang et al, 2018).…”

An analysis of CEE equity market integration and their volatility spillover effects

“…This paper reports that emerging markets are less linked to the developed market in terms of returns and weak co-movement between stock markets. More recently, Baumöhl et al (2018) show the persistence of significant temporal proximity effects between markets and somewhat weaker temporal effects with regard to the US equity market, provide evidence of volatility spillovers that present a high degree of interconnectedness. The models used in this paper are ARFIMAX-GARCH.…”

A Weighted and Directed Perspective of Global Stock Market Connectedness: A Variance Decomposition and GERGM Framework

“…As the increased fluctuation in the foreign exchange markets is proved to induce higher volatility spillover in the stock market [10], sufficient exchange reserves can be regarded as a buffer stock to respond to fluctuations in international payments, ensuring that countries can calmly respond to sudden financial crisis and meet the demands for maintaining stable local currency exchange [32]. (2) External debt: Both the external debt sender and receiver effects are negative in subprime crisis.…”

“…The inner logic of financial networks is that the intricate connectedness, either physical based on bilateral exposures/flows between financial institutions, or association-based depicting return dependency among financial markets, could be captured and analyzed in complex financial systems using a network approach [1,2,6]. The network approach, which describes relationship architecture and regularities involved in complex multivariate systems, has become a powerful tool in financial crises early warning and tracking [7,8], risk spillover sources tracing [9,10], or exploitation of asset allocation [11,12]. Three research paradigms exist in the current financial network literature [1], namely, (i) mean-spillover network or Granger-causality network [13], (ii) volatility spillover network represented by variance decomposition-based network [14] and GARCH-based network [15], and (iii) risk spillover network with the main forms in tail-risk driven network [16] and extreme risk network [8].…”

“…Scholars seek to explain the influential factors that amplify or mitigate volatility spillover in terms of equity market importance, development and liquidity [10,[21][22][23], investment behaviors and macroeconomic fundamentals [1,20], capital account flow [24,25], financial crisis [20,26,27], and financial institution linkages [3,28]. For instance, Baumöhl et al [10] used spatial regression to model the volatility spillover network of 40 developed, emerging, and frontier stock markets between 2006 and 2014 to identify escalating magnitude of spillovers with increasing market size, market liquidity, and economic openness. Applying the same method in the G20 stock network, Zhang et al [1] found that government debt and inflation are positively correlated with spillover risk.…”