Significant Risk Spillover Research Articles

The impact of climate attention on risk spillover effect in energy futures markets

This study initially develops a risk spillover network within the energy futures market, subsequently analyzing the impacts of climate attention on the risk spillovers associated with individual contracts in this network. We construct a high-dimensional network of 19 futures contracts CoVaR based on the LASSO-VAR method. Furthermore, we construct a climate attention index using the search volume of the climate-related Baidu Index during the same period and use a random forest (RF) model to study its impact on the energy futures market. We find that the energy futures market has a significant risk spillover effect, and climate attention has a significant non-linear effect on risk spillover. In light of increasing climate attention, our RF regression analyses reveal a notable shift in the risk spillover of energy futures. Based on these findings, we recommend tailored management strategies to address this evolving trend effectively.

Cross-Border Spillover of Imported Sovereign Risk to China: key factors identification based on XGBoost-SHAP explainable machine learning algorithm

This paper examines the imported sovereign risk from G20 countries to China and the transmission channels. To explore how sovereign risk transmit in different conditions, the study constructs QVAR model to measure China's imported sovereign risk and use XGBoost-SHAP machine learning algorithm to explain the key channels. The research reveals key findings. First, external sovereign risk importing to China in upper and lower conditions are asymmetric. Second, developing countries have more significant risk spillovers to China comparing to advanced countries in normal condition. Third, there are significant differences in risk transmission channels under different quantile conditions.

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.

The Co-volatility of Uncertainty Shock on Chinese Exchange Rate: Based on Time-frequency Domain and Time-varying Method

In recent years, with the global epidemic, the Russia-Ukraine conflict, the great power game, and so on events, the international political and economic environment has become more and more unstable, the USDCNY exchange rate fluctuates significantly, and the external uncertainty risk shocks negatively hit the foreign exchange market. Based on this, this article expands global uncertainty risk shocks into four dimensions: global geopolitical risk, U.S. economic policy uncertainty, Chinese economic policy uncertainty, and VIX index, in addition to adding international WTI crude oil and gold price as additional variables, which are significantly correlated with both, to study the linkage among uncertainty shocks, commodity volatility and USDCNY exchange rate from the time-frequency domain. Furthermore, we analyze the time-varying spillover effects among variables at different frequency scales by decomposing the data into multiple scales using the Maximum Overlap Discrete Wavelet Transform (MODWT). Findings reveal that uncertainty factors and commodity prices exert distinct influence mechanisms on the USDCNY exchange rate under different cycles of frequency fluctuations. At various time-frequency domain volatility scales, VIX and WTI generate significant risk spillovers over the USDCNY exchange rate. Moreover, when global major events occur, time-varying spillover effects of variables are also heterogeneous. The research findings presented in this paper can provide valuable insights for policymakers and investors regarding the impact of international uncertainty shocks on the Chinese exchange rate across various short, medium, and long frequencies. This understanding can aid in managing uncertainty risks in decision-making processes, ultimately facilitating the efficient functioning of the foreign exchange market.

Exploring interconnectedness between climate change, renewable energy, technological innovation, and G-17 banking stock markets

This study schemes to explore novel insights from crisis risk spillovers and volatility connectedness among climate change, renewable energy, technological innovation, and G17-banking stocks. We adopt a diversified approach combining the time-varying parameter vector autoregressive (TVP-VAR) model for dynamic analysis with the Diebold-Yilmaz framework for static analysis and found that all markets have significant risk spillover and connectedness during the crisis. The ECOI index, OCEAN, and NEX are risk spillover transmitters and designed significant connectedness networks with these markets in crises. Further, France, Canada, and the US banking sectors are the main transmitters in the network during COVID-19. The US banking sector is also the primary risk transmitter during the Russia-Ukraine war. However, Japan and Korea banking stock markets consistently receive risk spillovers in the COVID-19 and Russia-Ukraine wars. This study provides significant implications for all stakeholders across the globe in these financial markets and for sustainable climate and environment.

Governance network externality: Exploring systemic risk generation mechanisms

We show three potential stages in transforming corporate debt default risk to systemic risk in an interlocking directorate network: network contagion of corporate debt default risk, network transformation, and network cascade of systemic risk. In addition, based on the arguments of ‘too connected to fail’ and ‘too big to fail’ in the scope of systemic risk, nodes with higher network connectivity and larger asset scale can significantly improve the efficiency of risk contagion, transformation and cascade in the network. Finally, a significant risk spillover effect exists between the real and financial sectors. From the perspective of corporate internal governance, we confirm that corporate ‘governance connections’ represented by the director network are crucial in transforming micro corporate risks to macro systemic risks and analyzes the whole process of systemic financial risks from endogenous outbreak to diffusion and finally accumulation and outbreak.

Time–frequency correlation and risk spillovers between Euramerican mature and Asian emerging crude oil futures markets

PurposeThis paper aims to empirically investigate time–frequency linkages between Euramerican mature and Asian emerging crude oil futures markets in terms of correlation and risk spillovers.Design/methodology/approachWith daily data, the authors first undertake the MODWT method to decompose yield series into four different timescales, and then use the R-Vine Copula-CoVaR to analyze correlation and risk spillovers between Euramerican mature and Asian emerging crude oil futures markets.FindingsThe empirical results are as follows: (a) short-term trading is the primary driver of price volatility in crude oil futures markets. (b) The crude oil futures markets exhibit certain regional aggregation characteristics, with the Indian crude oil futures market playing an important role in connecting Euramerican mature and Asian emerging crude oil futures markets. What’s more, Oman crude oil serves as a bridge to link Asian emerging crude oil futures markets. (c) There are significant tail correlations among different futures markets, making them susceptible to “same fall but different rise” scenarios. The volatility behavior of the Indian and Euramerican markets is highly correlated in extreme incidents. (d) Those markets exhibit asymmetric bidirectional risk spillovers. Specifically, the Euramerican mature crude oil futures markets demonstrate significant risk spillovers in the extreme short term, with a relatively larger spillover effect observed on the Indian crude oil futures market. Compared with India and Japan in Asian emerging crude oil futures markets, China's crude oil futures market places more emphasis on changes in market fundamentals and prefers to hold long-term positions rather than short-term technical factors.Originality/valueThe MODWT model is utilized to capture the multiscale coordinated motion characteristics of the data in the time–frequency perspective. What’s more, compared to traditional methods, the R-Vine Copula model exhibits greater flexibility and higher measurement accuracy, enabling it to more accurately capture correlation structures among multiple markets. The proposed methodology can provide evidence for whether crude oil futures markets exhibit integration characteristics and can deepen our understanding of connections among crude oil futures prices.

The global spillovers of unconventional monetary policies on tail risks

This article analyzes the risk spillovers of UMPs of four major central banks on the expectations of a market crash in foreign equity markets. The empirical findings show that forward guidance measures exhibit a significant cross-border impact on tail risks. Other expansionary UMPs are innocuous on inducing risk spillovers to other economies. A classification of reversal UMPs into contractionary and tapering provides opposite conclusions: contractionary UMPs shocks exhibit a strong and negative cross-border impact on the tail risks of other economic areas, contrary to tapering announcements. Both the Fed and ECB unconventional policies induce significant risk spillovers with similar magnitudes.

Are the systemic risk spillovers of good and bad volatility in oil and global equity markets alike?

This paper explores the asymmetric connectedness of systemic risk between the oil and global stock markets in both the time and frequency domains. To do so, we introduce time-varying parametric vector autoregressive (TVP-VAR) spillover index models and implied volatility indices to examine risk spillover under bad and good volatility changes. The results reveal that the oil market and global stock markets are highly risk-connected. The risk spillover from increases in oil-stock volatility is greater than that from decreases in volatility, showing an apparent asymmetric phenomenon. Additionally, asymmetric risk spillovers are caused by volatility in the oil market. In particular, the ability to predict systemic risk driven by bad and good volatility changes is enhanced during extreme market periods. Furthermore, we observe that the risk spillovers between the oil and global stock markets under bad and good volatility changes are more pronounced in the short term and that risk spillovers are still dominated by bad volatility in each period. The oil market has a significant risk spillover effect under bad volatility changes in all periods, while it only plays a strong risk spillover role under good volatility changes in the medium and long term. Finally, trading strategies based on minimum connectedness portfolios significantly reduce asset volatility. These findings can aid investors in optimizing their portfolios according to market conditions and provide recommendations to relevant policymakers.

Assessing systemic risk spillovers from FinTech to China’s financial system

Today, the potential of FinTech in China is immense. After a prolonged period of dormancy, a blazing trail in finance surges. This study estimates the extent to which risk is transmitted from FinTech to various sub-industries within the Chinese financial sector, employing the GARCH copula quantile regression model. Our empirical findings indicate that FinTech exerts significant risk spillover effects on these financial sub-industries. Notably, at lower risk levels of 0.1 and 0.05, the securities and state-owned commercial banks sub-industries demonstrate the most substantial and least significant risk spillovers, respectively. Conversely, at the highest risk level of 0.01, the joint-stock commercial banks and securities exhibit the largest and smallest risk spillovers, respectively. Additionally, our analysis reveals that the dynamic risk spillovers for each financial sub-industry differ and reflect the influences of the stock market crash that occurred during 2015–2016. The implications of our study extend to portfolio managers and financial authorities, highlighting the importance of enhancing supervision and regulation of FinTech companies to uphold financial stability.

Research on spillover effect of foreign market risk on Chinese capital market from perspective of full financial opening-up

ABSTRACT Starting from document research, this paper analyzes the mechanism of the risk spillover effect from developed capital markets to the Chinese capital market. After that, this paper conducts an empirical study on the risk spillover effect of developed capital markets on the Chinese capital market by using the DCC-GARCH model. Then the impact degree of global major stock market fluctuations on the Chinese stock market is measured. The analysis shows that there exists a significant risk spillover effect of developed capital markets on the Chinese capital market, but the effect began to weaken after the financial crisis and the size of the spillover effect can be affected by macro factors such as geographical locations, foreign trade, and foreign investment.

Market integration and volatility spillover across major East Asian stock and Bitcoin markets: an empirical assessment

PurposeThis paper aims to provide new perspectives on the integration of East Asian stock markets and the dynamic volatility transmission to the Bitcoin market utilising daily data from 2014 to 2020.Design/methodology/approachThe authors undertake comprehensive analyses of the dependency dynamics, systemic risk and volatility spillover between major East Asian stock and Bitcoin markets. The authors employ a vine-copula-CoVaR framework and a VAR-BEKK-GARCH method with a Wald test.Findings(a) With exception of KS11 and N225; HSI and SSE; HSI and KS11, which have moderate dependence, dependencies among other markets are low. In terms of tail risk, the upper tail risk is more significant in capturing strong common variation. (b) Two-way and asymmetric risk spillover effects exist in all markets. The Hong Kong and Japanese stock markets have significant risk spillovers to other markets, and quite notably, the Chinese stock market is the largest recipient of systemic risk. However, the authors observe a more significant risk spillover from the Chinese stock market to the Bitcoin market. (c) The VAR-BEKK-GARCH results confirm that the Korean market is a significant emitter of volatility spillovers. The Bitcoin market does provide diversification benefits. Interestingly, the Chinese stock market has an intriguing relationship with Bitcoin. (d) An increase in spillovers in East Asia boosts spillovers to Bitcoin, but there is no intuitive effect of Bitcoin spillovers on East Asian spillovers.Originality/valueFor the first time, the authors examine the dynamic linkage between Bitcoin and the major East Asian stock markets.

Examining the interrelatedness of NFTs, DeFi tokens and cryptocurrencies

The high volatility of the blockchain markets has driven the attention of investors and market participants to concentrate on the diversification avenues of NFTs, DeFi Tokens, and Cryptocurrencies. We examined the extreme risk transmission of blockchain markets using the quantile connectedness technique at the median, extreme low, and extreme high volatility conditions. We find significant risk spillovers among blockchain markets with strong disconnection of NFTs. Meanwhile, time-varying features characterized various uneven economic circumstances. Overall, NFTs offer greater diversification avenues with substantial risk-bearing potential among other blockchain markets to shelter the investments and minimize extreme risks.

High-dimensional CoVaR network connectedness for measuring conditional financial contagion and risk spillovers from oil markets to the G20 stock system

This paper employs a new framework, the high-dimensional conditional Value-at-Risk (CoVaR) connectedness based on the LASSO-VAR model, to explore the conditional financial contagion among a specific system conditional on extreme events occurring outside the specific system and its extreme risk spillovers to the system from the systemic perspective. Then, this paper employs the delta CoVaR and CoVaR networks to analyse the risk spillovers from oil markets to the G20 stock system from both the pairwise and systemic perspectives. From the pairwise perspective, the delta CoVaR (∆CoVaR) results show that when separating every G20 stock market from the whole market system, there are significant risk spillovers from oil to G20 stocks only during the crisis period. Further, the CoVaR connectedness results show that the G20 stock contagion presents regional characteristics and oil-related characteristics conditional on oil in extreme risk, and also verify the significant risk spillovers from the oil market to the global stock system from the systemic perspective. North American oil-related countries, including the United States of America, Canada and Mexico, are the most affected, and Asian countries have few shocks when the oil market shifts to extreme risk from a normal state. Last, this paper proposes two main policy implications after considering the empirical results.

The Dependence and Risk Spillover Between Energy Market and BRICS Stock Markets: A Copula-MGARCH Model Approach

The article examines the dynamic dependence structure and risk spillover between the future market of energy commodities and Brazil, Russia, India, China and South Africa (BRICS) stock markets for different market conditions. The study used copula-based multivariate GARCH model, or in short C-MGARCH model, to explore the conditional correlation by multivariate generalized autoregressive conditional heteroskedastic (MGARCH) and the remaining dependence by different copula models. Our results provide significant positive dynamic dependency among crude oil markets (natural gas market) and BRICS stock markets. We then explore the financial implications of volatility spillovers regarding portfolio risk management through an analysis of risk spillovers from energy market to BRICS countries using the value at Risk (VaR), conditional value at risk (CVaR) and delta CVaR. Our findings support the existence of significant risk spillover between crude oil markets (natural gas market) and BRICS stock markets. The presence of volatility spillover among oil prices, natural gas prices and BRICS stock market implies that oil market information (natural gas market information) enhances the volatility forecast in stock markets. Consequently, investors must take oil markets and natural gas markets into account at the time of financial portfolios structuring and in improving their hedging strategies.

Contagion risk: cases of Islamic and emerging market banks

PurposeGlobally influential Islamic banks from the Middle East and Southeast Asia carry voluminous correspondence banking with banks from China and India, leading to potential spillover effect of contagion among the banks from these regions. This study aims to investigate the Islamic banks systemic risk contagion with major banks from China and India.Design/methodology/approachHaving the option pricing theory in the backdrop, the authors calculated three different distance to risk measurements (default, insolvency and capital). The authors have included top six listed globally influential Islamic banks, top seven Indian banks and top eight Chinese banks based on their net asset value. They then measured the banks’ extreme shocks based on the extreme value theory by using the logistic regression model. These extreme shocks helped the authors to map the spillover among the selected banks from multiple regions.FindingsThe authors have found strong evidences of directional risk spillover among the banks in this sample. Islamic banks are receiving a significant risk spillover from the other sample banks but transmitting less toward the other banks from India and China. Hence, there is strong one-directional risk contagion toward the Islamic banks in the study sample.Practical implicationsThis research would be particularly useful to the regulators and bankers from emerging and Islamic markets to understand the conniving nature of the crisis by effectively mapping the source, destination and implementation of the shock transmission mechanism of the potential financial contagion.Originality/valueEven though the corresponding banking among the top Islamic banks from the Middle East and Southeast Asian countries, and banks from India and China, is on the rise, the assessment of risk among these banks has been limited. In particular, the authors extended on the extreme value theory to focus on the wider impact of spillover, including significant direction of contagion from non-Islamic banks to Islamic banks.

Dynamic frequency relationships and volatility spillovers in natural gas, crude oil, gas oil, gasoline, and heating oil markets: Implications for portfolio management

This paper examines the dynamic frequency co-movements and volatility spillovers between crude oil, gas oil, gasoline, heating oil, and natural gas futures markets during the global financial crisis and European crisis (GFC & ESDC), recent oil price crash, and COVID-19 pandemic crisis. We apply the spillover index by Diebold and Yilmaz (2012) and wavelet methods. The results show significant risk spillovers among the leading energy futures markets. Moreover, the spillovers are intensified during the financial crisis, oil crisis and COVID-19 outbreak. WTI crude oil is the highest net contributor of volatility spillovers to the other markets, whereas the other energy markets are net receivers of spillovers for the different sub periods, with the exception of natural gas before the GFC and during COVID-19 as well as Brent oil during COVID-19. Furthermore, the results show significant integration and multiscale co-movements among energy futures. Natural gas asset offers better diversification benefits to crude oil, heating oil, gasoline and gas oil under short term. Finally, the diversification gains diminish as scales rise. The optimal weight, hedge ratios and hedging effectiveness are time varying and crises-sensitive. These findings have significant implications for energy traders and policymakers to inform their decision-making.

Multidimensional risk spillovers among crude oil, the US and Chinese stock markets: Evidence during the COVID-19 epidemic

This paper investigates the multidimensional risk spillovers among crude oil, the US and Chinese stock markets during the COVID-19 epidemic through a GARCHSK-Mixed Copula-CoVaR-Network method. Firstly, we find that during the COVID-19 period, the oil-stock risk spillovers are obviously stronger than those during the normal period. And there are significant risk spillovers from the US and Chinese stock markets to the oil markets. It is also discovered that the oil markets are greatly influenced by the second board stock markets, also known as the growth enterprise markets, especially during the COVID-19 outbreak. Furthermore, the bidirectional China-oil risk spillovers during the COVID-19 pandemic have rapidly increased. Besides, it is reported that the relationships across oil futures, main board and second board stock markets in the US and China are stable under different TSI levels and extreme events. Finally, the GARCHSK-Mixed Copula-CoVaR-Network outperforms the control groups in terms of marginal distribution and dependence structure. Our study not only offers new method and insight into the oil-stock relationship, but also has economic implications for investors and policymakers.

Cross-region risk spillover between the stock and stock index futures markets under exogenous shocks

By integrating the stock and futures markets of mainland China and Hong Kong into the same financial system, we explore the cross-region risk spillovers between the stock market and stock index futures market under the impact of exogenous events. We find evidence of significant risk spillovers between the two stock markets, and confirm that exogenous shocks, including the adjustments of regulatory policies of mainland China and 2019 Hong Kong Protest, can significantly affect the volatility spillover across assets and markets. Our findings can potentially help regulators and investors understand the cross-region risk conduction and assess portfolio risk after exogenous event.

Measuring risk spillovers from multiple developed stock markets to China: A vine-copula-GARCH-MIDAS model

In order to examine the risk spillovers among multiple stock markets efficiently, we develop a vine-copula-GARCH-MIDAS model to estimate the multivariate joint distribution, and then derive CoVaR-type risk measures. Our empirical results on international stock markets show that the vine-copula-GARCH-MIDAS model is promising and is superior to several popular models. One the one hand, it exploits macroeconomic fundamentals to improve the accuracy of CoVaR measure. On the other hand, it is able to measure risk spillovers in a “multiple-to-one” pattern and solve the problem of underestimation in conventional “one-to-one” methods. What’s more, we find that there are significant risk spillovers from multiple developed stock markets including the US, Japan, and Britain, to China, which is necessary for regulators to be concerned with multiple markets simultaneously instead of a single market.