Intensity Of Spillovers Research Articles

Asymmetric multi-scale systemic risk spillovers across international commodity futures markets: The role of infectious disease uncertainty

This paper investigates the role of infectious disease uncertainty on multi-scale risk spillovers and portfolio implications across 12 international commodity futures markets from January 2006 to August 2022. We use wavelet packet decomposition and a novel risk spillover network topology approach based on a smooth transition vector autoregression model. The main findings are summarized as follows. First, there is an obvious asymmetry in spillover effects, i.e., the intensity of risk spillovers increases significantly during periods of high infectious disease uncertainty, and clear evidence of time-varying total spillovers across various regimes and frequencies. Second, cross-category risk spillovers are more pronounced in high-uncertainty regimes, while risk networks tend to cluster within the same category during low-uncertainty regimes. Third, the role of commodity futures in the risk spillover networks varies across different time scales and regimes, with gold consistently acting as a stable net risk transmitter. We also develop optimal portfolio strategies across commodity futures markets at different time scales and regimes based on the risk spillover analysis.

Cryptocurrencies under climate shocks: a dynamic network analysis of extreme risk spillovers

Systematic risks in cryptocurrency markets have recently increased and have been gaining a rising number of connections with economics and financial markets; however, in this area, climate shocks could be a new kind of impact factor. In this paper, a spillover network based on a time-varying parametric-vector autoregressive (TVP-VAR) model is constructed to measure overall cryptocurrency market extreme risks. Based on this, a second spillover network is proposed to assess the intensity of risk spillovers between extreme risks of cryptocurrency markets and uncertainties in climate conditions, economic policy, and global financial markets. The results show that extreme risks in cryptocurrency markets are highly sensitive to climate shocks, whereas uncertainties in the global financial market are the main transmitters. Dynamically, each spillover network is highly sensitive to emergent global extreme events, with a surge in overall risk exposure and risk spillovers between submarkets. Full consideration of overall market connectivity, including climate shocks, will provide a solid foundation for risk management in cryptocurrency markets.

Spillover relationships between international crude oil markets and global energy stock markets under the influence of geopolitical risks: New evidence

The VAR-LASSO connectedness method and VAR-X-LASSO connectedness method are employed in this study to explore the intricate spillover relationships between international crude oil markets and global energy stock markets while also examining the impact of geopolitical risks on these spillover relationships. By comparing the connectedness indices derived from the VAR-X-LASSO connectedness method and the VAR-LASSO connectedness method, this paper yields some intriguing empirical findings. First, the net transmitter of systemic shocks mainly appears in energy stock markets within developed countries. Second, we observed the net spillover direction from energy stock markets in most developed countries, especially those in the United States, Canada, France, Italy, Norway and Spain, to international crude oil markets, and the net spillover direction from international crude oil markets to energy stock markets in most developing countries. Third, geopolitical risks have been observed to strengthen the unidirectional spillover intensity from international crude oil markets to energy stock markets, and their influence intensified after 2015. However, there is minimal influence of geopolitical risks on the unidirectional spillover effects from energy stock markets to international crude oil markets.

Price spillovers and interdependences in China's agricultural commodity futures market: Evidence from the US-China trade dispute

We examine the effects of the US-China trade dispute on price spillovers and interdependencies among the ten most liquid agricultural futures contracts traded on China's commodity exchanges by employing the time-varying connectedness framework modified by Antonakakis, Chatziantoniou, and Gabauer (2020). We find that the overall market frictions increase modestly due to the clustering of mixed informational disturbances caused by the trade dispute. This lowers the gross spillovers in the entire asset system by reducing the efficiency of general price signal transmission. Notably, subsidiary products of rapeseed and soybean contribute the most to the total and net directional spillovers in the whole market, acting as shock transmitters before the trade dispute. The RBD palm market gains a dominant role as a shock transmitter during the trade dispute and its role strengthens further after the trade agreement was reached. Additionally, the average intensity of pairwise spillovers declines considerably during the trade dispute, suggesting weakening asset interconnectedness. The responsiveness of shock transmission channels to the trade dispute impacts is found to vary across industrial chains and the presence of delayed economic effects is detected in the structural changes of market interdependences.

Service provision decisions of competitive manufacturers: From service spillover perspective

This paper investigates competing manufacturers’ strategic choice of service provision in a two-level supply chain composed two manufacturers and one common retailer. Our results indicate that the strategy that two competing manufacturers provide services is always the dominant choice, and two competing manufacturers could have the highest profit in that case. But when only one of them provides services, the result is related to the potential market size, the unit service cost coefficient and the service spillover intensity. Finally, through the cost sharing and quantity competition extensions, we find the Pareto regions of cost sharing under the equilibrium strategy, and there will be multiple Nash equilibrium regions under the quantity competition.

Network structure, dynamic evolution and block characteristics of sovereign debt risk: The global evidence

Global sovereign debt risks are escalating with the tightening of global liquidity. We apply the LASSO-VAR spillover model, network approach and block model to analyze the overall situation and time-varying characteristics of sovereign risk volatility spillover effects among 50 countries. The results show that there is a discernible cyclical pattern in the spillover effect of global sovereign risk volatility, with the spillover intensity progressively rising in each cycle, and more countries have emerged as "net risk exporters" recently. Countries and regions exhibit diverse risk spillover traits, and assume varying roles in the transmission of sovereign debt risks. The originator of a sovereign risk shock isn't necessarily its primary disseminator, underscoring the need to account for systemic significance and risk infectivity. The interlinkages among sovereign debt risk spillover blocks vary over time, given that the composition and attributes of these blocks change. Our findings provide implications for related regulators and investors.

Climate policy uncertainty and the Chinese sectoral stock market: A multilayer network analysis

Climate change is the most important challenge currently faced by humans, which affects their health and financial stability. This study proposes a multilayer network that includes return, volatility, and risk layers to investigate the risk connectedness between climate policy uncertainty and the sectoral stock market in China. We explore the static and dynamic topological features of multilayer networks from the perspectives of the system and market levels, respectively. The results indicate that (i) a crisis increases the intensity of risk spillovers; (ii) an obvious nonsynchronous effect was found among the layers during financial turmoil; and (iii) industrials, materials, information technology, and consumer discretionary act more often as net risk transmitters during the sample period, whereas climate policy uncertainty, telecommunication services, and financials are more frequently net risk receivers. The results of this study have important implications for regulators, policymakers, and investors in alleviating climate risk and developing hedging and investment strategies.

Risk spillover mechanism among commercial banks and FinTech institutions throughout public health emergencies

While FinTech has contributed to the improvement of efficiency and cost reduction for commercial banks, it has also brought about risks for them. Especially during the outbreak of major public emergencies, the degree of risk spillover among financial institutions increased significantly. In this paper, we constructed a high-dimensional risk spillover network using the elastic net shrinkage technique to investigate the impact of public health emergencies on the risk spillovers between FinTech institutions and commercial banks. The total spillover index was utilized to access the overall coupling between FinTech institutions and commercial banks. Furthermore, we employed the sectoral spillover index and institutional centrality index to examine the spillover intensity across different sectors and institutions. Additionally, we analyzed the changes in the risk spillover network structure and institutional risk role during emergencies, aiming to uncover the impact mechanism of public health emergencies on risk spillovers. The results reveal that (1) public health emergencies, such as the COVID-19 pandemic, have intensified the industry correlation between the FinTech and banking sectors, and the primary risks of the system have shifted from intraindustry risks to interindustry risks. (2) Public health emergencies have changed the risk transmission roles of FinTechs and banks. FinTechs transitioned from being risk recipients to risk contributors, while banks shifted from being risk contributors to risk recipients. (3) FinTechs play a crucial role in facilitating indirect risk transmission within the system, acting as influential adjacent institutions that bridge the gap between critical institutions. (4) Compared with large commercial banks, small and medium-sized banks are more sensitive to FinTech risks. This study provides supporting evidence for regulatory agencies to enhance risk management in financial innovation during public health emergencies and in the post-pandemic era.

Can NFTs hedge the risk of traditional assets after the COVID-19 pandemic?

Our study analyzes the dynamic spillover effects between the NFT market, Bitcoin market, oil market, gold futures market, S&P 500 stock index, bond market, and US dollar index over three time periods: the full sample period (2017/6/23–2022/9/2), before the COVID-19 outbreak (2017/6/23–2020/1/12), and after the COVID-19 outbreak (2020/1/13–2022/9/2) by employing the DCC-GARCH-based connectedness model. Furthermore, we apply the DCC-GARCH-t-Copula model, Risk Parity Portfolio (RPP) model, and Minimum Connectedness Portfolio (MCoP) model to evaluate the bilateral dynamic hedge ratios, portfolio weights, and the multivariate portfolio performance of these financial assets, respectively. Our findings suggest that NFTs are a source of volatility spillovers during both periods and that the COVID-19 outbreak accelerates the speed and intensity of volatility spillovers from NFTs to traditional financial markets. Moreover, most of the volatility spillovers from NFTs are caused by own shocks, which implies that NFTs are not susceptible to financial risk contagion. The results of bilateral portfolio analysis show that NFTs can effectively hedge other traditional financial assets when used as long positions. The results of multivariate portfolio analysis demonstrate that NFTs can also reduce investment risk despite their smaller weighting. In the two months after the COVID-19 outbreak, MCoP slightly outperforms RPP, although RPP outperforms MCoP in general.

Bidirectional Risk Spillovers between Chinese and Asian Stock Markets: A Dynamic Copula-EVT-CoVaR Approach

This study aims to investigate bidirectional risk spillovers between the Chinese and other Asian stock markets. To achieve this, we construct a dynamic Copula-EVT-CoVaR model based on 11 Asian stock indexes from 1 January 2007 to 31 December 2021. The findings show that, firstly, synchronicity exists between the Chinese stock market and other Asian stock markets, creating conditions for risk contagion. Secondly, the Chinese stock market exhibits a strong risk spillover to other Asian stock markets with time-varying and heterogeneous characteristics. Additionally, the risk spillover displays an asymmetry, indicating that the intensity of risk spillover from other Asian stock markets to the Chinese is weaker than that from the Chinese to other Asian stock markets. Finally, the Chinese stock market generated significant extreme risk spillovers to other Asian stock markets during the 2007–2009 global financial crisis, the European debt crisis, the 2015–2016 Chinese stock market crash, and the China–US trade war. However, during the COVID-19 pandemic, the risk spillover intensity of the Chinese stock market was weaker, and it acted as the recipient of risk from other Asian stock markets. The originality of this study is reflected in proposing a novel dynamic copula-EVT-CoVaR model and incorporating multiple crises into an analytical framework to examine bidirectional risk spillover effects. These findings can help Asian countries (regions) adopt effective supervision to deal with cross-border risk spillovers and assist Asian stock market investors in optimizing portfolio strategies.

A pathway to coordinated regional development: Energy utilization efficiency and green development - Evidence from China's major national strategic zones

According to the new economic geography hypothesis, this study establishes an analytical framework to assess the efficiency of energy use and the progress of green development. The framework is based on panel data collected from 254 cities at the prefecture level in China. The spatial Durbin model is employed to examine the influence and spatial spillover effect of energy utilization efficiency on green development in major national strategic zones. The results show that: improving energy utilization efficiency promotes green development in major national strategic regions, with a stronger intra-regional transfer effect than inter-regional. This is confirmed by robustness and endogeneity analyses. The spillover intensity of energy utilization efficiency on green development varies across regions. It shows an inverted U-shape curve in the Yangtze River Economic Belt, a U-shape curve in the Yellow River Basin and Yangtze River Delta region, a diminishing margin in the Beijing-Tianjin-Hebei region, and a growing margin in the Guangdong-Hong Kong-Macao Greater Bay Area. There is significant spatial heterogeneity within these regions. Different mediating mechanisms affect green development in different regions. The knowledge spillover effect mediates energy utilization efficiency's impact on green development in the Guangdong-Hong Kong-Macao Greater Bay Area, the industry linkage effect in the Yangtze River Economic Belt and Beijing-Tianjin-Hebei region, the environmental improvement effect in the Yellow River Basin, and the market linkage effect in the Yangtze River Delta region. To promote regional coordinated development with energy utilization efficiency, the country should not only consider differences between national strategic areas but also focus on the effects of different paths to realize the complementarity between energy utilization efficiency and green development.

Frequency spillovers between oil shocks and stock markets of top oil-producing and -consuming economies

Motivated by large oil price swings, high economic and geopolitical uncertainties, and the financialization of oil, this paper examines the frequency spillovers and co-movements between oil shocks (risk and demand) and the stock markets of top oil-producer and consumer countries, namely, Canada, China, Russia, Saudi Arabia, and the US. The analysis uses the time-domain spillover index of [1], the frequency-domain spillover of [2]; and the wavelet coherence approach. The findings reveal that spillovers run from the U.S., Canada, and, to a lesser extent, Russia to oil shocks. On the other hand, oil shocks, Saudi Arabia, and China constitute net receivers of shocks. The intensity of spillovers is heavier in the short-term frequency than in the intermediate- and long-term. Furthermore, the direction of spillovers is more defined in the long-term. The U.S. stock market exerts a strong impact on oil risk in general, but the impact is stronger in the short-term. Conversely, an oil demand shock is susceptible to innovations from Canada and Russia that are stronger in the long-term. This means that oil risk shock stemming from innovations in financial markets is short-lived and dissipates quickly due to quick reactions from market participants. On the contrary, long-term links characterize the relationship between oil demand shock and financial markets, mirroring the macroeconomic nature of the linkages. Finally, while the 2008 crisis, EDC, oil price crash, and the COVID-19 pandemic coincided with strong spillovers in the short-term, the COVID-19 era was marked by higher spillovers in the long term. The findings provide important information for investors and policymakers in terms of diversification, risk management, and efforts to mitigate contagion.

Потенциал создания и внедрения технологий искусственного интеллекта в регионах России

Geography of innovation allows us to understand the spatial patterns for creation, diffusion, and support of new technologies, although with the development of communications there is a delusion of insignificance of space in these processes. The development of one of the breakthrough technologies – artificial intelligence (AI) – cannot be widespread but must be concentrated in centers with high innovation potential, where the intensity of knowledge spillovers is high. In Russia, education in the field of AI can be obtained in 21 regions, research is conducted in 35, and technology is being developed in 40. We proposed a rating of the regional potential to create AI technologies based on scientific and technological development and the main elements of the regional innovation ecosystem in the field of AI. It shows a high concentration of potential in Moscow and several creative core regions: Moscow region, St. Petersburg, Tatarstan and Novosibirsk region. 16 creative-acceptor centers have also been identified, capable of both creating and implementing AI technologies, mainly acceptor centers (23 regions) and 40 regions with minimal potential. Leading regions can receive priority attention and funding in Russia. In acceptor regions, advantage may be given to AI production technologies, and in lagging regions, increasing the population’s receptivity to digital technologies in general.

Diluting the Resistance of Built-in Electric Fields in Oxygen Vacancy-enriched Ru/NiMoO4-x for Enhanced Hydrogen Spillover in Alkaline Seawater Splitting.

Recently, hydrogen spillover based binary (HSBB) catalysts have received widespread attention due to the sufficiently utilized reaction sites. However, the specific regulation mechanism of spillover intensity is still unclear. Herein, we have fabricated oxygen vacancies enriched Ru/NiMoO4-x to investigate the internal relationship between electron supply and mechanism of hydrogen spillover enhancement. The DFT calculations cooperate with in situ Raman spectrum to uncover that the H* spillover from NiMoO4-x to Ru. Meanwhile, oxygen vacancies weakened the electron supply from Ru to NiMoO4-x , which contributes to dilute the resistance of built-in electric field (BEF) for hydrogen spillover. In addition, the higher ion concentration in electrolyte will promote the H* adsorption step obviously, which is demonstrated by in situ EIS tests. As a result, the Ru/NiMoO4-x exhibits a low overpotential of 206 mV at 3.0 A cm-2 , a small Tafel slope of 28.8 mV dec-1 , and an excellent durability of 550 h at the current density of 0.5 A cm-2 for HER in 1.0 M KOH seawater.

Diluting the Resistance of Built‐in Electric Fields in Oxygen Vacancy‐enriched Ru/NiMoO4‐x for Enhanced Hydrogen Spillover in Alkaline Seawater Splitting

AbstractRecently, hydrogen spillover based binary (HSBB) catalysts have received widespread attention due to the sufficiently utilized reaction sites. However, the specific regulation mechanism of spillover intensity is still unclear. Herein, we have fabricated oxygen vacancies enriched Ru/NiMoO4‐x to investigate the internal relationship between electron supply and mechanism of hydrogen spillover enhancement. The DFT calculations cooperate with in situ Raman spectrum to uncover that the H* spillover from NiMoO4‐x to Ru. Meanwhile, oxygen vacancies weakened the electron supply from Ru to NiMoO4‐x, which contributes to dilute the resistance of built‐in electric field (BEF) for hydrogen spillover. In addition, the higher ion concentration in electrolyte will promote the H* adsorption step obviously, which is demonstrated by in situ EIS tests. As a result, the Ru/NiMoO4‐x exhibits a low overpotential of 206 mV at 3.0 A cm−2, a small Tafel slope of 28.8 mV dec−1, and an excellent durability of 550 h at the current density of 0.5 A cm−2 for HER in 1.0 M KOH seawater.

Dynamic impact of the COVID-19 lockdown intervention policies on network structure of energy futures return connectedness

This work investigates the dynamic impact of COVID-19 lockdown policies, implemented to curb the pandemic's spread, on the structure of the energy futures return connectedness network (EFRCN). Firstly, we measured the connectedness of 20 energy futures return series using the time-varying parameter vector autoregression (TVP-VAR) frequency connectedness approach and constructed a directed multi-layer dynamic EFRCN. Secondly, we developed a novel statistical indicator, the ratio of average vertex out-strength to average vertex in-strength, to represent the spillover intensity of a specific network layer. Finally, we utilized the pruned exact linear time (PELT) algorithm to pinpoint structural changepoints of COVID-19 lockdown policies and explored the change in the EFRCN structure before and after these changepoints. The empirical findings demonstrate that the scales of population and gross domestic product (GDP) impacted by the COVID-19 lockdown positively drive the average vertex strength, density, and clustering coefficient of the EFRCN, as well as the return spillover intensity of oil and power futures markets, respectively. Conversely, the regression results exhibit noteworthy negative causal relationships between the scales of population and GDP impacted by the COVID-19 lockdown and the return spillover intensity of coal and natural gas futures markets for non-periodic and short-term networks. Furthermore, the empirical results illustrate distinct structural changes in the evolution of global indicators of the non-periodic and short-term networks at the changepoints of COVID-19 lockdown policies before June 2020. To sum up, COVID-19 lockdown intensification increases the clustering of the EFRCN and significantly enhances the net spillover effects of oil and power futures markets within the EFRCN. Moreover, the changes in COVID-19 lockdown policies significantly influence the EFRCN's structure.

Connectedness analysis of oil price shocks, inflation, and exchange rate for the MENA region countries

The relation among energy prices, exchange rates and inflation has been perturbed in the recent past by both the COVID-19 shock, which resulted in a sharp downturn of the economic activity at the beginning of 2020 and the Ukraine crisis, which resulted in sharp increase in energy prices and then inflation at the beginning of 2022. This is the first paper to analyze this relation for the MENA region, conducting an empirical estimation of a VARX model on monthly data from Jan 2010 to Dec 2022 for the main 11 MENA countries. The model includes asymmetric effects, a detailed contagion analysis, conducted with the methodology à la Pesaran - Pick (2007) to identify periods of high volatility, and presents a spillover analysis à la Diebold - Yılmaz (2014), used to measure the connectedness of the three main variables.The novelties of the paper are the construction of a country specific oil portfolio; the asymmetric analysis of oil shocks and contagion; the measurement of the general impulse response spillover, to ascertain the intensity and direction of volatility spillovers. The empirical results show that: (i) the specific oil prices considered for each country induce different effects; (ii) the oil price changes effect depends on the direction and period of the shock; (iii) there are asymmetric contagion effects for exchange rate and inflation only for some countries. Finally, some policy implications are presented based on the spillover connectedness analysis, especially considering the post Covid period.

Quantitative easing and the spillover effects from the crude oil market to other financial markets: Evidence from QE1 to QE3

The relationships between the oil market and other financial markets remain poorly understood. In this paper, we first construct a set of spillover indices that measure the return spillovers from the oil market to other financial markets in the short, medium, and long terms, and then we examine the drivers of spillover intensity by focusing on the effect of quantitative easing in the U.S. The main empirical results are as follows. First, the return spillovers from the oil market to other markets are driven by various frequencies (short-term to long-term), and intensified during the global financial crisis of 2007–2009. Second, quantitative easing has different effects on the spillover intensity at different frequencies, with the effect on short-term spillovers being less significant. Third, our research provides the first empirical evidence for a double-edged sword effect of quantitative easing on the systemic risk from the frequency perspective.

Dynamic volatility spillover among cryptocurrencies and energy markets: An empirical analysis based on a multilevel complex network

Based on the DCC-GARCH-CONNECTEDNESS approach, this paper investigates the dynamic volatility spillover among ten cryptocurrency markets and three energy markets. This study constructed a volatility spillover network weighted by the spillover intensity index. The overall network topological properties, node characteristics, and spillover structure were then analyzed by applying the index quantification and hierarchical clustering algorithm. This research examined the multi-level risk spillover structure while realizing network visualization and also judged the dynamic correlation among the spillover network structures. The findings show that the network topology properties are relatively stable, but there is a large heterogeneity among markets. Compared to the energy market, the cryptocurrency market exhibits more profound features of volatility resonance between different currencies. Traditional cryptocurrency markets display higher risk resonance levels than stable coins. In terms of risk spillover structure, different markets tend to rely on market attributes to demonstrate their spillover and agglomeration characteristics. In addition, the risk spillover resonance among the sub-networks is also dynamic and time-varying, and this network structure will continue over time.

Dynamic Connectedness among Forex Markets of Pakistan and Its Major Trading Partners

Globalization and financial liberalization have made market participants and policymakers ponder over the importance of financial interconnectedness and shock spillovers. This discussion is in line with the International Portfolio Theory & Diversification. In this context, this study investigates the intensity and direction of return and volatility spillovers of foreign exchange (FX) markets of Pakistan and its major trading partners. Using an innovative research technique by Diebold and Yilmaz (2009, 2012), we separately compute measures of return and volatility spillovers. Furthermore, we also calculate total spillover, directional spillover, and net spillover indices using a daily data set over the period 1995 to 2019. To capture secular and cyclical movements in the trading partners’ FX markets, we rely upon a rolling window analysis. Our results, based on the spillovers indices, support the presence of dynamic connectedness among currency pairs of the major trading partners. We also note that among the sample economies, the USA, the EU, Singapore, and Malaysia are the main sources and originators of shocks spillover while Pakistan, India, Japan, Kuwait, Singapore, and UAE are net shock receivers. The rolling window analysis indicates that returns and volatility spillovers intensify during the phases of financial or economic anxiety. These results have some important implications for individuals working on risk management, portfolio diversification, and trading strategies.