Volatility Spillover Index Research Articles

The impact of COVID-19 on global financial markets: A multiscale volatility spillover analysis

This paper presents a comprehensive multiscale analysis of volatility spillover mechanisms among diverse financial markets utilizing a novel combination of Normalized Adaptive Multivariate Empirical Mode Decomposition (NA-MEMD) and Time-Varying Parameter Vector Autoregressive (TVP-VAR) model. Our analysis spans Bitcoin, crude oil, gold, foreign exchange, emerging stock markets, and developed stock markets, examining their respective roles in global volatility transmission. Utilizing the NA-MEMD methodology and a time-varying volatility spillover index, we find that in the short-term, Bitcoin and crude oil primarily act as volatility receivers, while other markets serve as volatility transmitters. Over the long-term, gold and Bitcoin consistently act as volatility receivers, with Bitcoin and gold showing evident safe-haven effects, while the remaining markets primarily function as volatility transmitters. Particularly post-COVID-19, developed stock markets emerge as significant transmitters of volatility, largely directed towards the crude oil market. By evaluating and comparing risk transmission patterns among financial markets before and during the COVID-19 pandemic, our findings offer valuable insights that can assist policymakers and other stakeholders in managing financial uncertainty in times of global crises.

Comparative Analysis of Spillover Effects in the Global Stock Market under Normal and Extreme Market Conditions

Using the volatility spillover index method based on the quantile vector autoregression (QVAR) model, this paper systematically examines structural changes and corresponding spillover effects within 20 major stock markets under both extreme and normal market conditions, using data spanning from January 2005 to January 2023. The results show that, compared to the traditional volatility spillover index method, which focuses mainly on average spillover effects, the QVAR model-based spillover index better captures spillover effects under extreme and various market conditions among global stock markets. The connections between stock markets are closer in extreme market conditions. The total spillover index of major global stock markets significantly increases in extreme conditions compared to normal conditions. In extreme market conditions, inflow indices show varying degrees of increase, with emerging economy stock markets displaying more significant increases. The outflow indices exhibit heterogeneity; emerging economies show consistent increases, while developed economies show mixed changes.

Volatility spillovers and carbon price in the Nordic wholesale electricity markets

This paper investigates price volatility and spillovers in the Nordic electricity wholesale markets. We use the Time-Varying Parameter Vector Autoregressive (TVP-VAR), Rolling Window-based VAR (RW-VAR), and high dimensional VAR with common factors (VAR-CF) methods and analyze the integration dynamics among these markets and impact of carbon prices on volatility spillovers. We use 107,352 hourly price data from January 2010 to March 2022. The novelty of this research is four-fold. First, we adopt a connectedness approach to explore volatility interactions among the four Nordic markets, contributing to the scarce literature on volatility in this market. Second, we segment the Norwegian market into southern and northern regions, revealing differences in volatility spillover patterns. Third, we investigate the effect of carbon prices on volatility spillovers and market dynamics. Last, we show significant contribution of covariances to interdependence among markets. We find significant connectedness between the Nordic markets, with an average Total Connectedness Index of between 50% (with a system of variance) and 90% (with a system of both variance and covariance). Sweden is the sole net volatility spillover transmitter, while Denmark experiences the largest shocks from the system. We further find that carbon prices exert a 5% significant impact on the volatility spillover index.

An Economic Analysis of the Impact of Digital Currencies on Land Asset Securities Transactions

Abstract Dilemmas in financing, risk control, and liquidity have arisen in the face of the current land system, and land securitization can provide a way of thinking to solve these problems. This paper proposes four financial instruments for land asset securitization. It calculates the transaction pricing of that land asset security by splitting it into a composite of independent financial derivative values based on the risk-free arbitrage theory in financial engineering. Finally, based on the multiple fractals and volatility spillover index method, the impact of digital currency on the trading dependence and risk spillover of land asset securities when land is securitized as an asset is studied in depth. The results show that the fractal characteristics of land stocks are 0.756, and land bonds are 0.737. The multiple fractal characteristics of the cross-correlation of different land security assets are differentiated, which indicates that the impact of digital currency on the land security assets among them is stronger in the long memory of small fluctuations. The inverse persistence of large fluctuations is stronger. The risk spillover results show that there is a small risk correlation between digital currencies and land securities transactions.

Volatility spillover features in financial industries and identification of systemically important financial institutions: A new perspective

We study the spillover effects between financial industries and the identification of systemically important financial institutions (SIFIs) from a new perspective. The total volatility is decomposed into two categories, i.e., good volatility triggered by positive returns and bad volatility triggered by negative returns, which are applied to the volatility spillover index (the DY spillover index) model (Diebold and Yilmaz, 2012; 2014) to study the volatility spillover effects between different financial industries based on different types of volatility. We construct the total, good, and bad volatility spillover networks and use PageRank to measure the systemic importance of financial institutions based on the three networks and compare them with the risks of financial institutions obtained by the three risk measurement methods of ΔCoVaR, MES, and SRISK. By decomposing total volatility into good volatility triggered by positive returns and bad volatility triggered by negative returns, the positive and negative impacts of different financial industries during different periods can be effectively identified to accurately evaluate the risk levels of different financial industries. The results indicate that the PageRank rankings of financial institutions calculated by the bad volatility spillover network have the highest correlation with the risk rankings of financial institutions calculated by ΔCoVaR, MES, and SRISK. The findings suggest the necessity of decomposing the spillover effects between financial industries into good spillover effects triggered by positive returns and bad spillover effects triggered by negative returns, providing a new way of identifying SIFIs.

Pattern, Source, Destination of Volatilities in Financial Market and Policy Lessons

This paper studies patterns of volatilities and their spillovers across six major segments of Indian financial market applying univariate GARCH model and estimating the Diebold and Yilmaz (DY) volatility spillover index. The study found increasing integration of financial market segments over time and that equity, bank index and corporate bond segments are net contributors while money, gsec and forex segments are net receivers of volatility. The study highlights the need for a healthy banking sector and the increasing significance of corporate bond market volatility and suggests segment specific policy responses to tackle financial market volatilities.

Spillovers between cryptocurrencies, gold and stock markets: implication for hedging strategies and portfolio diversification under the COVID-19 pandemic

PurposeThis study analyzes the static and dynamic risk spillover between US/Chinese stock markets, cryptocurrencies and gold using daily data from August 24, 2018, to January 29, 2021. This study provides practical policy implications for investors and portfolio managers.Design/methodology/approach The authors use the Diebold and Yilmaz (2012) spillover indices based on the forecast error variance decomposition from vector autoregression framework. This approach allows the authors to examine both return and volatility spillover before and after the COVID-19 pandemic crisis. First, the authors used a static analysis to calculate the return and volatility spillover indices. Second, the authors make a dynamic analysis based on the 30-day moving window spillover index estimation.FindingsGenerally, results show evidence of significant spillovers between markets, particularly during the COVID-19 pandemic. In addition, cryptocurrencies and gold markets are net receivers of risk. This study provides also practical policy implications for investors and portfolio managers. The reached findings suggest that the mix of Bitcoin (or Ethereum), gold and equities could offer diversification opportunities for US and Chinese investors. Gold, Bitcoin and Ethereum can be considered as safe havens or as hedging instruments during the COVID-19 crisis. In contrast, Stablecoins (Tether and TrueUSD) do not offer hedging opportunities for US and Chinese investors.Originality/value The paper's empirical contribution lies in examining both return and volatility spillover between the US and Chinese stock market indices, gold and cryptocurrencies before and after the COVID-19 pandemic crisis. This contribution goes a long way in helping investors to identify optimal diversification and hedging strategies during a crisis.

Net-Zero Policy vs Energy Security: The Impact on GCC Countries

Gulf Cooperation Council countries have accumulated large oil portfolio revenues. However, the world economy is seeking to reduce carbon emissions, and in turn, its reliance on fossil fuel resources through investments in renewable energyresources. The aim of this research is to analyze oil portfolio risk from anexporters’perspective, highlighting how relevant determinants, such as the increasing penetration of renewables in the importer counterparties, and financial and policy uncertainty, increase the volatility of oil export portfolios.We construct oil portfolios for four Gulf Cooperation Council countries (Kuwait, Oman, Saudi Arabia, United Arab Emirates) from 2008 to 2018, and compute volatility spillovers à la Diebold and Yilmaz. Then, the effects of policy and economic variables on volatility spillover indices are estimated using different panel linear regression models. We find rising renewable market shares significantly affects oil export portfolio risks and reduces adverse impacts on importing countries of oil market fluctuations.

Industry volatility spillover and aggregate stock returns

ABSTRACT We propose an industry volatility spillover index and find that this new predictor has an impressive in-sample and out-of-sample predictability of aggregate stock returns. A one-standard-deviation increase in this index leads to a 4.14% increase in the expected excess return over the next month. Furthermore, a mean-variance investor can realize sizable economic gains by using this spillover index in his/her asset allocation. The forecasting power of the spillover index remains significant after controlling for popular economic variables and newly proposed predictors. Due to the absence of learning, the spillover index shows increasing predictive ability over the recent period. The economic origins of the spillover index’s success stem from both the cash flow and discount rate channels, as well as significant association with investor sentiment and tail risk. The return predictability of this index is pervasive across characteristic-sorted portfolios and is particularly strong for large-cap or difficult-to-value stocks.

Detecting Plant-Wide Oscillation Propagation Effects of Disturbances and Faults in a Chemical Process Plant Using Network Topology of Variance Decompositions

This work demonstrates for the first time the application of network topology of variance decompositions in analyzing the connectedness of chemical plant process variable oscillations arising from disturbances and faults. Specifically, the time-based connectedness and frequency-based connectedness of variables can be used to compute the net pairwise dynamic connectedness (NPDC), which originated as a volatility spillover index for financial markets studies in the field of econometrics. This work used the anomaly-detection benchmark Tennessee-Eastman chemical process (TEP) dataset, which consists of 41 measured variables and 11 manipulated variables subjected to various faulty operating conditions. The data analytics was performed using key functions from the R-package ‘ConnectednessApproach’ that implements connectedness computations based on time and frequency. The NPDC coefficient matrices were then transformed into network adjacency matrices for the rendering of the network topology of connectedness for TEP. The resulting network topologies allow a comprehensive analysis of oscillation effects across all plant-measured and manipulated variables. Analyzing the directed connectedness of the system dynamics at short-range, mid-range, and long-range frequencies showed how the oscillation effects of disturbances and faults propagate and dissipate in the short-term, mid-term, and long-term periods.

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.

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

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.

The spillover effect between Chinese crude oil futures market and Chinese green energy stock market

With the increasing severe pollution, the new energy industry is greatly favored by the government and investors. Using the static network connectedness method of Diebold and Yilmaz (2009, 2012, 2014) and the dynamic network connectedness approach of Antonakakis et al. (2020), this paper discusses the return and volatility spillover effects between China's crude oil futures market and 7 Chinese green energy stock markets. In terms of return spillover effects, we find firstly that Chinese green energy stock is able to dominate the price changes in the crude oil market, and the dominate role of natural gas stock market is stronger. Secondly, rather than changing the dominant role of the green energy stock market on crude oil futures market price changes, the outbreak of COVID-19 in 2020 strengthened that dominant role. For the volatility spillover effects, the results of static volatility spillover index show that the crude oil futures market volatility is mainly dominated by the green energy equity market, but the dynamic connectedness indices results show that the volatility in the international energy market can strengthen the dominant role of the crude oil futures on the volatility of the green energy stock market. Finally, we can find that both the outbreak of COVID-19 in 2020 and the online operation of the Chinese carbon trading market in 2021 can strengthen the dominant role of the green energy stock market on the crude oil futures market volatility.

Exploring the Contagion Effect from Developed to Emerging CEE Financial Markets

The paper aims to analyze the contagion effect coming from the developed stock markets of the US and Germany to the emerging CEE stock markets of Romania, Czech Republic, Hungary, and Poland using daily data for the period April 2005–April 2021. The paper also captures the level of integration of these emerging stock markets by analyzing the volatility spillover phenomenon. The quantification of the contagion effect coming from the developed to the emerging stock markets consisted of an empirical analysis based on the DCC-GARCH (Dynamic Conditional Correlation) model. Through this multivariate model, the time-varying conditional correlations were analyzed, both in periods of normal economic development and in times of economic instability, when there was a significant increase in the correlation coefficients between developed and emerging stock market indices. Furthermore, the level of connectedness between these markets has been analyzed using the volatility spillover index developed by Diebold and Yilmaz. The empirical results surprised the high level of integration of the analyzed stock markets in Central and Eastern Europe, with the intensity of volatility transmission between these markets increasing significantly during times of crisis. All stock market indices analyzed show periods during which they transmit net volatility and periods during which they receive net volatility, indicating a bidirectional volatility spillover phenomenon. Mostly, the BET, PX, and WIG indices are net transmitters of volatilities, whereas the BUX index is net recipient, except during the COVID-19 crisis, when it transmitted net volatility to the other three indices. Finally, using a Markov switching-regime VAR approach with two regimes, we explored the contagion effect between emerging CEE and developed stock markets during the COVID-19 pandemic. The empirical results proved a shift around the outbreak of the health crisis, after which the high volatility regime dominates the CEE markets. The contagion effects from developed stock markets to emerging CEE markets significantly increased during the first stage of the health crisis.

Impact of geopolitical risk on the volatility spillovers among G7 and BRICS stock markets

Geopolitical risk (GPR) plays an important role in the international financial interactions. Previous studies have mostly analyzed the impact of the GPR on the return or volatility of financial assets, and less attention has been paid on the connection among international stock markets. Therefore, this study attempts to explore the impact of GPR on the volatility spillovers among G7 and BRICS stock markets using the impulse response function. To be specific, TVP-VAR model is utilized to capture the dynamic volatility spillovers among 12 stock markets, and we obtain the total and directional spillover indexes. Then, impulse response function based on the VAR model is applied to capture the dynamic effect of GPR on the spillover series. Based on the weekly price data ranging from August 16, 2002 to April 10, 2023, we find that the volatility spillover index increases when there are large shocks to the market such as war, financial crisis and COVID-19. In addition, the impact of GPR on the total spillover index is negative in the early and late periods, and it shows a more significant positive effect in the middle period. These results reflect the dynamic impact of GPR on the connection among international stock markets, which is essential for the international financial risk management.

Time-varying risk spillovers in Chinese stock market – New evidence from high-frequency data

In this paper, a framework of analyzing risk spillovers between stock sectors is proposed based on the high-frequency data sampled for 5 min. We calculate a weighted directed network through the TVP-VAR model to analyze the time-varying risk spillovers between stock sectors with “different sources and different order moments”. The results show jumps in the realized volatility of five stock sectors during risk events. The static total volatility spillover index of China's stock sector exceeds 70%, which indicates that the risk of Chinese stock sector is high. In addition, volatility spillovers between sectors are asymmetric and time-varying, with “bad” volatility spillovers dominating. After 2019, the sensitivity difference of the overall stock sector to negative news and positive news shocks has a decreasing trend. The sector spillovers of the volatility, skewness and kurtosis are strengthened in the recession period and weakened in the boom period of stock market. Both the industry and utility sectors—particularly the industry sector—are the main net contributors of risk spillovers of volatility, skewness and kurtosis, while the real estate sector acts as the net receiver of risk spillovers. In the future, it is necessary to be alert to risk spillovers from the integrated sector.

Volatility Spillovers Among the Three Places Across the Taiwan Strait: Evidence from a BEKK‐CARR Approach*

AbstractThis study introduces a new BEKK‐CARR model to explore the volatility spillover effects among mainland China, Hong Kong, and Taiwan stock markets during the COVID‐19 pandemic. We also extend the approach of Diebold and Yilmaz (2009, 2012) to infer a brand‐new volatility spillover index to discuss the bi‐directional volatility transmission. Our results show that the trading information flow among these three markets has changed significantly as a result of the COVID‐19 pandemic. The strength of volatility spillover is increasing during this momentous period. The Hong Kong stock market plays a pivotal role in volatility transmission. The values for half‐lives by exogenous shocks keep relatively low during the pandemic period. A reasonable explanation is that the trading information transmissions among stock markets are quicker than in the non‐pandemic period.

Volatility spillovers during normal and high volatility states and their driving factors: A cross‐country and cross‐asset analysis

AbstractUnderstanding the transmission of volatility across markets is essential for managing risk and financial stability, especially under crisis periods during which an extreme event occurring in one market is easily transmitted to another market. To gain such an understanding and enrich the related literature, we examine in this article the system of volatility spillovers across various equity markets and asset classes using a quantile‐based approach, allowing us to capture spillovers under normal and high volatility states. The sample period is 16 March 2011–10 November 2020 and the employed dataset comprises 12 implied volatility indices representing a forward‐looking measure of uncertainty of global equities, strategic commodities and the US Treasury bond market. The results show that the identity of transmitters and receivers of volatility shocks differ between normal and high volatility states. The US stock market is at the centre of volatility spillovers in the normal volatility state. European and Chinese stock markets and strategic commodities (e.g. crude oil and gold) become major volatility transmitters in the high volatility state, after acting as volatility receivers during normal periods. Furthermore, we study the drivers of implied volatility spillovers using regression models and find that US Default spread contributes to the total volatility spillover index in both volatility states, whereas TED spread plays a significant role in the normal volatility state. As for the role of short rate and risk aversion, it is significant in the high volatility state. These findings matter to the decision‐making process of risk managers and policymakers.

아시아 외환시장의 수익률 전이효과에 관한 실증연구

Purpose - This paper analyzes the connectivity of Asian foreign exchange markets using the volatility spillover index of Diebold and Yilmaz (2009/2012).
 Design/Methodology/Approach - The paper used exchange rate data for 11 Asian countries (Korea, Japan, Hong Kong, Singapore, Malaysia, China, Thailand, Indonesia, Taiwan, the Philippines, and Vietnam) and Australia. The data period is from January 2, 2015, to October 4, 2022. Analysis used the volatility spillover index model of Diebold and Yilmaz (2012).
 Findings - As a result, it was found that there were return spillovers in the Asian foreign exchange market, and the total volatility transfer is 59.5%. Singapore, Taiwan, Australia, Korea, Malaysia, Thailand, Indonesia, China, the Philippines, Japan, Vietnam, and Hong Kong have the highest outflow transfer effect in the Asian foreign exchange market, in that order. Singapore, Korea, Australia, Malaysia, Taiwan, Thailand, Indonesia, China, the Philippines, Vietnam, Japan, and Hong Kong have the highest inflow transfer effect, in that order. Singapore, Taiwan, Australia, Korea, Malaysia, and Thailand are the leading markets in the Asian foreign exchange market, while Vietnam, Japan, Hong Kong, China, the Philippines, and Indonesia are dependent markets in the Asian foreign exchange market. As a result of analyzing through a sample moving average analysis, it was found that the outbreak of COVID-19 and the WHO pandemic declaration had the strongest effect on the linkage of the foreign exchange market.
 Research Implications - This study empirically demonstrates the importance of linkages between markets for investors and policy makers in the foreign exchange market.