Risk Spillover Effects Research Articles

Systemic risk spillover effects within the Moroccan banking industry

Systemic risk spillover effects within the Moroccan banking industry

The nexus between local government debt risk, real estate sector, and financial stability

Rising local government debt and downturn in the real estate sector pose significant challenges to China’s economic growth and financial stability. To analyze these challenges, this paper develops a dynamic stochastic general equilibrium (DSGE) model to study the nexus between government debt risk, real estate sector, and financial stability. The findings suggest that an increase in debt risk initiated by a housing demand shock could have a persistent and magnified effect on the financial sector and the macroeconomy. The amplification mechanism originates from risk spillover and debt crowding-out effect, which trigger the negative feedback between the debt and financial risk. Longer debt maturity and the implementation of credit policy could effectively moderate the contractionary effects of this risk interaction on the economy.

Research on the risk spillover effect of carbon emission trading market and various industry markets in China and the European Union

AbstractWidely concerns over the carbon emission problems have been aroused. Prior studies have documented the correlation between the carbon emission market and industries separately. This study compared the tail risks and risk spillover effect of the carbon emission and 11 industries markets in China and the European Union (EU) by using the multivariate multi‐quartile conditional autoregressive at‐risk model. Moreover, to evaluate the risk spillover of each market under extreme risk conditions in time domain and frequency domain, DY spillover index and BK spillover index were constructed via generalized forecast error variance decomposition and generalized causation spectrum, respectively. Findings are as follows: (1) The tail risks and fluctuation of the trend of Chinese industry markets reflects more higher and larger than those in the EU; (2) The EU suffers from smaller external shocks, while China has the opposite result and can recover relatively faster; (3) In China, energy, industrial, information technology, financial, real estate, consumer goods, carbon emissions, and discretionary consumption industries are risk spillover industries, while healthcare, materials, telecommunication services, and utilities industries are risk receiving industries. In contrast, the risk spillover industries in the EU remain consistent with those of the Chinese markets except for the materials, discretionary consumption, consumer goods, information technology and real estate industries. (4) On the short‐term, medium‐term and long‐term scales, the risk spillover of China's carbon emission trading market and various industries is basically consistent with that of the EU. These findings contribute to reducing greenhouse gas emissions and achieving the goal of carbon peak and carbon neutrality.

Extreme co-movements between CO2 emission allowances and commodity markets and their response to economic policy uncertainty

This study employs the generalized extreme value - autoregressive conditional Fréchet - tail quotient correlation coefficient (GEV-AcF-TQCC) analytical framework to investigate the extreme co-movements between the EU carbon futures market and the commodity futures market. Subsequently, it also examines the response of the dynamic tail quotient correlation coefficient (TQCC) values to economic policy uncertainty stemming from the US using the linear Granger causality test. The empirical results demonstrate that the tail risk indices for both upside and downside trends of the two futures markets experience changes most of the time and show drastic changes during extreme events. More importantly, before the outbreaks of the COVID-19 pandemic (from September 2019 to February 2020) and the Russia-Ukraine conflict (from May 2021 to February 2022), the dynamic TQCC values for the downside trends of the two futures markets remain at relatively high levels. Of note, when examining the dynamic TQCC values between GSCI_down and EUA_down, as well as between GSCI_down and EUA_up, it can be observed that these TQCC values rise to higher levels during periods of extreme volatility. However, they quickly decline as the GSCI futures market experiences significant fluctuations during its downside trend. This pattern indicates a tail risk spillover effect from the GSCI futures market to the EUA futures market. Further analysis using the linear Granger causality test reveals that economic policy uncertainty is a significant driver of the extreme co-movements between the EU carbon and the commodity futures markets in most cases, in addition to the extreme co-movements between the downside trend of the EU carbon futures market and the upside trend of the commodity futures market. The insights gained from this study can provide valuable guidance and enlightenment for policymakers, financial investors, and academic researchers in understanding the dynamics of the extreme relationship between the two markets under discussion, and the impacts of economic policy uncertainty on their extreme co-movements.

Exploring the Spillover effects of tail risk fluctuations in the RMB exchange rate—The time-frequency and quantile connectivity perspective

This study constructs spillover indices from a volatility spillover network perspective using the Quantile Vector Autoregression (QVAR) model, capturing the RMB exchange rate's tail risk and time-frequency effects across varying shock sizes. Empirical results show that the QVAR-based spillover index more effectively captures the tail risk spillover effects across different quantiles. In the time domain, spillovers between RMB exchange rates are dynamic and particularly sensitive to extreme contingencies. In the frequency domain, RMB exchange rates demonstrate significant spillovers, primarily at low frequencies. During extreme upward events, dynamic observations show high-frequency spillovers surpassing low-frequency ones as dominant drivers in the tail spillovers of the RMB exchange rate. Additionally, the analysis of tail dependence indicators indicates a strong asymmetry in RMB exchange rate correlations, emphasizing market participants' heightened sensitivity to unfavorable shocks. These findings can serve as a reference for policymakers to strengthen risk management of the RMB exchange rate.

Risk spillover effect of the new energy market and its hedging effectiveness: New evidence from industry chain

During China’s industrial transformation, studying risk spillover effects and clarifying risk contagion pathways within the new energy industry chain is critical for promoting high-quality development in the new energy. The risk transmission mechanism of the new energy industry chain is explored by constructing a time–frequency index using a time-varying parameter vector autoregressive model in this paper. Additionally, we also further analyze the portfolio and hedging effectiveness of the new energy market. The findings reveal that, first, the downstream market serves as the primary risk source, propagating risk through the industry chain to the midstream and upstream sectors within the new energy industry chain contagion system. Second, the risk spillover effect of the new energy market has significant heterogeneity and time-varying due to the impact of short-term frequency domain drivers and extreme events. Third, the portfolio significantly reduces the investment risk of a single asset. With the extension of the investment horizon, investors’ portfolio weights in the upstream, midstream and downstream industries gradually tend to be even.

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.

Realized Volatility Spillover Connectedness among the Leading European Currencies after the End of the Sovereign-Debt Crisis: A QVAR Approach

This paper examines the time-varying spillover effects and connectedness between the euro and other EU and non-EU currencies after the end of the sovereign-debt crisis. We employ the Quantile Vector Autoregression connectedness approach using intraday data for seven currencies (the euro, the British pound, the Swiss franc, the Polish zloty, the Hungarian forint, the Czech koruna, and the Norwegian krone) spanning from 1 January 2016 to 30 November 2022. The results indicate that, almost in all quantiles, the currencies of Eastern European Group countries (i.e., Czech Republic, Hungary, and Poland) are net contributors of information spillovers to other currencies, while currencies of non-EU countries (Switzerland, UK, and Norway) are net takers. Further, we find that the euro is the highest transmitter of net information spillovers to all other currencies until 2021. Interestingly, after 2021, the euro changes to net information spillover taker from all other currencies; highlighting that external shocks (e.g., COVID-19, the energy crisis) have significant risk spillover effects on the European currency market. Policymakers and market participants could benefit from knowing which currency drives developments to avoid unexpected consequences.

Risk spillovers among oil, gold, stock, and foreign exchange markets: Evidence from G20 economies

This paper investigates the tail risk spillover effects among the stock and foreign exchange markets of G20 economies, as well as the oil and gold markets by constructing a tail event driven network. Adjacency matrices indicate time-varying connectedness between network nodes. The systemic risk decomposition results highlight the predominant contribution of stock markets to the aggregate risk level, while oil, gold, and specific currencies such as JPY, USD, and CNY contribute to diversifying systemic risk. Moreover, tail event driven network quantile regression analysis demonstrates the asymmetry and market heterogeneity of risk spillover effects. Our findings have instructive implications for financial regulators and institutional investors.

The higher-order moments connectedness between rare earth and clean energy markets and the role of geopolitical risk:New insights from a TVP-VAR framework

Against the backdrop of escalating geopolitical risks, the interdependence between rare earth and clean energy markets are crucial for cross-market investors and regulators. However, previous research has not adequately examined the higher-order moment connections between these markets. This study addresses this gap by integrating GRACHSK and TVP-VAR methods to explore the asymmetric and tail risk connectedness between rare earth and solar, wind, nuclear, and electric vehicle markets. Further, we investigated how geopolitical risks impact these connections over time. The results reveal significant higher-order moment connectedness between the clean energy and rare earth markets, each serving as an exporter and receiver, respectively. Among these, wind energy and solar energy exhibit significant net spillovers of skewness and kurtosis to rare earths. These high-order moment spillovers are time-varying and exacerbate during major geopolitical crises, such as the COVID-19 pandemic and the Russian-Ukraine war. The portfolios with minimum connectedness are effective at hedging extreme risks, particularly in terms of kurtosis. It implies that portfolios constructed based on higher-order moments can better hedge against extreme risks. GPR exacerbates higher-order moment spillovers between rare earth and clean energy markets in the short- and medium-term. In particular, the impulse response of kurtosis spillovers to GPR shocks shows prominent and frequent fluctuations, indicating that extreme risk spillover effects with significant lag effects would rapidly diffuse across various markets when geopolitical risks occur. The research findings contribute to three aspects: First, the connectedness analysis offers a network and dynamic perspective on extreme risk management between rare earth and clean energy markets. Second, higher-order moments can enhance the returns of diversified cross-market investment portfolios. Third, the conclusions aid in understanding the evolving patterns of rare earth and clean energy markets under geopolitical risk events, facilitating timely investment and regulatory strategies.

Asymmetric Effects of Renewable Energy Markets on China’s Green Financial Markets: A Perspective of Time and Frequency Dynamic Connectedness

This study investigates dynamic risk spillover effects between renewable energy markets and Chinese green financial markets from a time-frequency perspective by utilizing weekly data from two types of markets with a span from January 2010 to August 2022. The results show that the total spillover and net spillover effects vary widely across time. Short-run spillover is more dominant than long-run spillover. In most cases, green finance markets play the role of risk receivers in the system, while renewable energy markets are the main risk transmitters in the short run and the main risk spillover contributors in the long run. Finally, we determine that the hedging effect of green finance assets in the renewable energy market may decrease after the COVID-19 pandemic.

Volatility and returns connectedness between cryptocurrency and China’s financial markets: A TVP-VAR extended joint connectedness approach

We employ a time-varying parameter vector autoregression (TVP-VAR) joint connectedness approach to study the dynamic risk spillover effects between cryptocurrencies and China’s financial market, further exploring the impact of cryptocurrencies on China’s financial market. Our results show that there is asymmetric risk transmission between cryptocurrencies and China’s financial market, and the risk spillover effect is very weak. Specifically, the spillover of cryptocurrencies to China’s financial market is significantly stronger than the spillover of China’s financial market to cryptocurrencies. Cryptocurrencies have a stronger spillover effect to China’s exchange rate and gold. The net spillover effect of cryptocurrencies is weakening over time. Overall, the return spillover impact of cryptocurrencies on China’s financial market is greater than the volatility spillover impact, and the degree of impact of different cryptocurrencies is heterogeneous. The findings of this study have several implications for policymakers and investors.

Copula-MIDAS-TRV model for risk spillover analysis − Evidence from the Chinese stock market

In this study, a Copula-MIDAS-TRV model with high-frequency realized volatility as the threshold variable is developed for the first time to fit the joint distribution of returns, which takes into account the impact of the leverage effect of volatility on the time-varying interdependence structure among financial markets. Based on this model, we empirically analyze the risk spillover effects between the CSI 300 index and the SSE Composite Index in the Chinese market and test the validity of the model in risk spillover measurement. The empirical findings demonstrate how well the Copula-MIDAS-TRV model, which is the focus of this work, can assess risk spillover effects and analyze the time-varying interdependence between these two indices.

How connected is the oil-bank network? Firm-level and high-frequency evidence

By introducing a new generalized forecast error variance decomposition (GFEVD) approach that splits the same into its contemporaneous and lagged components, we investigate the risk spillover effects of different order moments, derived from intraday data, for the top 10 banks and top 10 oil and gas companies in the U.S., covering the period from December 29, 2017 to December 30, 2022. The study finds that, first, the dynamic total connectedness of all order moments is heterogeneous over time driven by economic events. Second, except realized volatility spillovers, the vast majority of overall spillovers are attributable to contemporaneous spillovers, while only a tiny fraction is associated with lagged spillovers. Finally, realized skewness (crash risk) and realized kurtosis (extreme events) in banks and oil and gas companies originate mainly from intra-industry rather than inter-industry transmission.

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.

Characteristics and influencing factors of risk spillover effects across clean energy stock prices: A comparative analysis during four periods of the COVID-19 pandemic

This paper explores the characteristics and influencing factors of risk spillover effects across clean energy stock prices during four periods of the COVID-19 pandemic, using the TVP-VAR-DY model and the TVP-SVAR-SV model. The empirical results suggest that the risk spillover level across clean energy stock prices declined with the outbreak of the COVID-19 pandemic, and the volatility was initially violent. However, as the pandemic began to mitigate and recovery ensued, the risk spillover level rebounded and became stable. After the COVID-19 pandemic, smart grids and three renewable energy sources (photovoltaics, wind energy, and hydropower) were the main transmitters of net risk spillover effects across clean energy stock prices, while the clean and efficient use of traditional energy was always a net risk spillover effects absorber. Regarding influencing factors, the impact of structural factors (net power generation of various types of clean energy) on the risk spillover effect across clean energy stock prices is generally greater than that of external factors (economic factors and political factors), and these effects last longer. Finally, among structural factors, the net power generation of two renewable energy sources—wind energy and photovoltaics—has a greater impact, especially during the outbreak and mitigation period of the COVID-19 pandemic. Beyond renewable energy, the net power generation from smart grids and nuclear power had a significant impact during the post-COVID-19 recovery period. These new findings suggest useful implications for investors and policymakers in the clean energy markets.

Risk spillover effect of resident leverage on corporate debt

Using panel data from 2008 to 2019, this study analyzes the risk spillover effect of resident leverage on the corporate sector. Thus, the results indicate that increasing resident leverage does not create a more favorable macroeconomic environment for an enterprise to deleverage. Instead, it worsens the debt risk of the enterprise through mechanisms such as weakening corporate solvency, changing the corporate debt maturity structure, and enhancing the financial trend.

Extreme co-movements between decomposed oil price shocks and sustainable investments

This paper investigates the extreme co-movements between three types of decomposed oil price shocks, e.g., supply shock, demand shock, and risk shock, and sustainable investments, using the generalized extreme value - autoregressive conditional Fréchet - tail quotient correlation coefficient (GEV-AcF-TQCC) analytical framework. The empirical results have identified that there are regular extreme co-movements between the three types of oil shocks and sustainable investments under extreme market conditions, especially, during the 2014–2016 oil crisis, the COVID-19 pandemic and the event of negative oil prices in 2020, according to the dynamic tail quotient correlation coefficient (TQCC) method. Specifically, the upside and downside trends of supply shocks, the downside trend of demand shocks, and the upside trend of risk shocks act as transmitters of the tail risks between oil prices and sustainable investments. The results also have demonstrated that the tail risk indexes for both the upside and downside trends of decomposed oil price shocks and sustainable investments have time-varying and asymmetric characteristics. Additionally, the static TQCC results have validated that among the three types of oil shocks, demand shocks exhibit the most pronounced tail risk spillover effects on sustainable investments, followed by supply and risk shocks, whose tail risk spillovers are close to each other. A series of alternative analyses ensure the robustness of the findings presented in this paper, which can offer valuable guidance and insights to policymakers and financial investors.

Exploring the asymmetric influence of economic policy uncertainty on the nonlinear relationship between exchange rate and carbon prices in China

Our objective is to investigate the impact of economic policy uncertainty (EPU) on the risk spillover effect between exchange rates and carbon prices. First, we employ the time-varying parametric vector autoregression (TVP-VAR) method to assess the dynamic nonlinear risk spillover effect between four exchange rates and China's carbon price for the period from September 2, 2013, to April 29, 2022. Our findings reveal a significant time-varying spillover effect between exchange rates and carbon prices. Among them, carbon prices and the EUR/CNY exchange rate predominantly act as risk contributors, and a pronounced correlation is observed among various exchange rates. Furthermore, the magnitude of risk spillover and interdependence intensifies significantly in response to major events. Second, we employ quantile-to-quantile regression (QQR) to examine the impact of EPU on risk spillover. Our results indicate that the influence of EPU on risk spillover exhibits is characterized by marked asymmetry and heterogeneity. At high levels of EPU, EPU has a substantial positive impact on risk spillover. However, when the risk spillover is already elevated, the effect of EPU tends to diminish. Based on these findings, we propose several policy recommendations, including prudent treatment of exchange rate adjustments, fostering stability in carbon prices, and the enactment of robust economic policies.

The dynamic risk spillover effects among carbon, renewable energy, and electricity markets based on the TVP-VAR-DY model.

The linkages among carbon, renewable energy, and electricity markets are gradually strengthening. In order to prevent risk transmission among markets, this paper uses the TVP-VAR-DY (Time-Varying Parameter-Vector Auto Regression-Dynamic) model to analyze the dynamic risk spillover effects and network structure of risk transmission among carbon, renewable energy, and electricity markets. The empirical results show that there are significant asymmetric spillover effects among carbon, renewable energy, and electricity markets. The total spillover index shows that spillover effects among carbon, renewable energy, and electricity markets are time-varying, especially during unexpected events. Besides, the net spillover index indicates that the spillover effects are bidirectional, asymmetric, and time-varying. Finally, under the influence of unexpected events, the network structures of risk transmission among carbon, renewable energy, and electricity markets are heterogeneous. Compared to the Russia-Ukraine conflict, the COVID-19 pandemic has a more significant impact on these markets.