Modelling international sovereign risk information spillovers: A multilayer network approach

Applying the TVP-VAR model, we creatively construct multilayer information spillover networks containing return spillover layer, volatility spillover layer and extreme risk spillover layer among 23 countries in the G20 to explore international sovereign risk spillovers. From the perspective of system-level and country-level measures, this article explores the topological structures of static and dynamic multilayer networks. We observe that (i) at the system-level, multilayer measures containing uniqueness edge ratio and average edge overlap show each layer has unique network structures and spillover evolution behavior, especially for dynamic networks. Average connectedness strength shows volatility and extreme risk spillover layers are more sensitive to extreme events. Meanwhile, three layers have highly intertwined and interrelated relations. Notably, their spillovers all show a great upsurge during the crisis (financial and European debt crisis) and the COVID-19 pandemic period. (ii) At the country-level, average overlapping net-strength shows that countries’ roles are different during distinct periods. Multiplex participation coefficient on out-strength indicates we’ll focus on countries with highly heterogeneous connectedness among three layers during the stable period since their underestimated spillovers soar in extreme events or crises. Multilayer networks supply comprehensive information that cannot obtain by single-layer.     

Upside/Downside spillovers between oil and Chinese stock sectors: From the global financial crisis to global pandemic

This paper examines the effects of the COVID-19 outbreak, recent oil price fall, and both global and European financial crises on dependence structure and asymmetric risk spillovers between crude oil and Chinese stock sectors. Using time-varying symmetric and asymmetric copula functions and the conditional Value at Risk measure, we provide evidence of positive tail dependence in most sectors using copula and conditional Value-at-Risk techniques. We can see the average dependence between oil and industries during the oil crisis. Moreover, we find strong evidence of bidirectional risk spillovers for all oil-sector pairs. The intensity of risk spillovers from oil to all stock sectors varies across sectors. The risk spillovers from sectors to oil are substantially larger than those from oil to sectors during COVID-19. Furthermore, the return spillover is time varying and sensitive to external shocks. The spillover strengths are higher during COVID-19 than financial and oil crises. Finally, oil do not exhibit neither hedge nor safe-haven characteristics irrespective of crisis periods.     

Analyzing quantile spillover effects among international financial markets

This paper investigates the quantile-based spillover effects among 17 stock markets from January 1993 to January 2022, utilizing a quantile approach based on the variance decomposition of a quantile vector autoregression (QVAR) model. Compared with the traditional mean-based spillover measures, this new quantile approach allows for a nuanced investigation of spillovers at every quantile and capture spillovers under extreme events. The results show that: (1) the total spillover is high and exhibits strong time-varying characteristics, and the tail spillover is higher and more complex in scale and direction; (2) the spillover at each quantile level shows an upward trend, especially during the 2008 crisis and the COVID-19 epidemic; (3) developed countries (or regions) are the net exporters of stock market spillovers, while the developing countries are the net importers; and (4) the 17 stock markets constitute different local financial networks, which may be related to economic conditions and geographical location.     

Credit risk of subsidiaries of foreign banks in CEE countries: Impacts of the parent bank and home country economic environment

In this study, using dynamic panel data, we investigated the influences of the home country economic environment and parent bank condition on the credit risk of foreign banks in Central and Eastern European (CEE) countries. We concentrated on the international transmission of credit risk through the internal capital market of multinational banks. Our theoretical assumptions follow studies that document how the parent bank condition and home country macroeconomic environment affect lending in subsidiaries in CEE countries. However, our results go one step further. We provide evidence that these relationships are reflected in subsidiaries’ credit risk in CEE countries. Our results suggest that the size and profitability of the parent bank have negative influences, while the liquidity and credit risk of the parent bank have positive influences on the subsidiaries’ credit risk. Moreover, the GDP growth in the parent bank’s country has a negative effect on the credit risk of the subsidiary, while the lending rate and liquidity in the parent bank country cause growth in the credit risk. These results indicate a new channel of international risk transfer from parent bank countries to host countries through foreign-owned banks.     

Dynamic spillovers and linkages between gold,crude oil,S&P 500, and other economic and financial variables. Evidence from the USA

This paper focuses on the price determinants of gold, and on the challenges associated with gold’s safe haven property. Specifically, it analyses the interlinkages and the return spillover effect among gold, crude oil, S&P 500, dollar exchange rate, Consumer Price Index (CPI), economic policy uncertainty and Treasury bills, by employing a Vector Autoregression (VAR) and the spillover index of Diebold and Yilmaz (2012), Diebold and Yılmaz (2014). Monthly realized return series, covering the period from 2nd of January 1986 to 31st of December 2019 are used to examine the short-run linkages, and the return spillovers rolling-window estimates in analyzing the transmission mechanism in a time-varying fashion, respectively. Our findings identify gold as a strong dollar hedge, while crude oil and Treasury bills appear to drive inflation; they also indicate strong spillover effects between exchange rate and gold returns. In general, co-movement dynamics display state-dependent characteristics. Both total and directional spillovers increase significantly during market turbulence caused by severe financial crises such as the Global Financial Crisis (GFC) of 2007–2009 and the European Sovereign Debt Crisis of 2010–2012. Net spillovers switch between positive and negative values for all these markets, implying that the recipient/transmitter position changes drastically with market events. Economic policy uncertainty, stock market returns, and crude oil price returns are the main transmitters, while Treasury bills and CPI are the main return shock recipients. Gold and exchange rate act both as receivers and transmitters over the sample period.     

COVID-19 and asymmetric volatility spillovers across global stock markets

In this study, I improve the assessment of asymmetry in volatility spillovers, and define six asymmetric spillover indexes. Employing Diebold-Yilmaz spillover index, network analysis, and my developed asymmetric spillover index, this study investigates the time-varying volatility spillovers and asymmetry in spillovers across stock markets of the U.S., Japan, Germany, the U.K., France, Italy, Canada, China, India, and Brazil based on high-frequency data from June 1, 2009, to August 28, 2020. I find that the global markets are well connected, and volatility spillovers across global stock markets are time-varying, crisis-sensitive, and asymmetric. Developed markets are the main risk transmitters, and emerging markets are the main risk receivers. Downside risk dominates financial contagion effects, and a great deal of downside risk spilled over from stock markets of risk transmitters into the global markets. Moreover, during the coronavirus recession, the total degree of volatility spillover is staying at an extremely high level, and emerging markets are the main risk receivers in the 2020 stock markets crash.     

The role of the world’s major steel markets in price spillover networks: an analysis based on complex network motifs

In recent years, the international steel market has shown increasingly strong cross-regional correlation. To better understand the price trends of various markets, it is necessary to identify their inherent price spillovers. This paper combines a generalized autoregressive conditional heteroskedasticity Baba–Engle–Kraft–Kroner (GARCH-BEKK) model and complex network motifs to explore the price fluctuations among international steel markets. The study selects steel markets in 12 countries and regions and uses daily data on import and export prices from January 2009 to September 2017 to analyze eight steel products. The results show that spillovers are associated with geographical location, market development, product type and status. Spillovers mostly occur between buyer’s markets; additionally, the Asian market, especially the East Asian market, is in most cases the recipient of spillover, whereas the European Union (EU) market is in most cases the sender of spillover effects. Developed markets have clear spillover effects on emerging markets, sheet steel products have clear spillover effects on profile steel products, and the prices of midstream and downstream products in the industrial chain are the most influenced. This paper examines international steel market relationships from the perspective of price transmission, and the results can help manage and prevent large-scale economic risks.

     

An analysis of dependence between Central and Eastern European stock markets

We examine the dependence structure between four Central and Eastern European (CEE) stock markets (Czech Republic, Hungary, Poland and Romania) using static and dynamic copula functions with different forms of tail dependence. We find evidence of positive dependence between all CEE stock markets, although this dependence is stronger between the Hungarian, Czech and Polish markets than between these markets and the Romanian market. We also find evidence of symmetric tail dependence, although not for the Hungarian and Czech markets. The dependence is time-varying and intensified after the onset of the recent global financial crisis. These results confirm that CEE stock markets are gradually coupling, a fact that has risk management implications for policymakers and investors.     

Multiscale features of extreme risk spillover networks among global stock markets

This paper studies the multiscale features of extreme risk spillover among global stock markets over various time–frequency horizons. We propose multiscale risk spillover indexes based on GARCH-EVT-VaR, maximal overlap discrete wavelet transform method, and forecast-error-variance decompositions. We further construct multiscale risk spillover networks to visualize risk spillovers at different scales. Our findings show that the US and the UK are detected as the centers of risk spillovers, while Asian stock markets are mainly at the edge of the risk spillover network. The topological properties are unevenly spread over each time scale. The network tends to be closer not only at the short-term scale but also during the financial crisis. For individual features, the US and the UK are super-spreaders of risk spillover at each time scale, while most developing markets mainly act as absorbers. The role of European stock markets is complex at different scales.     

Measuring Intersectoral Technology Spillovers: Estimates from the European and US Patent Office Databases

This paper presents several new methods for measuring intersectoral knowledge spillovers, and applies these methods in an analysis of productivity growth in manufacturing for a cross-country, cross-sectional sample for the 1980s. It is argued that existing methods of measuring such intersectoral knowledge spillovers are mostly aimed at measuring so-called crent spillovers'. The methods developed here are aimed at measuring knowledge spillovers—an additional aspect of the spillover process. The empirical analysis shows that there are indeed differences between these two types of spillover measure.     

Connectedness and systemic risk spillovers analysis of Chinese sectors based on tail risk network

This paper investigates the systemic risk spillovers and connectedness in the sectoral tail risk network of Chinese stock market, and explores the transmission mechanism of systemic risk spillovers by block models. Based on conditional value at risk (CoVaR) and single index model (SIM) quantile regression technique, we analyse the tail risk connectedness and find that during market crashes, stock market exposes to more systemic risk and more connectedness. Further, the orthogonal pulse function shows that Herfindahl-Hirschman Index (HHI) of edges has a significant positive effect on systemic risk, but the impact shows a certain lagging feature. Besides, the directional connectedness of sectors shows that systemic risk receivers and transmitters vary across time, and we adopt PageRank index to identify systemically important sector released by utilities and financial sectors. Finally, by block model we find that the tail risk network of Chinese sectors can be divided into four different spillover function blocks. The role of blocks and the spatial spillover transmission path between risk blocks are time-varying. Our results provide useful and positive implications for market participants and policy makers dealing with investment diversification and tracing the paths of risk shock transmission.     

Issues in Assessing the Effect of Interindustry R&D Spillovers

This paper aims to clarify three issues concerning the weighting methodol ogy generally used to evaluate interindustry R&D spillovers. These issues concern the likely nature of the spillovers estimated through different types of supporting matrices; the similarity between input–output (IO), technology flows and technological proximity matrices; and the relevance of the assumption that a single matrix can be used for different countries. Data analyses of weighting components show that technology flows matrices are in an intermediate position between IO matrices and technological proximity matrices, but closer to the former. The various IO matrices, as well as the three technological proximity matrices, are very similar to each other. The panel data estimates of the effect of different types of interindustry R&D spillovers on industrial productivity growth in the G7 countries reject the hypotheses that a technology flows matrix can be approximated by an IO matrix and that a single IO matrix can be usedfor different countries. By transitivity, the procedure that comprises using a single technology flow for several countries is not reliable. The international comparison shows that each country benefits from different types of R&D externality. In Japan and, to a lesser extent, in the US, the rate of return to direct R&D is very high and is likely to compensate for relatively weak interindustry R&D spillover effects. In the five other industrialized countries, the reverse observation is true: strong social rates of return to R&D counterbal ance the poor performances of direct R&D.     

Does sport affect the competitiveness of European Union countries? An analysis of the degree of innovation and GDP per capita using linear and QCA models

The sport industry has experienced great growth in recent years worldwide, and especially in the European Union (EU) countries. However, despite the role that this industry can play in improving the competitiveness of these countries (Gross Domestic Product (GDP) per capita and innovation performance), no studies have been found that analyse its influence. So, the main aim of this study is, firstly, to analyse the relationship between the innovation performance and the GDP per capita of EU countries, and, secondly, to find out how sport-related indicators and different innovation-related indicators influence the innovation performance and GDP per capita of the EU countries. To this end, two different methodologies have been used: hierarchical regression models and qualitative comparative analysis (QCA). A total sample of 23 EU countries have been analysed. The results show that GDP per capita and innovation performance are highly correlated in a positive and significant way (0.76; p?<?.0001), and that the variables related to sport help explain the variance of these two variables, highlighting as necessary variable in both cases the growth in the sports sector (consistency >0.90). Finally, a number of practical implications are presented that can help policy makers to improve the competitiveness of EU countries.

     

Spillover and connectedness among G7 real estate investment trusts: The effects of investor sentiment and global factors

This paper examines the dynamic spillover interconnectedness of G7 Real Estate Investment Trusts (REITs) markets. We use the spillover index of Diebold and Yilmaz (2012), the time-varying parameters vector-autoregression (TVP-VAR) model, and the quantile regression approach. The result show that REITs network connectedness is dynamic and experiences an abrupt increase in the first wave of COVID-19 outbreak (2020Q1). We also observe a substantial abrupt decrease in connectedness during the success of vaccination programs (end 2021). The connectedness among assets is much stronger during COVID-19 than before. The REITs of Japan and Italy are net receivers of spillover and those of US and UK are net transmitters of spillovers before and during COVID-19. Conversely, the REIT of Canada and Germany (France) switches from net receivers (contributors) of spillovers before the pandemic to net contributors (receivers) during the COVID-19. Finally, we show that News Sentiment index, Geopolitical Risk index, Economic Policy Uncertainty index, US Treasury yield, and Stock Volatility index influence the spillover magnitude across quantiles.     

Risk spillovers and hedge strategies between global crude oil markets and stock markets: Do regime switching processes combining long memory and asymmetry matter?

This paper investigates risk spillovers and hedge strategies between global crude oil markets and stock markets. In the paper, we propose a multivariate long memory and asymmetry GARCH framework that integrates state-dependent regime switching in the mean process with multivariate long memory and asymmetry GARCH in the variance process. Our results first show that there are linear risk spillovers running from the US stock markets to the WTI oil market in the short term. However, the linear risk spillover effect running from the oil market to the US stock market can only exist in the long term. In addition, there is a bidirectional linear risk spillover effect between the European stock markets and the Brent oil market in the short and long terms. Furthermore, there is no linear risk spillover effect between the Dubai oil market and the Chinese stock market. Second, the nonlinear risk spillovers running from the WTI oil market to the US stock market can be found in the tranquil regime. Moreover, there is also a nonlinear risk spillover effect running from the European stock markets to the Brent oil market in the tranquil regime. In addition, the nonlinear risk spillover effect running from the Brent oil markets to the European stock market can be found in the crisis regime. Furthermore, there is bidirectional nonlinear Granger causality between the Dubai crude oil market and the Chinese stock market in the tranquil regime. Finally, dynamic hedge effectiveness shows that the regime switching process combined with long memory and asymmetry behavior seems to be a plausible and feasible way to conduct hedge strategies between the global crude oil markets and stock markets.     

COVID-19 related media sentiment and the yield curve of G-7 economies

We explore the connectedness of the components of the sovereign yield curve (slope, level and curvature) across G-7 countries and media sentiment about COVID-19. The recent pandemic is a unique opportunity to identifying the transmitters and receivers of risk. Our results indicate that media sentiment along with the US yield curve components are main transmitter of spillovers, whereas Japan is the leading recipient of spillover. Among the European countries, we notice France as a major transmit, whereas Germany and UK switch role as transmitter and receiver alternatively. The results are important for mapping the interconnectedness between countries. In addition, policy makers can use them when devising disaster plans to prepare for future market crises.     

The impact of fiscal positions on government bond yields in CEE countries

This paper investigates the influence of government debt and primary balance on long-term government bond yields in 10 Central and Eastern European (CEE) countries in the period 2000–2013. The results indicate that a one percentage point increase in the stock of government debt is associated with an increase in government bond yields of 2.7–4 basis points, while a one percentage point increase in the primary deficit to GDP ratio is associated with an increase in government bond yields of 12.9–24.3 basis points. We also find evidence of non-linearities in the debt-interest rate relationship, whereby the threshold after which the impact of debt turns from negative to positive is significantly lower than in advanced economies.     

Productivity Growth,Human Capital and Technology Spillovers: Nonparametric Evidence for EU Regions

This paper assesses the strength of productivity spillovers nonparametrically in a data set of 12 industries and 231 NUTS2 regions in 17 European Union member countries between 1992 and 2006. It devotes particular attention to measuring the catching up through spillovers depending on the technology gap of a unit to the industry leader and the local human capital endowment. We find evidence of a nonlinear relationship between the technology gap to the leader as well as human capital and growth in logs. Spillovers are smallest for units with a medium‐high technology gap to the leader, especially for regions where human capital endowments are low.     

Risk spillover analysis of China’s financial sectors based on a new GARCH copula quantile regression model

This study employs a new GARCH copula quantile regression model to estimate the conditional value at risk for systemic risk spillover analysis. To be specific, thirteen copula quantile regression models are derived to capture the asymmetry and nonlinearity of the tail dependence between financial returns. Using Chinese stock market data over the period from January 2007 to October 2020, this paper investigates the risk spillovers from the banking, securities, and insurance sectors to the entire financial system. The empirical results indicate that (i) three financial sectors contribute significantly to the financial system, and the insurance sector displays the largest risk spillover effects on the financial system, followed by the banking sector and subsequently the securities sector; (ii) the time-varying risk spillovers are much larger during the global financial crisis than during the periods of the banking liquidity crisis, the stock market crash and the COVID-19 pandemic. Our results provide important implications for supervisory authorities and portfolio managers who want to maintain the stability of China’s financial system and optimize investment portfolios.     

FDI溢出对高经济产出区的增长影响

运用空间Durbin模型和极大似然估计技术,以1991-2012年大长三角25城市和大珠三角21城市面板数据,对比检验了FDI溢出与城市生产率间的基本关系。发现：FDI对本地城市增长正效应显著存在并随时间增强,其空间溢出显著但在二地区行为相反;空间依赖在城市生产率增长中有增强趋势,但在二地区方向相异;FDI提高了二地区城市增长的条件收敛速度;空间溢出在二地区的核心和外围都存在但表现各异并有不同演变。上述结论对于二地区引进FDI和促进城市发展具有重要政策含义。