Extreme Risk Spillovers Research Articles

Asymmetric relationship between green bonds and Sukuk markets: The role of global risk factors

This study investigates the asymmetric connectedness and spillover effects between two ethical fixed-income assets (Sukuk and green bonds) with regard to global risk factors using a sample of 15 Sukuk markets and green bond indices. This complex network allows us to examine the extreme risk spillover and interlinkages across green bonds and Sukuk under different market conditions, captures sudden upward changes in the total and net spillover indices and hence, serves as an alerting system for any impending crisis in relation to global risk factors. Empirical results indicate a persistency feature in the connectedness between Hong Kong and Malaysian, and UK and Nigerian Sukuk markets under different market conditions. More importantly, Sukuk and green bond markets are not largely affected by global risk factors in the middle, upper and lower quantiles. Findings from the portfolio analysis show that Sukuk is effective in hedging the risks of green bonds and global factors. These results of potential diversification characteristics and risk reduction benefits are robust and hold during the Covid-19 pandemic period. Finally, our findings are of paramount importance for investors who are interested in ethical investments as well as policymakers in order to maintain a stable and sound financial system.

Measurement of spillover effect between green bond market and traditional bond market in China

<abstract> <p>With the aim of effectively preventing and controlling systemic risk, by stimulating the advancement of the green bond market, it is significant and imperative to help investors and policymakers adopt more effective measures, which will ensure them to maximize profit. We construct VAR, DCC-GARCH and Copula-CoVaR models, and study the spillover effect between the green bond market and traditional bond market from the three perspectives of mean spillover, volatility spillover and extreme risk spillover using the data on daily closing prices of green bond market and traditional bond market indices. The research findings of this paper are as follows: (1) There are three spillover effects of mean value, volatility and extreme risk among the green bond market, corporate bond market, enterprise bond market and conventional bond market. (2) From the perspective of mean spillover between markets, only the mean spillover between the conventional bond market and the green bond market is bidirectional, and there is the profoundest impact of spillover from the green bond market to the conventional bond market. (3) As far as the volatility spillover between markets is concerned, the volatility spillover between the three traditional bond market and the green bond markets are all positive. The volatility spillover between the conventional bond market and the green bond market is the largest, which is particularly obvious in the first half of 2018 and the first half of 2020. (4) In terms of inter-market extreme risk spillover, the risk spillover between the green bond market and the traditional bond market is positive. The green bond market contributes more to the risk spillover of the enterprise bond market, and it has a time-varying risk spillover effect on the traditional bond market.</p> </abstract>

Assessing the extreme risk spillovers to carbon markets from energy markets: evidence from China.

Previous studies point to the impact of energy market on carbon market under normal market conditions, but little is known about the impact under extreme market conditions. Motivated by these concerns, we aim to investigate the extreme risk spillovers to carbon markets from traditional fossil energy and new energy markets in China. After using copula model to obtain nonlinear tail dependence structure between carbon and energy markets, we compute conditional Value-at-Risk (CoVaR) to quantify extreme risk spillovers. The results indicate that (i) the risk spillovers from both traditional fossil energy and new energy markets to carbon markets are obviously larger when extreme events cause large shocks to energy markets; (ii) there are risks in the opposite direction in carbon markets when carbon-intensive energy markets are under extreme market conditions, but the direction of risks in carbon markets is uncertain when low-carbon energy markets are under extreme market conditions; (iii) the extreme risk spillovers to carbon markets from energy markets are regionally heterogeneous not only in magnitude but also in direction; (iv) energy markets prefer to transmit extreme risks to more liquid carbon markets. These new findings have valuable implications for both policymakers and participating enterprises in carbon markets.

Dynamic comovement and extreme risk spillovers between international crude oil and China's non-ferrous metal futures market

This paper analyses the dynamic comovement and extreme risk spillovers between international crude oil and China's non-ferrous metals futures by combining the application of Copula and CoVaR models, considering the effects of structural breaks and the frequency of cycles. The results show a positive linkage between crude oil and China's non-ferrous metals, with significant long-period comovement. Upside risk in crude oil prices can generate stronger extreme shocks to non-ferrous metals, as evidenced by the outbreak of COVID-19 in 2020. Compared to zinc and aluminum, copper is more susceptible to risk spillovers from crude oil, particularly in the event of a downside trend in crude oil. In addition, WTI crude oil and Brent crude oil show some variability in the upper and lower tail spillover effects. Our research explained to some extent the time-varying, cyclical and asymmetric impact on the non-ferrous metal futures market under crude oil shocks, which can be informative for industry planners, regulators, and investors in making targeted decisions. It is mainly the case when dealing with the many effects of geopolitical conflicts, trade frictions, and commodity supply and demand mismatches.

Extreme risk spillover from uncertainty to carbon markets in China and the EU—A time varying copula approach

Emissions Trading Schemes (ETS) rely on market signals like price stability and predictability, which are affected by the volatility of the financial market and the energy market. Risk is an important indicator of price stability. Using a time-varying copula method, this study analyzes the extreme risk impact of uncertainties (including Economic policy uncertainty (EPU), financial market (VIX) and energy market uncertainty (OVX)) on carbon markets in China and the European Union (EU). This paper also measures the asymmetry of that risk spillover; that is, we identify the impact of increasing and decreasing uncertainties on ETS market risk (stability). Our results show that increased uncertainty in economic policies, financial markets and energy markets will lead to a decline in ETS prices in China and the EU. Chinese and European ETS market risks are most significantly affected by energy market uncertainty changes, and least affected by economic uncertainty changes. Although European ETS markets are riskier than Chinese ETS markets, European ETS prices are more stable. Downside and upside analysis show that risk spillovers to Chinese and European carbon markets are significantly asymmetric and dynamic. Specifically, the downward volatility of uncertainty has a stronger risk impact on ETS than the upward volatility does. Our research provides a useful reference for policy makers to improve ETS market price stability and guide expectations, providing investors with a reliable method to predict market risks.

Time-frequency extreme risk spillover network of cryptocurrency coins, DeFi tokens and NFTs

This study examines the time-frequency extreme risk spillover network among cryptocurrency coins, decentralized finance and non-fungible tokens via wavelet-based quantile causality analysis. We derive the following empirical results. First, long-lived coins dominate cryptocurrencies’ upside and downside risk networks. Second, yield farming tokens exacerbate decentralized finance's depreciation risk but hedge risk in the medium term. Metaverse-related non-fungible tokens are the center of NFTs’ upside risk network but reverse in the long term. Third, traditional concepts affect the depreciation risk, and emerging concepts drive market prosperity in the long run. Overall, these can provide investors with diversified strategies and risk management suggestions.

Extreme risk spillovers across financial markets under different crises

We examine how extreme risk spillovers caused by different crises transmit through financial markets in the US, Europe and Asia-Pacific regions. By modifying the bivariate peak-over-threshold and DCC-GARCH models and using daily negative log-returns from November 1991 to August 2020, we find significant bidirectional extreme risk spillovers with persistent effects between US and other markets. Contributions to extreme risk spillovers vary across crises. The recent COVID-19 shock (non-financial channel) and the 2007–2008 global crisis (financial channel) contribute more to extreme risk spillovers. Moreover, the extreme spillovers in the COVID-19 shock depend more on the non-financial channel compared to the global crisis and cause larger contributions to extreme losses, showing the impact of the non-financial shocks on financial markets. • We investigate how extreme risk spillovers propagate between financial markets in different crisis. • An approach to measure extreme risk spillovers is proposed. • Extreme risk spillovers are persistent and bidirectional. • Extreme risk spillovers transmit through the financial and non-financial channels. • Compared with different crisis, the recent COVID-19 crisis contributes more to extreme risk spillovers.

The extreme risk connectedness of the new financial system: European evidence

The paper empirically analyses the tail risk connectedness between FinTech and the banking sector in the European context over 2015–2022. For this purpose, we use the Tail-Event driven NETworks (TENET) risk model, i.e., we can capture the behaviour of extreme (negative and positive) risk spillover within the financial system. The results highlight how most tail risk spillovers are from banks to FinTech firms. Also, the findings suggest that the spillovers of cross-sector tail risk are more significant in downside (bearish) risk conditions than in upside (bullish) one. We find evidence of an asymmetric effect of extreme risk spillover to the real economy. Finally, we evaluate the monetary policy’s impact on extreme risk. Our findings highlight the importance of closer monitoring risk spillover between FinTech institutions and the European banking system to maintain financial stability.

Conditional dependence structure and risk spillovers between Bitcoin and fiat currencies

This paper investigates the extreme dependence and risk spillovers between Bitcoin and the currencies of the BRICS and G7 economies. We find time-varying dependence between Bitcoin and all currencies. Moreover, when analysing risk spillovers from Bitcoin to currencies, we find that Bitcoin exercises significant power over most currencies, with the South African rand and Brazilian real holding both the highest downside and upside risk before and during the COVID-19 pandemic period, respectively. When considering risk spillovers from currencies towards Bitcoin, the Japanese yen exhibits the highest downside spillovers. Importantly, we find asymmetric spillovers between extreme upward and downward movements.

Green bonds and conventional financial markets in China: A tale of three transmission modes

Existing research does not provide enough knowledge support for Chinese investors to adjust their investment portfolio to integrate green bonds. Given this background, this paper identifies how green bonds transmit information with other assets from three aspects return, volatility, and tail risks. We apply the time-frequency generalized spillover index method and the MVMQ-CAViaR model to shed light on this issue. Our empirical evidence shows that green bonds and fixed-income assets have strong pairwise information transmission in both the return and volatility spillovers, while the equity assets produce the most extreme risk spillovers. The green-connected assets (Green investment stocks and energy commodities) always display weak spillover effects, revealing that few investors have a green preference in the Chinese green bonds market. Moreover, we find that the green bonds would have a different net spillover role across frequency scales and that there are some asymmetric characteristics in extreme risk contagion, which also supplements the understanding of the information transmission modes. In short, this paper provides a comprehensive view of the cross-market information dependencies of Chinese green bonds, thereby obtaining some instructive implications for both green bond investors and policymakers.

Do financial volatilities mitigate the risk of cryptocurrency indexes?

Cryptocurrencies are renowned for their extreme volatilities, while financial volatilities exhibit inherent uncertainty in stock, oil, gold, and currency markets. Given these concerns, the current study endeavors to answer the question of whether financial volatilities can mitigate the risk of cryptocurrency indexes. We employ the time-varying parameters vector autoregression approach and find distinct spillover patterns between financial volatilities and cryptocurrency indexes. In this way, we stress the diversification benefits of financial volatilities to mitigate the risk of cryptocurrency indexes. Time-varying trends demonstrated extreme risk spillovers during turmoil periods. We outlined useful implications for policymakers, financial market participants, investors, and portfolio managers.

Multiscale features of extreme risk spillover networks among global stock markets

This paper studies the multiscale features of extreme risk spillover among global stock markets over various time–frequency horizons. We propose multiscale risk spillover indexes based on GARCH-EVT-VaR, maximal overlap discrete wavelet transform method, and forecast-error-variance decompositions. We further construct multiscale risk spillover networks to visualize risk spillovers at different scales. Our findings show that the US and the UK are detected as the centers of risk spillovers, while Asian stock markets are mainly at the edge of the risk spillover network. The topological properties are unevenly spread over each time scale. The network tends to be closer not only at the short-term scale but also during the financial crisis. For individual features, the US and the UK are super-spreaders of risk spillover at each time scale, while most developing markets mainly act as absorbers. The role of European stock markets is complex at different scales.

Identifying Risk Transmission in Carbon Market With Energy, Commodity and Financial Markets: Evidence From Time-Frequency and Extreme Risk Spillovers

The carbon market is a vital tool to achieve carbon neutrality. This paper uses daily closing price data of Shenzhen carbon trading market, energy, commodity and financial markets from 18 October 2018 to 19 August 2021, examining the transmission of risk/information from the perspective of market volatility spillover and tail risk transmission based on quantile spillover. The stock market crash and COVID-19 have increased the volatility of the system substantially. Next, the increase in trading frequency is accompanied by an increase in total volatility connectivity, and the carbon market transforms into a recipient of systemic shocks. Finally, the results of tail risk transmission reveal that the net effect of carbon reception increases significantly. These findings have implications for policymakers to improve the carbon market and provide important insights for investors to trade in turbulent periods.

The existence of flight-to-quality under extreme conditions: Evidence from a nonlinear perspective in Chinese stocks and bonds' sectors

The COVID-19 pandemic has showed that distress to the financial system is always accompanied with the interconnection between the stock and bond markets. However, limited studies have identified the flight-to-quality effect between these two markets from a nonlinear extreme perspective. Thus, using the multi-quantile VaR Granger causality test that measures the non-linearity of extreme risk, we investigated this effect in Chinese sectors via extreme risk spillover networks. Based on the findings, defensive (offensive) sectors are dominant in the stock market when facing upside (downside) risk to avoid potential investment losses. The results also confirm the robustness of the conclusion that the investment function of the financial markets weakened during the financial crisis. Moreover, compared to the Financial Bond and Enterprise Bond, the Government Bond is likely to show better risk hedging effect in cross-market risk spillover networks due to its high information transparency.

Extreme dependence and risk spillover across G7 and China stock markets before and during the COVID-19 period

PurposeThe paper analyzes downside and upside risk spillovers between stock markets of G7 countries and China before and during the COVID-19 pandemic.Design/methodology/approachBy using VAR-ADCC models and conditional value at risk (CoVaR) techniques, downside and upside risk spillovers between stock markets of G7 countries and China are analyzed before and during the COVID-19 pandemic.FindingsThe results suggested existence of a significant and asymmetrical two-way risk transmission between majority of pair markets, but the degree of asymmetry differs according to the use of the entire cumulative distributions or distribution tails. Downside and upside risk spillovers are significantly larger before the COVID-19 pandemic in all cases except between CAC 40/DAX and S&P/SSE pairs.Originality/valueThe paper used CoVaR and delta-CoVaR to investigate the downside and upside spillovers between stock markets of G7 countries and China before and during the COVID-19 pandemic.

Bearish Vs Bullish risk network: A Eurozone financial system analysis

This paper studies the extreme risk spillover between 183 Eurozone financial institutions (such as banks, insurances, diversified financial, real estate firms) over the period 2005–2020. Employing the Granger causality test across quantiles, we are able to investigate the tail risk interdependence between financial firms under extreme (downside and upside) conditions. Thanks to this framework, we can understand and estimate the risk spillover effect, in different propagation mechanisms, during bad and good conditions. Our findings show a heterogeneous effect between risk spillovers depending on the level of risk considered, highlighting how bearish conditions play an important role in the sectoral propagation of risk spillover. We document the presence of the “shift-contagion” effect. Finally, we investigate the risk-monetary policy nexus. Our findings provide new insights into the impact of the monetary stance on financial stability, documenting the double strategy played by the European Central Bank, namely the “leaning against the wind” and the “modified Jackson Hole consensus” approach.

Extreme risk spillover of the oil, exchange rate to Chinese stock market: Evidence from implied volatility indexes

The study explores extreme risk spillover of the oil and USD/CNY exchange rate to Chinese stock market from the uncertainty volatility perspective, based on the upside and downside Conditional Value-at-Risk (CoVaR) values. Specifically, after testing the statistical significance of the CoVaR and VaR values, we make comparison of the risk spillovers from uncertainty volatility increasing of oil and USD/CNY exchange rate market by computing the upside delta CoVaR. Thus, the empirical results confirm the risk of uncertainty fluctuation in Chinese stock market is much more sensitive to that of in the oil market, while the impact of the USD/CNY uncertainty volatility is relatively weak in most case. In addition, during the reform period of Chinese exchange rate system, the risk spillovers from USD/CNY exchange rate uncertainty fluctuations to Chinese stock market is significant. Furthermore, compared with the effects of USD/CNY exchange rate uncertainty volatility increases, there is great risk spillover from the uncertainty volatility increases in the oil market to that of Chinese stock market during the bearish period.

Modelling extreme risk spillovers in the commodity markets around crisis periods including COVID19

Modelling extreme risk spillovers in the commodity markets around crisis periods including COVID19

Extreme risk spillovers from commodity indexes to sovereign CDS spreads of commodity dependent countries: A VAR quantile analysis

Given the financialization of commodities and the increase in the CDS markets' size and structure, we examine the co-movement and dependence structure between four commodity indexes and sovereign credit risk via an extreme volatility risk spillover methodology. We use the daily change in sovereign CDS data between October 1, 2010 to March 31, 2020 for ten commodity-dependent countries and four commodity indexes (agricultural, precious and industrial metals, and energy). The results of White et al.'s (2015) VAR for Value at Risk (VaR) and the pseudo quantile impulse response function (QIRF) show that the volatility of the primary commodity export category (e.g., agriculture, mineral, and energy) substantially influences the volatility of sovereign spreads (except for two agriculture-dependent exporters). Still, it does not always have the strongest risk spillover effect when other commodity indixes are included in the analysis. When drilling down on the data and examining the single commodity index (i.e., gold, corn, etc.), our results indicate that the primary commodity exports significantly influence the volatility of its sovereign CDS spreads. Based on the results of the QIRF, most shocks are absorbed within 30 days. Most risk spillover from the volatility of sovereign CDS spreads to the volatility of commodity indexes is found to be insignificant.

Dynamic Comovement and Extreme Risk Spillovers between International Crude Oil and China's Non-Ferrous Metals Futures Market

Dynamic Comovement and Extreme Risk Spillovers between International Crude Oil and China's Non-Ferrous Metals Futures Market