Investigating Systemic Risk with Co-occurrence Networks – Studies from the fields of sovereign bond and energy markets [védés előtt]

Kivonat, rövid leírás

Each of the three articles within this dissertation represents a distinct investigation within the broader research domain. While they may stand alone as individual contributions, they are interconnected by a common research theme, systemic risk. The impact of crisis periods and monetary decisions of the Fed and the ECB on the sovereign yield curve network This study investigates the sovereign yield curve network of 12 developed countries. The term structure of interest rates into the Level, Slope, and Curvature factors are decomposed by using the Diebold and Li model. The connections between the latent yield curve factors across the countries are measured using the Toda and Yamamoto model. The timeframe covers more than 23 years; therefore, it is possible to compare two global and two local crisis periods. Investigating the network on node level, in the entire sample period, all three US latent factors act as key participants in the network, however, their contribution is time variant. The network density differences on average are relatively small across calm and local crises periods, but significantly larger during the Global Financial Crisis and the European sovereign debt crises. Furthermore, links between the easing and tightening decisions by the Fed and the ECB, and the time-varying dominance of the US yield curve in our sovereign yield curve network are explored. The dominance of the US factors peaks if the Fed leads the hiking cycle and reaches its minimum when the interest rate cycle is led by the ECB. • Local and global crises behave differently. • US yield curve factors are the key participants of the network in calm periods as well as in local and global crises. • The dominance of the US factors peaks if the Fed leads the hiking cycle and reaches its minimum when the interest rate cycle is led by the ECB. Dynamic volatility transfer in the European oil and gas industry The study examines dynamic volatility transmissions among European energy industry participants along the production lines of Upstream, Midstream, Downstream, and Integrated Oil Gas segments. Using Diebold-Yilmaz spillover index, during the sample period of October 2006 to June 2022, significant internal volatility spillover is found among the European energy sector participants, primarily emanating from Upstream companies. In subsamples, it is shown that Downstream and Midstream segments can also become volatility transmitters under certain conditions. More importantly, the large Russian IOG companies became significant volatility transmitters after 2022 with the onset of Russia's war on Ukraine, potentially causing major system instability because these IOG firms were traditionally volatility absorbers in the network. Overall, insights are provided about the interconnectedness among European energy companies during normal and extreme market conditions and highlight important system dynamics that could be useful for policy makers and investors. • There is a significant volatility spillover among the energy sector participants, primarily emanating from Upstream firms. • In market turbulence Downstream and Midstream segments can also become volatility transmitters under certain conditions. • The total volatility spillover varies in time, with pronounced peaks during crisis periods. European equity markets volatility spillover: Destabilizing energy risk is the new normal In this study oil and natural gas price changes and shocks are examined in relation to equity market returns and volatility for 24 European Economic Area (EEA) countries. In addition to panel regressions, the Diebold-Yilmaz spillover index is also deployed for a closed network analysis. Differentiation is done in the cross-section across the core EU block, PIIGS countries, EU enlargement countries, and other non-EU countries, to provide insights into the debates on the European energy market stability. While evidence of energy risk throughout the sample period is found, it is shown that until 2019 the primary sources of volatility spillover in the EEA economic network arose from economic or political uncertainty. Energy risks, measured by large crude oil and natural gas price shocks also significantly contributed to equity market volatility, with increasing volatility risk arising from natural gas, a green labelled energy source after 2019. • Until 2019, economic or political uncertainties were the main drivers of volatility spillover within the EEA economic network. • The equity market experienced significant volatility due to energy risks, particularly with the growing volatility risk from natural gas starting in 2019. • Equity markets in the CEEC region become more affected by fluctuations in oil and natural gas prices when their domestic currencies weaken against the Euro.