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This paper aims to examine dynamic equicorrelations (DECO) and directional volatility spillover effects among four energy futures markets, namely, West Texas Intermediate crude oil, heating oil, natural gas and reformulated blendstock for oxygenate blending gasoline, by using a multivariate fractionally integrated asymmetric power ARCH–DECO–generalized autoregressive conditional heteroskedasticity (GARCH) model and the spillover index technique.

The empirical analysis uses the dynamic equicorrelation model of Engle and Kelly (2012) to examine time-varying correlations at equilibrium. The authors further analyze dynamic volatility transmission among energy futures by using Diebold and Yilmaz (2012) dynamic spillover index based on generalized value-at-risk framework.

The empirical results provide evidence of heightened equicorrelations at times of financial turmoil. More specifically, the dynamic spillover analysis shows that volatility is transmitted predominantly from crude oil to the other markets and risk transfer among four markets exhibits asymmetries. Spillovers are found to be highly responsive to dramatic events such as the 9/11 terror attack, 2008–2009 global financial crisis and 2014–2016 oil glut.

The results of this study have important practical implications for investors, portfolio managers and energy policymakers as the presence of time-varying co-movements and spillovers suggests the need for dynamic trading strategies. There are also implications regarding risk management practices, as there is evidence of increased volatility transmission at times of financial turmoil and uncertainty. Finally, the results provide insights to policymakers in a better understanding of the spillover dynamics.

This paper investigates the DECOs and spillover effects among crude oil, natural gas, heating oil and gasoline futures markets. To the best of the knowledge, this is one of a few studies that examine co-movements and risk transfer in energy futures in a comprehensive framework.

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.

This article examines dynamic volatility spillovers between stock index returns of four main hospitality sub-sectors in US during the coronavirus disease 2019 (COVID-19) pandemic. These are tourism and travel, hotel and lodging, recreational services and food and beverages. Volatility spillovers are explicitly used as accurate and informative proxies for risk contagion between sectors during turbulent times.

The authors employ dynamic conditional correlation-generalized autoregression heteroskedasticity (DCC-GARCH) and wavelet coherence analysis (WCA) to analyze the phenomenon. The authors’ timeframe is divided into three main sub-periods, namely the pre-pandemic, the first wave and the second wave periods.

This study’s results reveal immense negative shocks in returns of all four sub-sectors on the Black Monday (8th March 2020). Moreover, high volatility persistence was observed during both waves with an exception of tourism and travel which exhibited lower volatility persistence during the second wave. The authors discovered magnified contagion effects between tourism and travel, hotel and lodgment and recreational services during the first wave of the pandemic with tourism and travel being the main volatility transmitter. Lower magnitudes of spillovers were observed between food and beverages and other sub-sectors with a decoupling effect being evident during the second wave.

This study’s findings contribute to the contagion theory by providing evidence of disproportional volatility spillover among hospitality sub-sectors despite being exposed to similar turbulent economic conditions.

Crucial implications can be drawn from this study’s findings to assist in risk management, asset valuation and portfolio management. The importance of close monitoring, safety measures, international diversification and adequacy of liquid assets during health crises cannot be stresses enough for hospitality firms. Retail investors, speculators and asset managers can take advantage of this study’s findings to design trading strategies and hedge against risk.

A body of knowledge pertaining to effects of crises such as COVID-19 on hospitality stocks has been proliferating. Nonetheless, there is still a relative dearth of empirical literature on volatility spillover between hospitality sub-sectors especially during periods of rising economic uncertainties.

This paper investigates the static and dynamic directional return spillovers and dependence among green investments, carbon markets, financial markets and commodity markets from January 2013 to September 2020.

This study employed both the quantile vector autoregression (QVAR) and time-varying parameter VAR (TVP-VAR) technique to examine the magnitude of static and dynamic directional spillovers and dependence of markets.

Results show that the magnitude of connectedness is extremely higher at quantile levels (q = 0.05 and q = 0.95) compared to those in the mean of the conditional distribution. This connotes that connectedness between green bonds and other assets increases with shock size for both negative and positive shocks. This further indicates that return shocks spread at a higher magnitude during extreme market conditions relative to normal periods. Additional analyses show the behavior of return transmission between green bond and other assets is asymmetric.

The findings of this study offer significant implications for portfolio investors, policymakers, regulatory authorities and investment community in terms of carefully assessing the unique characteristics offered by each markets in terms of return spillovers and dependence and diversifying the portfolios.

The study, first, uses a relatively new statistical technique, the QVAR advanced by Ando et al. (2018), to capture upper and lower tails’ quantile price connectedness and directional spillover. Therefore, the results possess adequate power against departure from mean-based conditional connectedness. Second, using a portfolio of green investments, carbon markets, financial markets and commodity markets, the uniqueness of this study lies in the examination of the static and dynamic dependence of the markets examined.

Internationalization 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.

Ten 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.

The 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.

Researchers, practitioners, policymakers and investors may all benefit from the findings in areas including risk management, portfolio diversification and trading methods.

This study aims to investigate the relationship between stock markets, environmental, social and governance (ESG) factors and Shariah-compliant in an integrated framework.

The authors employ the multivariate factor stochastic volatility (mvFSV) framework to extract the volatility of the different sectoral indices. Based on this evidence, the authors employ the quantile vector autoregressive (QVAR) approach to examine the dynamic spillover connectedness among the aforementioned indices.

The study emphasizes the following major findings: (1) significant time-varying spillover connectedness across quantiles, (2) bidirectional and asymmetric spillover effect among the ESG index and the other sectoral indices, (3) the strength of spillover connectedness is time-varying across quantiles, (4) based on the perspective of portfolio optimization, ESG market is a significant strong forecasting contributor to conventional and Shariah-compliant markets, (5) overall, the findings point out serious quantile pass-through effect among ESG index and the other sectoral indices during the COVID-19 health crisis.

This study extends the previous literature in the following ways. First, to the best of the researchers’ knowledge, none of the existing studies have investigated the relationship between stock markets, ESG factors and Shariah-compliant in an integrated framework. Second, this study extends the previous scholarships by applying the mvFSV. Third, the authors propose a new rolling version to estimate dynamic spillovers, namely the rolling-window quantile VAR method. This approach provides a great advantage in computing the dynamics of return and variance spillover between variables in terms not only of the overall factor but also of the net (pairwise) aspect.

Introduction – The rapid flow of information between the markets eliminates the possibility of diversifying the portfolio by bringing the markets closer, and may cause the volatility in a market to spread to another market. In this context, revealing the relationships between conventional and participation markets or financial assets is important in terms of portfolio diversification and risk management.

Purpose – The major aim of this work is to analyse the existence of volatility spillover between conventional stock index and participation index based on the indexes in Turkish Capital Markets. BIST-30 and Katılım-30 indexes are used as the representatives of conventional stock index and participation index, respectively.

Methodology – Firstly, the univariate HYGARCH (1,d,1) parameters are calculated, and secondly, the dynamic equicorrelation (DECO) methodology is applied. DECO model is proposed to simplify structural assumptions by introducing a structure in which all twosomes of returns take the same correlation for a given time period. In this way, DECO model enables to have an optimal portfolio selection in comparison to an unrestricted time varying-dynamic correlation approaches and gives more advanced forecasting ability for the duration of the financial crisis periods compared to the various portfolios.

Findings – There is a strong correlation between BIST-30 and Katılım-30. They are affected by the same shocks. We expect to see different investor behaviours for Katılım-30 and BIST-30. However, they seem to have almost the same investor profile. In addition, there is a causality in both ways and volatility spillover between them.

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.

The purpose of this paper is to investigate volatility spillovers across the interest rate swap markets of the G7 economies, and then the authors investigate whether spillovers of swap markets contain useful information to explain subsequent stock price movements.

This study uses the short- and long-term swap spread volatility of the G7 countries to explore the spillover effects of international swap markets, and then investigates the relationship between swap and stock markets. The authors use the generalized VAR approach suggested by Diebold and Yilmaz (2012) to study spillovers of international swap markets. The Granger-causality tests are employed to examine the linkage of interest rate swap and stock markets.

This paper shows that a moderate spillover effect exists for the short- and long-term swap markets. Moreover, the results show that the short- and long-term swap markets of France and Germany have a larger impact on other countries’ swap markets than that of other countries’ swap markets on the French and German swap markets. Finally, the results indicate that the total volatility spillovers for the long-term swap markets have a larger influence on the total volatility spillover index of stock markets and the global stock market volatility than that of the short-term swap markets.

Prior literature has used impulse response and variance decomposition analyses to investigate international swap markets linkages. However, the results depend on the ordering of variables. This study uses the framework of Diebold and Yilmaz (2012) to overcome the ordering issue, and thus the authors can compute directional spillovers. This paper is the first study to explore the linkage of the total volatility spillover of swap markets and the stock markets.

Focusing on the spillover effects between the CSI 500 stock index futures market and its underlying spot market during April to September 2015, the purpose of this paper is to explore whether Chinese stock index futures should be responsible for the 2015 stock market crash.

Using both linear and non-linear econometric models, this paper empirically examines the mean spillover and the volatility spillover between the CSI 500 stock index futures market and the underlying spot market.

The results showed the following: the CSI 500 stock index futures market has significant one-way mean spillover effect on its spot market. The volatility in CSI 500 stock index futures market also has a significant positive spillover effect on its spot stock market, and the mean value of dynamic correlation coefficient between the two market volatility is 0.4848. The spillover effect of the CSI 500 stock index futures market on the underlying spot market is significantly asymmetric, characterized by relatively moderate and slow during the period of the markets rising, yet violent and rapid during the period of the markets falling. The findings suggest that although the stock index futures itself was not the “culprit” of Chinese stock market crash in 2015, its existence indeed accelerated and exacerbated the stock market’s decline under the imperfect trading system.

Different from the existing literature mainly focusing on CSI 300 stock index futures, this paper empirically examines the impact of the introduction of CSI 500 stock index futures on 2015 Chinese stock market crash for the first time.