Net Transmitter Of Shocks Research Articles

Financial connectedness of BRICS and global sovereign bond markets

The paper examines the financial connectedness via return and volatility spillovers between Brazil, Russia, India, China and South Africa (BRICS) and three global bond market indices represented by the United States of America (USA), European Monetary Union (EMU) and Japan for the period 01 January 1997 to 27 July 2016 (weekly data). We find that Russia followed by South Africa is the net transmitter of shocks within BRICS, implying that the risk arising from these markets may have an adverse impact on others in BRICS. However, China and India exhibit weak connectedness, suggesting that these markets may be useful for hedging and diversification opportunities in BRICS. The networks of pairwise spillover results further confirm this. Among global indices, China appears as highly interconnected with the USA. USA is the strongest transmitter of shocks to BRICS bond indices. The panel data results further confirm the significant determinants of net directional spillover. Thus, we can conclude that BRICS is a heterogeneous asset class even in the case of the bond market. India and China are the markets to look for better risk management strategies.

Dynamic volatility spillovers and connectedness between global, regional, and GIPSI stock markets

This paper investigates the volatility spillover and connectedness among global and regional stock markets and those of Greece, Ireland, Portugal, Spain, and Italy (GIPSI). For this investigation, we perform a static and rolling-window analysis to measure volatility spillovers using the Diebold and Yilmaz (2012, 2014) methodology. We also examine the network connectedness at different stages of recent crises. Our finding indicates that recent crises intensify volatility spillovers, supporting the financial contagion effects. Furthermore, the GIPSI (except Spain and Greece markets), global and regional U.S. markets are net transmitter of shocks while the rest stock markets are net receiver of shocks.

Do Shining Precious Metals Overshadow Other Assets? Dynamic Spillovers Between Commodity and Currency Markets

In this study, we examine the dynamic link between returns and volatility of commodities and currency markets. In particular, based on weekly data on gold, silver, platinum, palladum, oil and the USD/EUR exchange rate over the period January 6, 1987 to July 22, 2014 we find the following empirical regularities. First, the static spillover analysis reveals that gold, silver and platinum are net transmitters of returns and volatility spillovers, while palladium, crude oil and USD/EUR exchange are net receivers of returns (volatility) spillovers. These results suggest that, the information contents of gold, silver and platinum can help improve forecast accuracy of returns (volatilities) on palladium, crude oil and currency returns (volatilities). Second, returns (volatilities) on gold are interestingly the largest gross transmitters of spillovers to the remaining assets in our model. Third, pairwise return (volatility) spillovers reveal relatively stronger bidirectional interdependences between gold and silver, and platinum and palladium. Fourth, the analysis of dynamic spillovers suggests that gold, silver and, to a lesser extent, platinum act as net transmitters of return (volatility) spillovers to palladium, crude oil and the USD/EUR exchange rate. Interestingly, the observed net transmission shows time- and event-specific patterns. For instance, for gold and silver (platinum) returns and volatility, it intensified (degraded) in the period marked by the global financial crisis. After the global financial crisis, the role of gold and silver (platinum) returns as net transmitters of shocks weakened (strengthened), while the role of gold, silver and platinum volatility as net transmitters of shocks remained relatively high. Overall, our findings reveal that, while the static analysis clearly classifies the aforementioned variables into net transmitters and net receivers, the dynamic analysis denotes episodes wherein the role of transmitters and receivers of return (volatility) spillovers can be interrupted or even reversed. Hence, even if certain commonalities prevail in each identified category of commodities, such commonalities are time- and event-dependent.