Extreme Risk Spillovers Research Articles

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.

Extreme risk spillovers in international energy markets: New insights from multilayer networks in the frequency domain

This paper constructs multilayer risk spillover networks in the frequency domain, containing short-, medium-, and long-term layers, to explore extreme risk spillovers in international energy markets at different frequency bands. We analyze the topology properties of multilayer networks at the system, region, and country levels. Based on daily data from 20 national energy stock markets spanning 2006–2023, the empirical results show that: (1) The overall topology of multilayer networks, including the edge structure and spillover strength structure, exhibit frequency heterogeneity. With the onset of a major crisis, the frequency heterogeneity of the network topology increases significantly, and international energy risk spillovers tend to concentrate in fewer layers (i.e., over one or two frequency bands). (2) Long-term risk is primarily responsible for the extreme risk spillover behavior between energy markets. (3) Compared to short- and medium-term spillovers, long-term spillovers are more crisis-sensitive. Furthermore, long-term spillovers within different regions vary in their sensitivity to local or global crises. (4) As market conditions fluctuate, the key players driving the short-term layer change, whereas those driving the medium- and long-term layers remain relatively stable. Overall, our findings provide new insights for policymakers and investors to prevent international energy systemic risks and optimize investment strategies.

Extreme Risk Spillovers From US Soybean Futures Market to China's Soybean‐Linked Futures Markets

ABSTRACTThis study investigates the cross‐border risk spillovers between the US soybean futures market and Chinese soybean‐related futures markets. We first confirm the existence of strong tail dependence between US soybean futures and four Chinese soybean‐related futures by conducting a novel quantile‐Granger causality test. Second, tests under MVMQ‐CAViaR further provide evidence of risk spillovers from CBOT soybean futures to the DCE No.1 soybean, No.2 soybean, soybean meal, and soybean oil futures in value‐at‐risk at different quantiles. Lastly, results from the quantile impulse‐response function reveal the time‐varying and asymmetric property of risk spillover effects. In addition, we compare the results from two subsample periods and identify different risk spillover effects across markets at different quantiles that may contribute to the investors' decision‐making under extreme market conditions.

Extreme weather, policy uncertainty, and risk spillovers between energy, financial, and carbon markets

Extreme weather, as well as policy uncertainties associated with climate change, have fueled global energy market turbulence and cross-market risk contagion, which have had serious effects on the economy. Using time-frequency network analysis based on quantile vector autoregression (QVAR), this study reveals the risk spillovers between China's energy, financial, and carbon markets in different time and frequency domains. The effects of extreme weather and policy uncertainty on market risk spillovers are also examined. The results show that: (1) Risk spillovers vary significantly in different time and frequency domains, and extreme risks tend to occur in the tails of the shock distribution, and market risk spillovers occur mainly in the long term; (2) Extreme weather and policy uncertainty significantly affect energy, financial, and carbon markets, altering their net spillover levels. Among which the coal market is the most volatile and highly sensitive to changes in external conditions; (3) Risk spillovers exhibit distinct time-varying characteristics, with extreme weather, climate policy uncertainty, and trade policy uncertainty exacerbating market risk spillovers. These findings could serve as a reference for monitoring market risk spillovers, facilitating energy transition, and achieving dual‑carbon targets.

Regime changes and extreme risk spillovers of INE crude oil futures

ABSTRACT This study identifies the regime changes and investigates the extreme risk spillovers of China’s first international crude oil futures (INECOFs). The novelty of this study is that a non-linear process is incorporated to examine the structural breaks of the INECOFs and to capture the movements of the INECOFs extreme risk. To facilitate the INECOFs traders and the global financial investors to hedge against extreme risk, the extreme risk spillovers between the INECOFs and the global financial market are investigated. We find that: (1) the two-regime GJRGARCH-SGED model can identify the regime changes of INECOFs and generate more accurate risk measures; (2) the probability of INECOFs in the tranquil regime is 86.01% and in the agitated regime is 13.99%; (3) the INECOFs plays a modest role in the extreme risk spillover network, while the global oil benchmark plays an important role; (4) the INECOFs is an upside risk receiver and a downside risk transmitter. We make the first attempt to examine the regime changes of the INECOFs volatility and the INECOFs extreme risk spillovers.

Extreme risk spillovers in RMB exchange rates: The role of categorical economic policy uncertainties

Extreme risk fluctuations of one exchange rate can spill over to other exchange rates, and exploring the impact of economic policy uncertainty (EPU) on the spillover effect remains a limited but crucial issue. In addition, do different types of EPUs impact exchange-rate risk spillovers consistently, and at which frequencies are they the most significant? To answer this puzzle, categorical EPUs, namely fiscal policy uncertainty (FPU), monetary policy uncertainty (MPU), trade policy uncertainty (TPU), and exchange rate and capital account policy uncertainty (ECPU), are introduced to explore the role that each type of EPU plays in the RMB exchange rate extreme risk spillovers. Moreover, our investigation is also conducted in multiple time-frequency domains through the implementation of the wavelet transform approach. The results indicate that the MPU exerts the most significant impact on total spillovers, while the TPU exhibits the weakest impact. For the net spillovers of each RMB exchange rate, the HKD/CNY and the AUD/CNY are more sensitive to EPUs, particularly in the long-term time horizon. These findings provide potential implications for policymakers in terms of market stabilization and for investors with foreign currency assets.

Consumer Confidence, Uncertainties, and Risk of the UK Travel and Leisure Industry

This study examines the impact of changes in consumer confidence and uncertainty related to economic policy, financial markets, and the crude oil market, on the returns and risk of the UK travel and leisure stock index. Using monthly data from November 2005 to February 2021 and applying quantile regressions and copula-based conditional value-at-risk (CoVaR) measures, the results are as follows. First, consumer confidence and uncertainties significantly impact the travel and leisure index returns across various quantiles, however, the impact is stronger at the left tail than the right tail; furthermore, the sign and magnitude of the impact differ across the various uncertainty measures. Second, there are significant upside and downside extreme risk spillovers from the extreme movements in the consumer confidence and uncertainty indices to travel and leisure index returns, with evidence of an asymmetric impact. Third, the results are influenced by the COVID-19 pandemic and global financial crisis. These findings have implications for market participants, such as portfolio managers, individual and institutional investors, and policymakers in the UK travel and leisure industry.

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.

Does climate policy uncertainty exacerbate extreme risk spillovers between green economy and energy metals?

This study employs a novel quantile time-frequency connectedness approach to capture the risk spillovers between the green economy and energy metals under different market conditions. Also, this paper utilizes the GARCH-MIDAS model to explore whether climate policy uncertainty exacerbates the risk spillover levels. The research findings indicate significant risk spillovers between the green economy and energy metals, notably stronger under extreme quantile conditions. The green economy mainly serves as a risk emitter, while the energy metals act as a receiver. From a dynamic perspective, the risk spillovers are time-varying, with increased fluctuations during periods of climate policy transition. Moreover, extreme risk spillover effects between the green economy and energy metals manifest differences across various frequency domains. The long-term spillovers predominate at lower percentiles, while the short-term effects take over at higher percentiles. Furthermore, climate policy uncertainty significantly exacerbates extreme risk spillovers between the green economy and metals, especially during market turmoil. These findings provide valuable empirical insights for the construction of portfolios and risk management of green assets.

Time-varying risk spillovers between renewable energy and Islamic stock markets: Evidence from the Russia-Ukraine conflict

This paper examines the downside and upside risk spillovers between renewable energy and Islamic stock markets before and during the Russia-Ukraine crisis. We employ the VAR-ADCC model and conditional value at risk (CoVaR) techniques to estimate downside and upside risk spillovers. The results show that the risk spillovers are asymmetric for the majority of cases, but the degree of asymmetry varies across distributions and sub-samples before and during the Russia-Ukraine conflict. Specifically, the downside and upside risk spillover magnitude is not significant between renewable energy and Islamic stock markets of Malaysia, Turkey, and India during the Russia-Ukraine war compared to the pre-crisis period. These results imply that renewable energy markets are not sensitivitive to the Islamic stock markets of the three countries (Malaysia, Turkey, and India) during the Russia-Ukraine war. Our findings also imply that the Canadian Islamic stock markets provide diversification opportunities against renewable energy during the extreme downside risk spillovers. These findings provide interesting insights to portfolio managers and policymakers regarding important decisions like portfolio construction, hedging, and market stability.

Green finance under stress: Unraveling the spillover effects of tail risk

The study was undertaken in direct response to escalating concerns regarding the environmental implications of green assets, with the primary objective of determining the potential of green financial markets to serve as a mitigating force against the risks associated with green stocks, bonds, cryptocurrencies, and carbon markets. To evaluate the risk, the study employed tail risk at the 5% value-at-risk level using the conditional autoregressive value-at-risk (CAViaR) and time-varying parameters vector auto regressions (TVP-VAR) techniques. From the analysis it was found that there was a strong intra-class connectedness between the assets but limited interconnectedness between the markets. Furthermore, we conducted an analysis of dynamic NET spillovers to discern the markets responsible for transmitting and receiving risk spillovers across diverse markets, with the aim of comprehending their behavior both before and after the onset of the COVID-19 pandemic. It was also found that extreme risk spillovers were observed during the study period. These findings have important implications for policymakers, regulators, investors, and other financial market participants. Green assets are designed to combat climate risk by promoting sustainable energy practices and reducing carbon emissions and to maintain the stability of the financial markets under both extreme and typical market conditions.

When one domino falls, others follow: A machine learning analysis of extreme risk spillovers in developed stock markets

This study investigates the potential for extreme risk spillovers across developed stock markets using a machine learning approach. We utilize a novel methodology, proposed by Keilbar and Wang (2022), that combines extreme value theory with artificial neural networks to quantify the likelihood and magnitude of risk spillovers among twenty-three major developed stock markets for the period encompassing January 1991 to July 2022. The results reveal significant evidence of risk spillovers across the markets based on the extent of trade integration among countries. Secondly, during prolonged and vigorous periods of crisis events, extreme risk spillovers and corresponding contagion(s) within this integrated system of markets are likely to return. Moreover, the authors find that the magnitude of spillovers can be influenced by factors such as economic interconnectedness, size, book-to-market, investment portfolio and financial market volatility. The study offers important insights into the nature and dynamics of risk spillovers in developed stock markets and highlights the potential benefits of incorporating machine learning techniques into risk management strategies.

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.

Extreme risk spillovers between US and Chinese agricultural futures markets in crises: A dependence-switching copula-CoVaR model.

The linkages between the US and China, the world's two major agricultural powers, have brought great uncertainty to the global food markets. Inspired by these, this paper examines the extreme risk spillovers between US and Chinese agricultural futures markets during significant crises. We use a copula-conditional value at risk (CoVaR) model with Markov-switching regimes to capture the tail dependence in their pair markets. The study covers the period from January 2006 to December 2022 and identifies two distinct dependence regimes (stable and crisis periods). Moreover, we find significant and asymmetric upside/downside extreme risk spillovers between the US and Chinese markets, which are highly volatile in crises. Additionally, the impact of international capital flows (the financial channel) on risk spillovers is particularly pronounced during the global financial crisis. During the period of the COVID-19 pandemic and the Russia-Ukraine 2022 war, the impact of supply chain disruptions (the non-financial channel) is highlighted. Our findings provide a theoretical reference for monitoring the co-movements in agricultural futures markets and practical insights for managing investment portfolios and enhancing food market stability during crises.

From black gold to financial fallout: Analyzing extreme risk spillovers in oil-exporting nations

Considering various critical periods including the COVID-19 pandemic and the ongoing Russian–Ukraine war, this paper investigates the dynamics of extreme spillover effects from the crude oil market to the financial markets of major oil-exporting countries. With the increased integration of global financial systems, oil market fluctuations can have far-reaching implications for economies that are heavily reliant on oil exports. We employ a wavelet approach to explore the co-movement and lead–lag relationships between the oil market and the financial markets of the considered countries. Next, we follow the newly introduced frequency-based connectedness approach of Hanif et al. (2023) to explore the dynamic connectedness and risk transmission among these markets. First, results from the wavelet coherency technique show that the degree of co-movement during the Russia–Ukraine war was significantly lower than it was under both the pre-crises and COVID-19 pandemic periods as shown by fewer regions with warmer colors (red), which show significant dependence at the 5% level, especially for Canada. Secondly, the dynamic connectedness of these markets was largely driven by long-term dynamics during the Russia–Ukraine crisis period, unlike the short-term driven connectedness observed during the COVID-19 pandemic. The average degree of connectedness at high frequencies (short-term) forms a smaller proportion of the average level of connectedness at low frequencies (long-term), indicating a stronger long-term influence of the crisis on the interconnectedness of these markets. Additionally, we find that Canada and the United States were the major net transmitters of shocks to the network during the conflict period, while Iraq exhibited the strongest level of idiosyncratic shocks. Interestingly, the crude oil market was observed to send stronger shocks to the network at the onset of the war, with the impact gradually diminishing as the conflict progressed. Our study provides valuable insights for policymakers and investors as a guide towards more informed decision-making and appropriate risk management strategies in the face of oil price volatility in these regions.

Climate Risk and Systemic Risk: Insights From Extreme Risk Spillover Networks

Climate Risk and Systemic Risk: Insights From Extreme Risk Spillover Networks

Dynamic Connectedness between Crypto and Conventional Financial Assets: Novel Findings from Russian Financial Market

In the dynamic landscape of the Russian digital economy and increasing financial openness, crypto assets have emerged as influential players in the financial market. The geopolitical and economic developments after a conflict with Ukraine have presented formidable challenges in the shape of financial and trade sanctions, coupled with the suspension from the SWIFT banking system, has plunged the Russian economy into a precarious situation. The current study delves into the network spillover effects between a prominent crypto asset and various financial assets including equity, exchange rates, crude oil, gold, and commodity futures using daily data from January 01, 2018, to August 31, 2023. The purpose of the study is to provide empirical and theoretical insights into countering the impact of sanctions on Russia, proposing a pragmatic solution for the Russian financial market. The research methodology involves the application of network spillover estimation and value-at-risk analysis. Notably, the findings expose a robust association between crypto and financial assets, where crypto assets play a pivotal role in transmitting risk within the financial landscape. While their impact on other financial assets remains relatively subdued, short-term correlations exhibit volatile fluctuations, often marked by sharp increases in downside risk. Theoretical implications follow the portfolio theory of asset pricing, with extreme risk spillover originating from long-run fluctuations in the crypto market, impacting market sentiment and elevating risk propagation in the Russian financial market. These results carry practical significance for payment and receipt processes, as well as trading activities with foreign countries, presenting essential insights for policymakers and investment decision-makers.

The impact of the Russia-Ukraine conflict on the extreme risk spillovers between agricultural futures and spots

The ongoing Russia-Ukraine conflict between two major agricultural powers has posed significant threats and challenges to the global food system and world food security. Focusing on the impact of the conflict on the global agricultural market, we propose a new analytical framework for tail dependence, and combine the Copula-CoVaR method with the ARMA-GARCH-skewed Student-t model to examine the tail dependence structure and extreme risk spillover between agricultural futures and spots over the pre- and post-outbreak periods. Our results indicate that the tail dependence structures in the futures-spot markets of soybean, maize, wheat, and rice have all reacted to the Russia-Ukraine conflict. Furthermore, the outbreak of the conflict has intensified risks of the four agricultural markets in varying degrees, with the wheat market being affected the most. Additionally, all the agricultural futures markets exhibit significant downside and upside risk spillovers to their corresponding spot markets before and after the outbreak of the conflict, whereas the strengths of these extreme risk spillover effects demonstrate significant asymmetries at the directional (downside versus upside) and temporal (pre-outbreak versus post-outbreak) levels.

Measuring dependence structure and extreme risk spillovers in stock markets: An APARCH-EVT-DMC approach

A new APARCH-extreme value theory-dynamic mixture copula (APARCH-EVT-DMC) approach is proposed to investigate the dependence structure and risk spillovers. This method can illuminate the dynamic and complex tail dependence among stock markets. By analyzing the dynamic extreme risk spillovers in stock markets from China to G20 countries for the period from January 4, 2007 to February 7, 2023, firstly, our empirical results indicate that the EVT model is considerably better than the alternative model in fitting the tail distribution and the new DMC models outperform available copula models. Furthermore, the Russian and Argentine stock markets present the largest upside and downside risk spillovers for developed and emerging markets, respectively. Upside and downside risk spillovers from China to Canada and Saudi Arabia stock markets are the smallest for the two types of markets. Our results also provide evidence that upside and downside risk spillovers exhibit asymmetry, or more precisely, the downside risk spillovers are larger than the upside risk spillovers with exceptions in Brazil. Finally, the dynamic risk spillovers display heterogeneity and significant differences across countries.

Extreme state media reporting and the extreme stock market during COVID-19: A multi-quantile VaR Granger causality approach in China

This study examines the causality between extreme state media coverage and extreme stock market return during the COVID-19 crisis in China. Using XinwenLianbo as a proxy for national media, we find bidirectional Granger causality between state media focus/tone and stock market returns under extreme conditions. We also find that the spillover effects of extreme risk are heterogeneous across different industries, companies of different sizes and ownership structures, and at different timescales for stock market returns. Robust and extended testing shows that our results are valid after accounting for the severe impact of COVID-19, although the extreme risk spillover effect may vary slightly on weekends. The focus and tone of national media greatly impact retail and institutional investors, respectively. We also notice that investors have become more optimistic in the post-pandemic era, and the share price plunge on the first day after the Chinese New Year 2020 has had a significant impact on national media coverage.