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At the onset of the Global Financial Crisis in 2007–2008, majority of the analysts and policymakers have anticipated contagion from the markets volatility in the advanced economies (AEs) to the emerging markets (EMs). This chapter examines the volatility spillovers from the AEs’ equity markets (Japan, the United States and Europe) to the four key EMs, the BRIC (Brazil, Russia, India and China).

The period under study, from 2000 through mid-2014, reflects a time of varying regimes in markets volatility, including the periods of dot.com bubble, the Global Financial Crisis and the European Sovereign Debt Crisis, the Great Recession and the start of the Russian-Ukrainian geopolitical crisis. To estimate volatility cross-linkages between the AEs and BRIC markets, we use multivariate GARCH-BEKK model across a number of specifications.

We find that, the developed economies weighted return volatility did have a significant impact on volatility across all four of the BRIC economies returns. However, contrary to the consensus view, there was no evidence of volatility spillover from the individual AEs onto BRIC economies with the exception of a spillover from Europe to Brazil. The implied forward-looking expectations for markets volatility had a strong and significant spillover effect onto Brazil, Russia and China, and a weaker effect on India.

The evidence on volatility spillovers from the AEs markets to EMs puts into question the traditional view of financial and economic systems sustainability in the presence of higher orders of integration of the global monetary and financial systems. Overall, data suggest that we are witnessing less than perfect integration between BRIC economies and AEs markets to-date can offer some volatility hedging opportunities for investors.

Our chapter contributes to the growing literature on volatility spillovers from the AEs to the EMs in a number of ways. Firstly, we provide a formal analysis of the spillovers to the BRIC economies over the periods of recent crises. Secondly, we make new conclusions concerning longer-term spillovers as opposed to higher frequency volatility contagion covered by the previous literature. Thirdly, we consider a new channel for volatility contagion – the trade-weighted AEs volatility measure.

This study aims to explore the risk spillover effects among different sectors of the Chinese stock market after the outbreak of COVID-19 from both Internet sentiment and price fluctuations.

The authors develop four indicators used for risk contagion analysis, including Internet investors and news sentiments constructed by the FinBERT model, together with realized and jump volatilities yielded by high-frequency data. The authors also apply the time-varying parameter vector autoregressive (TVP-VAR) model-based and the tail-based connectedness framework to investigate the interdependence of tail risk during catastrophic events.

The empirical analysis provides meaningful results related to the COVID-19 pandemic, stock market conditions and tail behavior. The results show that after the outbreak of COVID-19, the connectivity between risk spillovers in China's stock market has grown, indicating the increased instability of the connected system and enhanced connectivity in the tail. The changes in network structure during COVID-19 pandemic are not only reflected by the increased spillover connectivity but also by the closer relationships between some industries. The authors also found that major public events could significantly impact total connectedness. In addition, spillovers and network structures vary with market conditions and tend to exhibit a highly connected network structure during extreme market status.

The results confirm the connectivity between sentiments and volatilities spillovers in China's stock market, especially in the tails. The conclusion further expands the practical application and theoretical framework of behavioral finance and also lays a theoretical basis for investors to focus on the practical application of volatility prediction and risk management across stock sectors.

This work investigates the volatility spillovers across stock markets and the nature of such spillovers through different periods of crises and tranquility.

Using daily stock return volatility data from June 2003 to June 2021, the generalized forecast error variance decomposition method (based on Diebold and Yilmaz, 2012 approach) is employed to measure the degree of volatility spillovers/connectedness among stock markets of 24 Asia–Pacific and 12 European Union (EU) economies.

The empirical results from static analysis suggested that about 28.1% (63.7%) of forecast error variance in return volatility for Asia–Pacific (EU) markets is due to spillovers. The evidence from dynamic analysis suggested that during mid of the global financial crisis, European debt crisis (EDC) and Covid-19, the gross volatility spillovers for Asia–Pacific (EU) was around 67% (80%), 65% (80%) and 73% (67%), respectively. The degree of net volatility transmission from Singapore (Denmark) to other Asia–Pacific (EU) markets was found to be highest.

The findings have crucial implications for the investors and portfolio managers in assessment of risk and optimum allocation of assets and investment decisions.

This study adds to the literature on risk management by systematically examining the impact of global financial crises, EDC and Covid-19 on the market interactions by capturing the magnitude, duration and pattern of the shock-specific market volatilities for a large sample of Asian and European markets using recent and large data set.

This study aims to examine the tail connectedness between the Chinese and Association of Southeast Asian Nations (ASEAN) stock markets. More specifically, the authors measure the return spillovers at three quantile levels: median (t = 0.5), lower extreme (t = 0.05) and upper extreme (t = 0.95). The connectedness at extreme upper and lower quantiles provides insightful information to investors regarding tail risk propagation, which ultimately suggests that investors adjust their portfolios according to the extreme bullish and bearish market conditions.

The authors employ the quantile connectedness approach of Ando et al. (2022) to examine the quantile transmission mechanism among the ASEAN and Chinese stock markets.

The results show significant evidence of a higher level of connectedness between Chinese and ASEAN stock markets at extreme upper and lower quantiles compared to the median quantiles, which suggests the use of a quantile-based connectedness approach instead of an average-measure-based one. Furthermore, the time-varying connectedness analysis shows that the total spillovers reach the highest peaks during the global financial crisis, the Chinese stock market crash and the COVID-19 pandemic at the upper, lower and median quantiles. Finally, the static and dynamic pairwise spillovers between the Chinese and ASEAN markets vary over quantiles as well.

This study is the first attempt to examine quantile vector autoregression (VAR)-based return spillovers between China and ASEAN stock markets during different market statuses. Besides, the COVID-19 has intensified the uncertainty in Asian countries, mainly China and ASEAN economies.

This study unravels an attempt to investigate the dynamic connectedness of agri-commodity (wheat) of Russia with 10 financial markets of wheat importing counties during the Russia–Ukraine invasion.

This study took the daily prices of Wheat FOB Black Sea Index (Russia) along with stock indices of 10 major wheat-importing nations of Russia and Ukraine. The time frame for this study ranges from February 24, 2022 to July 31, 2022. This time frame was selected since it fully examines all of the effects of the crisis. The conditional correlations and volatility spillovers of these indices are predicted using the DCC-GARCH model, Diebold and Yilmaz (2012) and Baruník and Křehlík (2018) models.

It is found that there is dynamic linkage of agri-commodity of with stock markets of Iraq, Pakistan and Tanzania in short run while stock markets of Egypt, Turkey, Bangladesh, Pakistan, Brazil and Iraq are spilled by agri-commodity in long run. In addition, it documents that there is large spillover in short run than medium and long run comparatively. This signifies that investors have more diversification opportunity in short run then long run contemplating to invest in these markets.

To the best of the authors’ understanding this is the first study to undertake the dynamic linkage of agri-commodity (wheat) of Russia with financial market of select importing counties during the Russia–Ukraine invasion.

This paper aims to provide new perspectives on the integration of East Asian stock markets and the dynamic volatility transmission to the Bitcoin market utilising daily data from 2014 to 2020.

The authors undertake comprehensive analyses of the dependency dynamics, systemic risk and volatility spillover between major East Asian stock and Bitcoin markets. The authors employ a vine-copula-CoVaR framework and a VAR-BEKK-GARCH method with a Wald test.

(a) With exception of KS11 and N225; HSI and SSE; HSI and KS11, which have moderate dependence, dependencies among other markets are low. In terms of tail risk, the upper tail risk is more significant in capturing strong common variation. (b) Two-way and asymmetric risk spillover effects exist in all markets. The Hong Kong and Japanese stock markets have significant risk spillovers to other markets, and quite notably, the Chinese stock market is the largest recipient of systemic risk. However, the authors observe a more significant risk spillover from the Chinese stock market to the Bitcoin market. (c) The VAR-BEKK-GARCH results confirm that the Korean market is a significant emitter of volatility spillovers. The Bitcoin market does provide diversification benefits. Interestingly, the Chinese stock market has an intriguing relationship with Bitcoin. (d) An increase in spillovers in East Asia boosts spillovers to Bitcoin, but there is no intuitive effect of Bitcoin spillovers on East Asian spillovers.

For the first time, the authors examine the dynamic linkage between Bitcoin and the major East Asian stock markets.

The purpose of this paper is to analyze the nature of returns and volatility spillovers between exchange rates and stock price in the IBSA nations (India, Brazil, South Africa).

The study uses VAR framework and the recently proposed Spillover measure of Diebold and Yilmaz to examine the returns and volatility spillover between exchange rates and stock prices of IBSA nations. In addition, multivariate GARCH with time varying variance‐covariance BEKK model is used as a benchmark against the spillover methodology proposed by Diebold and Yilmaz.

The results of multivariate GARCH model suggests the integration between stock and foreign exchange markets and indicates the existence of bi‐directional volatility spillover between stock and foreign exchange markets in the IBSA countries. Spillover results using the Diebold Yilmaz model suggest the bi‐directional contribution between stock and foreign exchange market, in terms of both returns and volatility spillovers. Overall, results confirm the presence of returns and volatility spillovers within the IBSA nations and, in particular, the stock markets play a relatively more important role than foreign exchange markets in the first and second moment interactions and spillovers.

The market participants may consider the relationship between the exchange rate and stock index to predict the future movement of each other effectively. Multinational companies interested in exchange rate forecasting may consider the stock market as an important attribute. There is an interesting implication for portfolio managers too because of the spillover stock and foreign exchange markets. This knowledge would help to create a fund which performs well. Moreover, the paper can help regulators and policy makers in IBSA nations to understand the structure of the market in a better way and then design their policies.

The study contributes to the literature by extending the existing studies on the spillover between stock price and exchange rate by investigating the issue for three emerging economies, India, Brazil and South Africa. Unlike most studies in the literature which focus on multivariate GARCH model, this is the first study which explores the issue of returns and volatility spillover between the stock prices and the exchange rates using spillover measure of Diebold and Yilmaz and much longer and recent daily data. Moreover, multivariate GARCH with time varying variance‐covariance BEKK model is used as a benchmark against the spillover methodology proposed by Diebold and Yilmaz.

The purpose of this paper is to examine weekly dynamic conditional correlations (DCC) and vector autoregressive (VAR)-based volatility spillover effects within the three Greater China (GC) public property markets, as well as across the GC property markets, three Asian emerging markets and two developed markets of the USA and Japan over the period from January 1999 through December 2013.

First, the author employ the DCC methodology proposed by Engle (2002) to examine the time-varying nature in return co-movements among the public property markets. Second, the author appeal to the generalized VAR methodology, variance decomposition and the generalized spillover index of Diebold and Yilmaz (2012) to investigate the volatility spillover effects across the real estate markets. Finally, the spillover framework is able to combine with recent developments in time series econometrics to provide a comprehensive analysis of the dynamic volatility co-movements regionally and globally. The author also examine whether there are volatility spillover regimes, as well as explore the relationship between the volatility spillover cycles and the correlation spillover cycles.

Results indicate moderate return co-movements and volatility spillover effects within and across the GC region. Cross-market volatility spillovers are bidirectional with the highest spillovers occur during the global financial crisis (GFC) period. Comparatively, the Chinese public property market's volatility is more exogenous and less influenced by other markets. The volatility spillover effects are subject to regime switching with two structural breaks detected for the five sub-groups of markets examined. There is evidence of significant dependence between the volatility spillover cycles across stock and public real estate, due to the presence of unobserved common shocks.

Because international investors incorporate into their portfolio allocation not only the long-term price relationship but also the short-term market volatility interaction and return correlation structure, the results of this study can shed more light on the extent to which investors can benefit from regional and international diversification in the long run and short-term within and across the GC securitized property sector, with Asian emerging market and global developed markets of Japan and USA. Although it is beyond the scope of this paper, it would be interesting to examine how the two co-movement measures (volatility spillovers and correlation spillovers) can be combined in optimal covariance forecasting in global investing that includes stock and public real estate markets.

This is one of very few papers that comprehensively analyze the dynamic return correlations and conditional volatility spillover effects among the three GC public property markets, as well as with their selected emerging and developed partners over the last decade and during the GFC period, which is the main contribution of the study. The specific contribution is to characterize and measure cross-public real estate market volatility transmission in asset pricing through estimates of several conditional “volatility spillover” indices. In this case, a volatility spillover index is defined as share of total return variability in one public real estate market attributable to volatility surprises in another public real estate market.

The purpose of this paper is to study the spillover effects of marketing expertise on the market performance of domestic firms and multinational enterprises (MNEs). Specifically, this study examines how the adoption of frequency loyalty programs by a domestic firm following an MNE affects the competitive dynamics and the market performance of both firms in a Chinese retail gasoline market.

This study is based on empirical data that were obtained from a quasi-field experiment in which the MNE entered the market with a frequency loyalty program and the domestic firm later responded with a similar loyalty program. The authors measured the impact of the adoption of a frequency loyalty program by the domestic firm on the market performance of both the domestic firm and the MNE.

The authors find that the domestic firm’s adoption of a similar loyalty program significantly increased its market share in the regular gasoline market. The domestic firm’s adoption of a loyalty program also increased the market performance of the MNE in the premium gasoline market.

This study explicitly demonstrates the spillover benefits through demonstration effects and provides empirical evidence on specific spillover benefits to domestic firms and MNEs based on their competencies in distinct market segments where they compete.

The purpose of this paper is to empirically examine return and volatility spillovers between oil and the stock markets of Nigeria and South Africa.

The authors make use of an innovative new methodology of capturing spillovers, which is different from what many existing studies use. The authors employ the measures of return spillovers and volatility spillovers of Diebold and Yilmaz (2009, 2012), referred to as spillover indexes. The spillover index facilitates an assessment of the net contribution of one market in the information transmission mechanism of another market.

The empirical results show bi-directional, but weak interdependence between the South African and Nigerian stock markets returns and oil market returns. The results for volatility spillovers show independence of volatilities between Nigeria stock markets and oil markets, while weak bi-directional spillovers were found between South African equity volatilities and oil volatilities. The time-varying total spillover plots for returns and volatilities are broadly similar and show a trend that has been observed in other studies: an increasing trend during the non-crisis period, a burst in the crisis year, a maintained higher level of transmission afterwards.

Existing studies examining spillovers between oil and stock markets have largely ignored Sub-Saharan African markets. A common feature of existing studies is that they have been conducted for two groups of countries: either European and US markets; or Gulf Cooperation Council markets Thus, this study fills this gap in the literature by examining return and volatility spillovers between oil and the stock markets of Nigeria and South Africa.