Global financial interconnectedness: a non-linear assessment of the uncertainty channel

Abstract

The role of uncertainty in the global economy is now widely recognized by policy-makers but its specific effects on the international financial system are less understood. In this paper we assess the impact of uncertainty fluctuations on the interconnectedness within the international system of equity prices. In this respect, we extend the measure of connectedness put forward by Diebold and Yilmaz ( 2009) by allowing for non-linear effects through the estimation of a non-linear Threshold VAR model whose regimes depend on the level on uncertainty. Results show that high uncertainty tends to generate more connectedness among equity indexes of a set of advanced and emerging countries. From an economic policy point of view, this result suggests that in the presence of high uncertainty, an adverse financial shock in a specific country is likely to propagate more widely and more strongly to the whole financial system. This result advocates for a close real-time monitoring of uncertainty measures.

Returns and volatility connectedness among the Eurozone equity markets

“…Youssef et al (2020) utilized the TVP-VAR to document that the European stock markets transmit more shocks than they receive from all other stock markets during the COVID-19 pandemic. In line with the work of Diebold and Yilmaz (2015), Candelon et al (2018) documented that the major net transmitters of shocks are the United States and the United Kingdom, while other countries in their connectedness framework are net receivers of financial shocks. Also, Shahzad et al (2018) Another strand of the literature examines how the GFC altered the connectedness, analysing the periods before and after this crisis.…”

Interdependence and portfolio optimisation of bank equity returns from developed and emerging Europe

“…Some studies have used other measures of interconnectedness as a link in the bank network; these include credit default swap contracts (Ballester, Casu, & González-Urteaga, 2016;Gandy & Veraart, 2019;Kanno, 2018;Peltonen, Scheicher, & Vuillemey, 2014), and cross-ownership of equity shares (Cao, Gregory-Smith, & Montagnoli, 2018;Dastkhan & Gharneh, 2016, 2018Elliott et al, 2014;Elsinger, 2009). However, bank network, as defined in our study, is based on economic connections identified from stock market data (i.e., correlation network of stock return dynamics), as in Diebold and Yilmaz (2014), Brunetti et al (2015), and Candelon, Ferrara, and Joëts (2018). This view is generally accepted in the literature, as high correlations in the bank equity sector are the main source of systemic risk in the bank network.…”

Higher Moments Actually Matter: Spillover Approach for Case of CESEE Stock Markets

“…To serve as control variables, we collected the following: the European EPU index was collected from [61], the shadow rate for the ECB from [62], and the German VIX from [63]. The EPU index was previously found to be a significant factor in affecting the dynamics of stock markets in Europe and CESEE countries, as seen in [12,[64][65][66][67][68]. The effects of monetary policy on stock market dynamics have been significant for a long time now (see [69][70][71][72]).…”