Spillover Index Research Articles

The impact of pandemic on dynamic volatility spillover network of international stock markets.

Since the beginning of the twenty-first century, several pandemics, including SARS and COVID-19, have spread faster and on a broader scale. Not only do they harm people's health, but they can also cause significant damage to the global economy within a short period of time. This study uses the infectious disease EMV tracker index to investigate the impact of pandemics on the volatility spillover effects of global stock markets. Spillover index model estimation is conducted using the time-varying parameter vector autoregressive approach, and the maximum spanning tree and threshold filtering techniques are combined to construct the dynamic network of volatility spillovers. The conclusion from the dynamic network is that when a pandemic occurs, the total volatility spillover effect increases sharply. In particular, the total volatility spillover effect historically peaked during the COVID-19 pandemic. Moreover, when pandemics occur, the density of the volatility spillover network increases, while the diameter of the network decreases. This indicates that global financial markets are increasingly interconnected, speeding up the transmission of volatility information. The empirical results further reveal that volatility spillovers among international markets have a significant positive correlation with the severity of a pandemic. The study's findings are expected to help investors and policymakers understand volatility spillovers during pandemics.

Research on tail risk contagion in international energy markets—The quantile time-frequency volatility spillover perspective

From the perspective of volatility spillover network, this paper constructs the spillover index based on the quantile vector autoregression (QVAR) model to capture tail risk spillover effects in international energy markets under different shock sizes and frequency domains. Using the traditional and clean energy markets data covering 2010 to 2022, the empirical results show that the spillover index based on the QVAR model can better capture the tail risk spillover effects with different shock sizes, while the conditional mean-based spillover index may underestimate or misjudge the true level of tail risk spillover among markets. In the extreme state, the tail risk spillover effect among energy markets is significantly enhanced compared to the normal state. From the perspective of frequency domain, it is found that the total tail risk spillovers among international energy markets are dominated by long-term risk spillovers. At the same time, the asymmetry of tail risk spillover effect is more remarkable in the extreme state in the long-term. In addition, the traditional energy markets are the main sources of tail risk spillover, and mainly play the role of net risk spillover. In contrast, the fuel cell, geothermal energy and solar energy markets in the clean energy market have greater net spillover-in effect and play the net receiver role in the interconnectedness network.

Dynamic spillovers between clean energy and non-ferrous metals markets in China: A network-based analysis during the COVID-19 pandemic

Due to the close production link between clean energy and non-ferrous metals, their price and market dynamics can easily affect one another through production costs. Furthermore, with the increased financialization of clean energy and non-ferrous metals markets, investment risk can easily spread between them. Therefore, this paper intends to explore the risk contagion between the two markets through the spillover index model and the minimum spanning tree (MST) method. Employing the data collected in China, this paper quantifies the magnitude of risk transfer by the volatility spillovers of eight clean energy stock markets as identified in The Energy Conservation and Environmental Protection Clean Industry Statistical Classification 2021 and the eight corresponding non-ferrous metals futures markets, while fully considering the heterogeneity between sub-markets. First, we find that risk is mainly transmitted from clean energy to non-ferrous metals. Second, this paper identifies not only the most influential market but also the shortest path of risk contagion based on the MST topology analysis. Last, the empirical results show that the COVID-19 has increased the scale of risk transmission between the two markets and their connectivity. During the COVID-19 period, the shortest path between the two markets shifted from “hydropower–gold” to “smartgrid–zinc”, and the systematically influential markets correspondingly become smartgrid and zinc. The results obtained in this paper might have practical implications for policymakers seeking to achieve effective risk management, which could also facilitate investors for diversification benefits.

Risk Connectedness among International Stock Markets: Fresh Findings from a Network Approach

In this study, we analyze the upside and downside risk connectedness among international stock markets. We characterize the connectedness among international stock returns using the Diebold and Yilmaz spillover index approach and compute the upside and downside value-at-risk. We document that the connectedness level of the downside risk is higher than that of the upside risk and stock markets are more sensitive when the stock market declines. We also find that specific periods (e.g., the global financial crisis, the European debt crisis, and the COVID-19 turmoil) intensified the spillover effects across international stock markets. Our results demonstrate that DE, UK, EU, and US acted as net transmitters of dynamic connectedness; however, Japan, China, India, and Hong Kong acted as net receivers of dynamic connectedness during the sample period. These findings provide significant new information to policymakers and market participants.

Spillover and connectedness among G7 real estate investment trusts: The effects of investor sentiment and global factors

This paper examines the dynamic spillover interconnectedness of G7 Real Estate Investment Trusts (REITs) markets. We use the spillover index of Diebold and Yilmaz (2012), the time-varying parameters vector-autoregression (TVP-VAR) model, and the quantile regression approach. The result show that REITs network connectedness is dynamic and experiences an abrupt increase in the first wave of COVID-19 outbreak (2020Q1). We also observe a substantial abrupt decrease in connectedness during the success of vaccination programs (end 2021). The connectedness among assets is much stronger during COVID-19 than before. The REITs of Japan and Italy are net receivers of spillover and those of US and UK are net transmitters of spillovers before and during COVID-19. Conversely, the REIT of Canada and Germany (France) switches from net receivers (contributors) of spillovers before the pandemic to net contributors (receivers) during the COVID-19. Finally, we show that News Sentiment index, Geopolitical Risk index, Economic Policy Uncertainty index, US Treasury yield, and Stock Volatility index influence the spillover magnitude across quantiles.

Sustainability and stability: Will ESG investment reduce the return and volatility spillover effects across the Chinese financial market?

This study provides a preliminary investigation of the relationship between sustainability and stability by investigating the impact of ESG investment on the return and volatility spillover effects in the major Chinese financial markets, including the stock, bond, interbank, and foreign exchange markets. We adopted both the TVP-VAR and DY methods to calculate the time-varying total, directional, and pairwise spillover indices. We examined the impact of ESG investment on financial market stability by comparing the spillover effects when ESG investment, represented by the ESG stock index, is considered with those without special consideration on the ESG investment, represented by the general stock index. The results show that when the ESG stock index replaces the general stock index, the total, directional, and pairwise spillover effects in the Chinese financial market generally decrease. Meanwhile, we find that although the overall Chinese financial market spillover index is around 13%, it is occasionally quite volatile. In particular, the markets were hugely uncertain in 2013 and 2020 due to the disequilibrium of supply and demand conditions in the money market and the considerable shocks created by the COVID-19 pandemic. We support the idea that, while the Chinese government develops its green finance, for instance, by advocating for ESG investment, it simultaneously builds a more stabilized financial market. In other words, sustainability and stability are positively correlated and can be achieved together. The reason for this is that ESG investment supports a long-run investment strategy by reducing excessive short-run speculation activities in the Chinese stock market, which accounts for the volatile property of the market since it was launched.

Spillovers in the joint system of conditional higher-order moments: US evidence from green energy, brown energy, and technology stocks

This study analyses the spillover in higher-order moments covering volatility, skewness, and kurtosis in the space of green energy, brown energy, and technology stocks in the US from May 8, 2006 to May 18, 2022. The analysis shows four noteworthy findings. Firstly, besides volatility, spillovers of skewness and kurtosis are important. Secondly, spillovers fluctuate with time and intensify during crisis periods, especially around the oil price crash of 2015. Thirdly, clean energy is the major net transmitter of volatility and kurtosis shocks; wind energy and crude oil are the major net transmitters of volatility and skewness shocks, whereas solar energy plays the same role for skewness spillovers. Fourthly, the spillover analysis based on the three moments jointly estimated in the same system indicates that the combined contribution of cross spillovers of volatility, skewness, and kurtosis to the total spillover index constitutes more than 40%. These results provide new insights for investors and policymakers necessary for making comprehensive inferences about asset pricing, portfolio risk management, and market stability under the energy transition. They suggest that if the spillover channels of skewness and kurtosis are overlooked, the risk propagation across these markets will be underestimated, potentially jeopardizing financial stability.

Analyzing the network structure of risk transmission among renewable, non-renewable energy and carbon markets

The linkage of renewable, non-renewable energy and carbon markets is increasing, and there is a complex network structure for the risk transmission among multiple markets. Based on the methods of network topology analysis and DY spillover index, this paper analyzes the time-varying spillover effect and network structure of risk transmission among renewable, non-renewable energy and carbon markets. The results show that: according to the static spillover index, there are significant spillover effects among renewable, non-renewable energy and carbon markets, and they are asymmetric. Moreover, the total spillover index further shows that the spillover effect between energy and carbon markets is time-varying, especially during the extreme events. Specifically, the net spillover index shows that the spillover effects among renewable, non-renewable energy and carbon markets are bidirectional, asymmetric and time-varying. Additionally, under the influence of various extreme events, the spillover effect and network structure of risk transmission among renewable, non-renewable energy and carbon markets are heterogeneous. Compared with the shale oil revolution and the Sino-US trade dispute, the influence of COVID-19 is more significant and complex, and it is long-term and comprehensive. Finally, some policy implications for preventing risk transmission and optimizing the energy structure to promote emission reduction are put forward.

Analysis of Risk Spillover and Asymmetry Between Three Crude Oil Markets and Chinese Financial Markets

Providing a scientific basis and method to ensure the smooth functioning of the Chinese crude oil market would be hugely significant to China’s future economic development and security. To this end, attempts have been made to both internationalize the Shanghai crude oil market and minimize financial risks. This paper selects three crude oil markets (INE, WTI and Brent) and five Chinese financial markets (the futures, bond, fund, stock and foreign exchange markets) as the research objects. The Diebold and Yilmaz spillover index model and the multifractal asymmetric detrended cross-correlation analysis (MF-ADCCA) method are used to study the volatility spillover effect and the asymmetric cross-correlation between crude oil markets and financial markets. When the volatility spillover effect and the asymmetric relationship that exists between the financial markets are examined, the volatility spillover of the oil market to the financial market is found to be significantly higher than that of the financial market to the oil market. In particular, the spillover effect was even more significant from late-2019 to early-2020. Analysis demonstrates an asymmetric cross-correlation between crude oil markets and the abovementioned five Chinese financial markets. In particular, the impact of the Chinese crude oil market on the stock market is greatest, especially with respect to the Brent and WTI crude oil markets. Except for the bond market, when the INE and Brent markets are increasing, the risk exposure to financial markets is more significant. Among financial markets, INE-Bond market asymmetry is stronger than WTI-bond market asymmetry, but weaker than that of the Brent-Bond market when there are large fluctuations.

Forecasting aggregate stock market volatility with industry volatilities: The role of spillover index

In this paper, we aim to improve the predictability of aggregate stock market volatility with industry volatilities. The empirical results show that individual industry volatilities can provide useful predictive information, while the predictive contribution is limited. We further consider the spillover index between industry volatilities and find it displays strong predictive power for stock market volatility. Based on the portfolio exercise, we find that a mean-variance investor can achieve sizeable economic gains by using volatility forecasts of the spillover index. In addition, we conduct three extended analyses and further demonstrate the superior performance of the spillover index. Also, our results show robustness to a series of alternative settings. Finally, we investigate why the spillover index performs better and answer what information it contains. The results show that the spillover index can reflect and explain investor sentiments that are related to stock market volatility.

High frequency volatility spillover between oil and non-energy commodities during crisis and tranquil periods.

In this article, we scrutinize volatility spillover between oil and individual non-energy commodities during crisis and non-crisis periods. We use high-frequency data to capture the effects of both the global financial crisis (2008) and the COVID-19 pandemic between 2008 and 2022. To this end, we utilize wavelet coherence analysis to diagnose the magnitudes of dynamic co-movements and lead-lag effects between commodities. Our results provide evidence of strong coherence between oil and the majority of individual non-energy commodities during both crises. Precious metals were generally found to exhibit heightened levels of co-movement with oil as opposed to other non-energy commodities. On the other hand, weak co-movements were found between oil and a few commodities, namely soy, wheat, zinc, and tin. The lead-lag effects of oil on agricultural commodities, base metals, and precious metals were evident, especially during crisis periods. However, aluminium and precious metals, especially gold, silver, and palladium, also had a lead-lag effect on oil at different points in time, including during the pandemic. We further utilize dynamic frequency-domain connectedness for capturing pairwise volatility spillover indices, with the results providing evidence of heightened volatility spillovers during turbulent times. Our findings have significant implications for retail investors, portfolio managers, and policymakers.

Market Volatility Spillover, Network Diffusion, and Financial Systemic Risk Management: Financial Modeling and Empirical Study

With the accelerated pace of financial globalization and the gradual increase in linkages among financial markets, correctly identifying and describing the risk spillover and network diffusion in the financial system is extremely important for the prevention and management of systemic risk. Based on this, this paper takes the equity markets of 17 countries around the world from 2007 to 2022 as the research object, measures the volatility spillover effect of global financial markets using R-Vine Copula and the DY spillover index, constructs the volatility spillover network of global financial markets, discovers the spillover and diffusion pattern of global financial market risks, and provides relevant suggestions for systemic risk management. It is found that (1) there are certain aggregation characteristics in the network diffusion of global financial market volatility spillover; (2) developed European countries such as the Netherlands, France, the UK, and Germany are at the center of the network and have a strong influence; (3) Asian countries such as China, Japan, and India are at the periphery of the network; and (4) shocks from crisis events enhance the global financial market volatility spillover effect. Based on the above findings, effective prevention of global financial market risk volatility spillover and network diffusion and reduction in systemic risk need to be carried out in two ways. First, by focusing on the financial markets of key countries in the network, such as the Netherlands, the UK, France, and Germany. The second approach is to mitigate the uneven development in global financial markets and reduce the high correlation among them.

Better to Give than to Receive: A Study of BRICS Countries Stock Markets

This study uses the MGARCH-BEKK model and Diebold–Yilmaz (DY) volatility spillover index to examine volatility spillovers among BRICS countries’ stock markets. The study finds that the own volatility spillover is more than the cross-markets and has increased during the financial crisis. In contrast, the cross-markets volatility spillovers have decreased after the financial crisis. The total net return spillover increased during the crisis period (27.30%) and the pre-crisis period (25.50%) in comparison with the post-crisis period (6.30%) and the whole sample period (10.70%). Brazil is the highest net volatility transmitter among the BRICS countries’ stock markets, and China is the highest net volatility receiver. We learned from the volatility network connectedness that China is highly connected with India regarding volatility. Foreign institutional investors may use this study’s result to find diversification opportunities across the BRICS stock markets. JEL Codes: F3, G11, G12, G15

The time-varying co-movements between energy market and global financial market

Since the global financial crisis in 2008, international energy markets have become more closely linked to financial markets and energy prices have exhibited more financial characteristics. Therefore, it is of great theoretical and practical significance to study the time-varying synergy between the energy market and the global financial market. This paper sets up a model for realizing the time-varying co-movements between energy markets and global financial markets: It uses the Diebold &Yilmaz spillover index method and its dynamic expansion model to test the spillover mechanism of market volatility shocks, applies the deep long and short-term memory (DLSTM) model to predict market prices. The results of this study show that, first, energy markets and global financial markets are closely linked networks, and the spillover effects have obvious time-varying characteristics. Second, from a static spillover perspective, the Global Financial Price Index shows the largest net exporter in both yield and volatility spillovers, suggesting that the global financial development market has the strongest influence on other markets. However, in the volatility spillover, the net spillover index shows alternating periods of positive and negative periods most of the time.

The role of China's crude oil futures in world oil futures market and China's financial market

China's crude oil futures (COF) denominated in RMB were formally launched in 2018 and traded in the Shanghai International Exchange (INE), which was an important milestone in China's energy financialization. Combining TVP-VAR method and DY spillover index, this paper firstly studied the information spillover relationship between the world's major COF and crude oil spot (COS) markets before and after the launch of China's COF. It is found that China's COF mainly play the role of net information receiver in world COF/COS market, and its influence in international market and price discovery function needs to be improved. Further, this paper comprehensively considered China's financial system, taking volatility of financial time series as risk proxy to investigate the risk spillover effect. The results show that the financial attributes of China's COF have become prominent. The dynamic analysis shows that since the launch of China's COF, the degree of its net spillover to other markets has generally been on the decrease, assuming a net risk transmitter role. On this basis, this paper puts forward policy recommendations to promote the development of China's COF.

Information spillovers between carbon emissions trading prices and shipping markets: A time-frequency analysis

Climate change has become mankind's main challenge. Greenhouse gas (GHG) emissions from shipping are not totally irresponsible for this representing, roughly, 3% of the global total; an amount equal to that of Germany's total GHG emissions. The Fourth Greenhouse Gas Study 2020 of the International Maritime Organization (IMO) predicts that the share of GHG emissions from shipping will increase further, as international trade recovers and continues to grow, alongside with the economic development of India, China, and Africa. China and the European Union have proposed to include shipping in their carbon emissions trading systems (ETS). As a result, the study of the relationship between the carbon finance market and the shipping industry, attempted here for the first time, is both important and timely, both for policymakers and shipowners. We use wavelet analysis and the spillover index methods to explore the dynamic dependence and information spillovers between the carbon finance market and shipping. We discover a long-term dependence and information linkages between the two markets, with the carbon finance market being the dominant one. Major events, such as the 2009 global financial crisis; Brexit in 2016; the 2018 China-US trade frictions; and COVID-19 are shown to strengthen the dependence of carbon finance and shipping. We find that the dependence is strongest between the EU carbon finance market and dry bulk shipping, while the link is weaker in the case of tanker shipping. Nonetheless, carbon finance and tanker shipping showed a relatively stronger dependence when OPEC refused to cut production in 2014, and when the China-US trade disputes led to the collapse of oil prices after 2018. We show that information spillovers between carbon finance and shipping are bidirectional and asymmetric, with the carbon finance market being the principal transmitter of information. Our results and their interpretation provide guidance to governments on whether (and how) to include shipping in emissions trading schemes, supporting at the same time the environmental sustainability decisions of shipping companies.

Is There Any Impact of the World Uncertainty Spillover Index (WUSI) on Firm Investment? Evidence from Turkey

Bu makale, Dünya Belirsizlik Yayılma Endeksi-Amerika Birleşik Devletleri'nin (WUSI-USA) 164 Türk imalat firmasının 2005 ve 2019 yılları arasındaki yatırım politikası üzerindeki etkisini incelemektedir. Bulgulara göre, WUSI-USA'nin firmaların yatırım politikası üzerinde olumsuz bir etkisi vardır. Reel opsiyon teorisine göre firmalar belirsiz koşullar altında yatırımlarını ertelemeyi tercih etmektedirler. Alternatif yatırım ölçümlerinin kullanılması, sonuçlarımızın geçerliliğini doğrulamaktadır. Genel olarak, bu çalışma ABD'den gelen belirsizliğin firmaların yatırım kararlarını etkilediğini ortaya koymaktadır. Türk firmaları finansal kararlarına belirsizlik yayılmalarını dahil etmeli ve stratejilerini belirsizliğin yayıldığı zamanlarda firmaya özgü faktörlere göre ayarlamalıdır. Politika yapıcılar ve firma yöneticileri, yatırım stratejilerini oluştururken ABD'deki belirsizlik yayılma etkilerini dikkate almalıdır.

Dynamic connectedness among Pakistani stock markets and its major trading partners

PurposeInternationalization and financial deregulation have caused market participants and policymakers to consider the significance of financial connectedness and the spillover effects of shocks. In this context, this research is a pioneering effort to investigate the direction and magnitude of return volatility spillovers between Pakistan’s financial markets and those of its key trade partners. This paper examines the relationship between return and volatility spillover in the financial markets of Pakistan and its major trading partners.Design/methodology/approachTen countries are selected for empirical examination of dynamic connectedness among Pakistan and its major trading partner’s stock markets. This study utilizes a spillover index approach model and considers daily, weekly and monthly datasets spanning 25 years from 1995 to 2019.FindingsThe results indicate that stock markets provide efficient channels for return and volatility spillovers. Moreover, it is found that the intensity of spillovers during the financial crisis is more intense as these crises are major determinants of contagion; consequently, investors, speculators and policymakers use these events for their respective purposes.Originality/valueResearchers, practitioners, policymakers and investors may all benefit from the findings in areas including risk management, portfolio diversification and trading methods.

Forecasting global stock market volatility: The impact of volatility spillover index in spatial‐temporal graph‐based model

AbstractThe shocks on certain market spread to other markets due to the financial linkages of global economy, which is known as volatility spillover effect. In this study, we propose a volatility forecasting model for global market indices using the spatial‐temporal graph neural network (GNN). The volatility spillover between markets are reflected in the model by estimating the linkage between markets, which is the input of GNN, using the volatility spillover index. An empirical analysis is conducted on eight representative global market indices. From the out‐of‐sample results, we found the following features. First, the proposed spatial‐temporal GNN spillover model outperforms the benchmark models in short‐ and mid‐term forecasting. Second, the forecasting accuracy highly depends on the inclusion of the market index with a high volatility spillover effect. Including S&P500, which contains the highest net spillover index, effectively helps to forecast the volatilities of other markets. Third, the investor can gain economic gain by using predicted volatility from proposed model in the mean‐variance framework.

RETURN AND VOLATILITY SPILLOVER ACROSS STOCK MARKETS OF THE US AND ITS MAJOR TRADING PARTNERS

ABSTRACT This Study Applies The Diebold And Yilmaz (2012) Spillover Index Methodology To Analyze The Effects Of Return And Volatility Spillovers Between The Us Market And Its Major Trading Partners Using Weekly Stock Market Returns From January 2011 To December 2019. The Findings Provide Credence To The Hypothesis That The Us Market Has Been The Most Influential In Terms Of Return Spillover (259.5) To Other Stock Indexes, With The Biggest Return Spillovers Occurring In The Canadian Market (58.4) And The German Market (57.0). In A Similar Manner, The Us Market Has Been The Biggest Transmitter Of Volatility Spillover (202.3) To Other Stock Indexes, With The Highest Volatility Spillovers Occurring In The Canadian Market (51.8) And The German Market (45.8). The Us Market Has Been The Most Influential Based On Return And Volatility Spillovers. This Finding Also Suggests That The Us And Chinese Markets Are The Least Vulnerable To Foreign Shocks. In Contrast, The Canadian Market Is The Most Vulnerable To External In Terms Of Return Spillover And Volatility Spillover. As A Consequence, Chinese Stock Market Participants Can Still Reap The Rewards Of Diversity. During The Sample Period, However, Integration Across Developed Stock Markets Has Increased, Reducing The Benefits Of Diversity. Keywords Return and Volatility Spillovers, Diebold and Yilmaz (2012) Spillover Index, Rolling Window Analysis