Spillover Patterns Research Articles

Spillover dynamics among commodities along the Chinese oil industrial chain: From the perspective of multidimensional networks

This paper investigates the dynamic spillover effects of major commodity prices within the Chinese oil industrial chain. By integrating the Granger causality test with the BEKK-GARCH model, a multidimensional spillover network is constructed to comprehensively analyze the price spillover relationships between commodities from 2013 to 2019. The study finds that crude oil price volatility is the primary driver of spillovers to other commodities within the industrial chain, with midstream commodities such as ethylene and benzene playing a critical intermediary role in the spillover process. Additionally, the spillover between commodities is generally bidirectional, with non-closed chain spillover patterns being more common. Based on these findings, the paper offers policy recommendations to mitigate the risks of price fluctuations, promoting the stable development of the oil market and the sustained growth of the national economy.

Asymmetric spillovers and resilience in physical and financial assets amid climate policy uncertainties: Evidence from China

Climate policy is essential for fostering sustainable development and addressing climate-related issues, while climate policy uncertainty (CPU) introduces complex challenges in providing a consistent framework for action. In this study, we delve into the spillover dynamics among China's domestic CPU, foreign CPU, physical assets, and financial assets across various market scenarios, through a quantile-based connectedness method and evidence from China. The findings reveal: (1) The spillover dynamics are asymmetric across quantiles and time, and the COVID-19 pandemic influences the total connectedness. (2) The complexity of spillover patterns in physical assets is likely contingent upon market conditions, policy sources, asset types, and industrial-chain positions, while the spillovers of financial assets are quantile-dependent and time-varying, with the agricultural equity market showing relative resilience to the pandemic. (3) There is no significant Granger causality in quantiles between domestic CPU and foreign CPU under the entire sample period, and the rankings regarding the spillovers from CPUs to assets vary across market conditions. Implications for policymakers, investors, and firms are provided.

Navigating APEC Countries: TVP-VAR Insights into Developed and Emerging Stock Markets

The interdependence of stock markets provides important discernment into the behavior of the larger international financial markets. This study investigates magnitude and directional volatility spillover patterns among developed and emerging countries within the APEC bloc, utilizing the TVP-VAR model. The findings indicate that Russia (15.06%), Vietnam11.64%), and Thailand (11.57%) are identified as major transmitters, and Malaysia (-28.95%), Philippines (-9.28%), China (-9.53%) are major receptor of the volatility spillovers in APEC emerging countries. In APEC-developed countries, the United States (56.85%) and Canada (42.6%) are major transmitters, and Japan (-34.02%) and Australia (-53.54%) are identified as a major receptor of the spillover. Moreover, COVID-19 was the most significant crisis, with the highest volatility spillover identified in the APEC bloc's developed and emerging economies. The discoveries have substantial ramifications, offering valuable insights into optimal investment strategies by identifying patterns, magnitudes, and directions of economic volatility shocks.

Analyzing dynamic patterns of information flow between bitcoin and economic uncertainty in light of public sentiments: A statistical behavior approach

Modeling and analyzing interrelationships within the Bitcoin market, as a prominent cryptocurrency, leads to understanding hidden structures, effective management and informed decision-making. Regarding this matter, numerous studies have analyzed the time-varying spillover patterns in this ecosystem. Although spillover network analysis can elucidate the nature and strength of correlations, it may not be adept at handling the conditional interdependencies within intricate non-linear and dynamic essential behaviors of financial time series. This research tries to address the mentioned challenges by presenting a novel analytical model to investigate the dynamic communication patterns among Bitcoin, United States Economic Policy Uncertainty (US EPU) and public sentiments. Following this objective, rather than directly exploring the effect of original data series on each other, the approach decomposes them into sequences of meaningful statistical behaviors, at different lag-lead horizons. Subsequently, considering the significance of conditional dependencies, we extract and analyze the rules and patterns of information flow among the observed behaviors. The findings not only unveil a distinct flow pattern compared to the spillover network, but also offer valuable insights into dynamic interactions and dominant behaviors under various scenarios. One observation suggests that as the historical range of predictors increases in predicting future changes, their effectiveness or reliability decreases, while their number simultaneously increases. Moreover, the trend slope of Bitcoin functions as a notable behavior in propagating information, directly influencing both economic uncertainty and investor sentiment. The proposed model enhances the understanding of interaction between financial time series and provides useful perspectives for analysis and risk management.

Safe haven properties of industrial stocks against ESG in the United States: Portfolio implication for sustainable investments

This study explores the safe haven role of industry sectors classified by the Global Industry Classification Standards (GICS) for global Environmental, Social, and Governance (ESG) stocks in the United States. Our study applies the novel cross-quantilogram and quantile time-frequency connectedness approach for daily time series data from November 2019 to October 2023. Our findings confirm that industry sectors, mainly financials, industrial, communication, information technology, energy, consumer staples, and consumer discretionary, are the safe haven avenues at down, normal, and up market conditions; however, these safe haven avenues vanish for the long-term investment horizon. We also explore the risk spillover patterns for the total time domain and the decomposed spillover level for the short- and long-term spillovers. Our connectedness finding shows that the net transmitters and net receiver patterns are similar for the extreme markets but dramatically change for the normal markets. Finally, based on the portfolio rebalancing strategy, the returns of our portfolio, comprised of industry sectors and the ESG index, outperform the overall market. Moreover, our portfolio yields lower negative returns and a higher Sharpe ratio when compared with the overall market returns. These findings confirm the benefits of diversification by reducing downside portfolio risk. This study is primarily relevant for socially responsible portfolio and risk management investors.

Asymmetric Return Connectedness Among Indian Equity Sectors: Insights from Recent Global Disruptions

This article aims to study asymmetric shock transmission among India’s National Stock Exchange sectoral indexes before and amid the COVID-19 crisis and the Russian–Ukraine feud using an asymmetric time-varying parameter vector autoregressive connectedness technique. The daily closing price of equity sector indexes from 1 January 2018 to 31 March 2023 were used. The research reveals strong sectoral return interconnectedness, with negative returns spillover dominating the assessment period. COVID-19 substantially impacted connectedness more than the Russian–Ukraine feud. The sectors such as bank, private bank and financial services were net transmitters of risk, and information technology was the net recipient of risk throughout the assessment period, indicating these sectors are not much affected by these crises. Sector return spillover patterns were similar during the epidemic and the Russian–Ukraine dispute, except for the oil and gas and metal sectors, which shifted roles. These findings suggest that policymakers must recognize and assess the peculiarities of industries that are high-risk transmitters when formulating strategies to prevent market failure or mitigate risk transmission to others. Investors may benefit from recognizing industries that operate as risk transmitters and receivers to create effective risk-mitigation investment strategies amid turbulent times.

Risk spillover effects of the Israel–Hamas War on global financial and commodity markets: A time–frequency and network analysis

This paper uses TVP-VAR dynamic spillover method and spectral clustering algorithms to explore the impact of the Israel–Hamas war on the risk contagion in global financial and commodity markets. The war markedly intensified the overall risk spillover, with short-term impacts surpassing long-term effects. The static risk spillover and dynamic spillovers characteristics exhibited marked changes, but the directional risk spillovers in the time domain and the net risk level for each asset reacted differently. There was a significant shift in the global market’s community divisions and net spillover patterns networks, which changed the way risk spreads between the global financial market.

Time-frequency extreme risk spillovers between COVID-19 news-based panic sentiment and stock market volatility in the multi-layer network: Evidence from the RCEP countries

This study extends extant discussions on regional integration by exploring risk spillovers between news-based panic sentiment and stock market volatility among Regional Comprehensive Economic Partnership (RCEP) members during the COVID-19 pandemic. We innovatively estimate quantile-based spillover indices in both time and frequency domains and construct the multi-layer network. Both static and dynamic risk spillover patterns in the "RCEP panic-stock network" are outlined. It is found that risk spillovers are predominantly transmitted from the "panic-layer network" towards the "volatility-layer network". Besides, regression analyses are employed to investigate whether general features of COVID-19 media reporting affect the spillovers of panic sentiment at the country level. The results show that the magnitude of panic spillovers tend to be positively associated with overall media coverage and negatively with overall media sentiment, especially at the middle and lower quantiles.

Volatility spillovers and carbon price in the Nordic wholesale electricity markets

This paper investigates price volatility and spillovers in the Nordic electricity wholesale markets. We use the Time-Varying Parameter Vector Autoregressive (TVP-VAR), Rolling Window-based VAR (RW-VAR), and high dimensional VAR with common factors (VAR-CF) methods and analyze the integration dynamics among these markets and impact of carbon prices on volatility spillovers. We use 107,352 hourly price data from January 2010 to March 2022. The novelty of this research is four-fold. First, we adopt a connectedness approach to explore volatility interactions among the four Nordic markets, contributing to the scarce literature on volatility in this market. Second, we segment the Norwegian market into southern and northern regions, revealing differences in volatility spillover patterns. Third, we investigate the effect of carbon prices on volatility spillovers and market dynamics. Last, we show significant contribution of covariances to interdependence among markets. We find significant connectedness between the Nordic markets, with an average Total Connectedness Index of between 50% (with a system of variance) and 90% (with a system of both variance and covariance). Sweden is the sole net volatility spillover transmitter, while Denmark experiences the largest shocks from the system. We further find that carbon prices exert a 5% significant impact on the volatility spillover index.

Stock market reactions under the shadow of the COVID-19 pandemic: Evidence from China

This paper studies how the Chinese stock market reacts to new COVID-19 infections under the zero-COVID policy. Consistent with the literature, we find that a COVID-19 outbreak within a city adversely affects the performance of local firms, but further unveils the effect’s nonlinearity. More importantly, we document an unexpected pattern of spatial spillover of the COVID-19 shock, which is likely to be caused by the policy itself. In addition, firms with significant retail exposure are most vulnerable, while firms with low pandemic exposure, larger size, state-owned enterprise (SOE) status, and robust finances demonstrate greater resilience to the COVID-19 shock. Mechanism analysis indicates that the COVID-19 effects are realized through both cash flow and discount rate channels. Supplementary back-of-the-envelope calculations illustrate the substantial economic consequences of these phenomena, which shed light on the debate on the optimal policy in response to a future pandemic.

Research on dynamic spillover effect of stock market volatility in Beijing-Tianjin-Hebei region

To enhance economic cooperation in the Bohai Sea and Beijing-Tianjin-Hebei region, China introduced the Beijing-Tianjin-Hebei integration plan in 2014. Over the past decade, coordinated development has established a solid foundation, emphasizing economic and spatial structure optimization. The region’s development has now achieved comprehensive, qualitative, and profound progress. Recognizing economic integration as crucial, this study analyzes the 2020-2023 stock data of listed firms in the region, utilizing the DY spillover index to examine sectoral volatility spillover effects, both statically and dynamically. Results reveal bidirectional, asymmetric spillovers, with heterogeneous spillover levels. Industry, raw materials, and information technology are key spillover sectors, significantly influencing others and inducing synchronization. Property and finance sectors are primarily recipients, influenced by others. Energy and communication services exhibit strong bidirectional spillovers. Volatility spillover strength is influenced by factors such as inter-sector correlation, market risk, macroeconomic changes. Studying these factors helps grasp volatility spillover patterns and understand sectoral volatility-factor relationships.

Quantile connectedness between the climate policy and economic uncertainty: Evidence from the G7 countries

In this study, we investigate the transmission mechanism between climate policy uncertainty (CPU) and economic policy uncertainty (EPU) in the G7 countries. To account for different conditions, we use a quantile-based VAR (Q-VAR) model over the period between 2000 and 2021. Our results show high connectedness between the CPU and the EPU of G7 countries, particularly at extreme quantile orders. On the other hand, the spillover effects between climate and economic policy uncertainty differ depending on the distributional levels of the uncertainty indices. The CPU is a net receiver of uncertainty shocks, while for almost all countries, the EPU acts as a net receiver or emitter, depending on the economic situation. During times of high or low economic uncertainty, the EPU of all G7 countries is strongly affected by shocks originating from the CPU. Moreover, the results indicate that the dynamic spillover patterns between EPU and CPU vary over time, responding to different economic events and financial crises. These results call for policymakers and governments to urgently integrate climate considerations into economic planning, fiscal policies, and regulatory frameworks to promote sustainable economic growth and mitigate the impacts of climate change.

Stock Investment Strategies with Time-Varying Co-Movements Between Energy Market and Global Financial Market

One of central challenges in guiding investment mangement is how to intelligently identify investment strategies. This paper sets up a deeping learning model for realizing stock investment strategies with time-varying co-movements between energy market and global financial market: It uses LSTM neural network approach to predict energy stock price, and employs the improved time domain connectedness measures of Diebold and Yilmaz to test the spillover mechanism of market volatility shocks. By the propsed model, this paper explores the time pattern of volatility spillover between energy market and stock price in major global financial markets (mainly including stock market) from January 3, 2000 to December 31, 2020.

Over-expected shocks and financial market security: Evidence from China's markets

Based on the quantile connectedness method, we investigate China's financial market's structural characteristics under varying shock scales. Our results reveal an asymmetric U-shaped pattern of risk spillovers within China’s financial market under different shock magnitudes. The measures of relative intensity and relative tail dependence indicators show that the risk spillover is much larger in the right tail than in the intermediate and conditional mean states. Moreover, different financial sub-markets exhibit time-varying heterogeneity when facing over-expected shocks. The elasticity of risk-shock is greater in the money and foreign exchange markets. The commodity market tends to take on risk, but becomes a significant source of risk spillover during over-expected shocks. These findings offer policymakers valuable insights to comprehensively evaluate risks and effectively formulate policies.

Balancing urban expansion with a focus on ecological security: A case study of Zhaotong City, China

The conflict between socio-economic development and ecological environmental protection presents a significant challenge to achieving regional and urban sustainable development. In Zhaotong City, Yunnan Province, China, this conflict arises from the interplay between urban expansion and ecological environmental protection land, creating a fundamental tension between built-up and non-built-up ecosystems. This study focuses on investigating the spatial characteristics of expansion, constructing an ecological security pattern based on ecosystem service functions and sensitivity, and utilizing integrated models to simulate urban expansion paths and analyze their formation mechanisms. The results highlight that Zhaotong City has undergone rapid urban expansion, characterized by linear growth surrounding the central city, specifically in the east, north, and west directions. The expansion between urban areas exhibits a discontinuous dotted-star distribution, with concentrated urbanization and a distinct urban boundary observed in Zhaoyang District. In contrast, other areas experience scattered expansion, indicative of random land encroachment. Through simulation, three primary urban expansion axes are identified: the western town sprawl belt, central town development axis, and resource-rich town corridor. Ecological conflicts primarily arise in Zhaoyang District, Zhenxiong County, and Yanjin County. By combining these findings, the study analyzes the scenario pattern of urban expansion evolution in Zhaotong City, revealing a transition from the spreading spillover expansion of a single center (Zhaoyang and Ludian) during the circle expansion phase to a polycentric expansion and spreading spillover pattern centered around Zhaoyang District along three primary axial radial paths. In conclusion, this study contributes to our understanding of the challenges posed by the conflict between socio-economic development and ecological environmental protection in Zhaotong City. The findings underscore the importance of integrating ecological considerations into urban planning processes and highlight the need for sustainable development practices to achieve a harmonious coexistence between urban expansion and ecological security. The insights gained from this research offer guidance for fostering sustainable urban development in Zhaotong City and beyond.

Host traits shape virome composition and virus transmission in wild small mammals

Bats, rodents, and shrews are the most important animal sources of human infectious diseases. However, the evolution and transmission of viruses among them remain largely unexplored. Through the meta-transcriptomic sequencing of internal organ and fecal samples from 2,443 wild bats, rodents, and shrews sampled from four Chinese habitats, we identified 669 viruses, including 534 novel viruses, thereby greatly expanding the mammalian virome. Our analysis revealed high levels of phylogenetic diversity, identified cross-species virus transmission events, elucidated virus origins, and identified cases of invertebrate viruses in mammalian hosts. Host order and sample size were the most important factors impacting virome composition and patterns of virus spillover. Shrews harbored a high richness of viruses, including many invertebrate-associated viruses with multi-organ distributions, whereas rodents carried viruses with a greater capacity for host jumping. These data highlight the remarkable diversity of mammalian viruses in local habitats and their ability to emerge in new hosts.

An investigation of the frequency dynamics of spillovers and connectedness among GCC sectoral indices

We examine intra-regional patterns of return and volatility spillovers between economic sectors of GCC over the period from 2007 to 2021 at different frequencies. First, we investigate the connectedness of sectoral equity returns and volatilities by applying TVP-VAR frequency connectedness method and explore the different patterns and magnitudes. Second, we explore possible determinants of sectoral equity return and volatility spillovers. We identify that spillovers are regime dependent increasing their intensity during turmoil periods such as 2007–2008 crisis, 2014 oil price crash and 2020 COVID-19 pandemic. The level of contagion is the highest in the financial sector and the lowest in the energy sector. In general, while Bahrain stock market is segmented from other markets in many sectors, Saudi Arabia is losing its dominance position to UAE and Qatar to transmit shocks to other countries. In line with the literature, the liquidity and profitability positions significantly affect the extent of the spillovers which are highly dispersed across sectors. Particularly, the sectors that have high leverage tend to transmit the shock rather than absorb. Our findings confirm the heterogeneity of sectoral spillover returns and volatilities, thereby suggesting that portfolio managers can monitor the magnitude of the spillovers by controlling the financial performance of the firms and guide their investment decisions accordingly at different time horizon.

Spotting the stock and crypto markets’ rings of fire: measuring change proximities among spillover dependencies within inter and intra-market asset classes

Crypto assets have lately become the chief interest of investors around the world. The excitement around, along with the promise of the nascent technology led to enormous speculation by impulsive investors. Despite a shaky understanding of the backbone technology, the price mechanism, and the business model, investors’ risk appetites pushed crypto market values to record highs. In addition, pricings are largely based on the perception of the market, making crypto assets naturally embedded with extreme volatility. Perhaps unsurprisingly, the new asset class has become an integral part of the investor’s portfolio, which traditionally consists of stock, commodities, forex, or any type of derivative. Therefore, it is critical to unearth possible connections between crypto currencies and traditional asset classes, scrutinizing correlational upheavals. Numerous research studies have focused on connectedness issues among the stock market, commodities, or other traditional asset classes. Scant attention has been paid, however, to similar issues when cryptos join the mix. We fill this void by studying the connectedness of the two biggest crypto assets to the stock market, both in terms of returns and volatility, through the Diebold Francis spillover model. In addition, through a novel bidirectional algorithm that is gaining currency in statistical inference, we locate times around which the nature of such connectedness alters. Subsequently, using Hausdorff-type metrics on such estimated changes, we cluster spillover patterns to describe changes in the dependencies between which two assets are evidenced to correlate with those between which other two. Creating an induced network from the cluster, we highlight which specific dependencies function as crucial hubs, how the impacts of drastic changes such as COVID-19 ripple through the networks—the Rings of Fire—of spillover dependencies.

Cross-border capital flows and information spillovers across the equity and currency markets in emerging economies

This paper presents a novel perspective on the interaction between equity and currency markets in emerging market economies (EMEs) by (i) examining the nonlinear effects of capital flows on return spillovers between the stock and currency markets in a sample of twelve EMEs via quantile causality tests that capture the quantile specific interactions, and (ii) providing a comparative analysis of the influence of debt versus equity flows over the spillover patterns. We show that the causal effects of international debt and equity flow on return spillovers across the equity and FX markets are largely concentrated at lower quantiles, suggesting that the arrival of information via capital flows tends to ease shock transmissions across these markets. At the same time, international flows are found to facilitate the propagation of shocks in the direction of the currency market from the equity market, in line with the portfolio rebalancing hypothesis wherein equity market fluctuations lead to a subsequent correction in the currency market. The findings have important implications for investors and policymakers regarding the role of international capital flows as a facilitator of informational spillovers in emerging equity and currency markets.

Experimental study on fire characteristics in a double long-narrow confined space connected by multiple lateral doors

A series of experiments were carried out within a scaled-down, elongated, and narrow space featuring multiple lateral doors. These experiments aimed to thoroughly examine the fire characteristics both inside the enclosed compartment and within the connected tunnel, which were induced by the fire occurring in the compartment. It shows that the classic maximum temperature model obtained from a single confined space underestimates or overestimates the values in the carriage and tunnel, and the three-region plume law cannot effectively cover the temperature values inside the carriage, the prediction relations for the double confined space scenario are proposed. The rate of temperature decrease beneath the ceiling in the double long-narrow confined space is greater than in a single confined space, with a significantly reduced impact of HRR on the decay rate. The study also provides a segmented characterization of the influence of HRR on temperature attenuation. Additionally, distinct flame extension and spillover patterns are observed at different fire source locations. These include ceiling jet flame overflow primarily from the upper part of the lateral door and direct overflow from the entire door height. The study's findings offer valuable insights for fire assessment and control in complex confined rail systems.