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This paper aims to examine the dynamic return and volatility connectedness for six major industrial metals (tin, lead, nickel, zinc, copper and aluminium) and the coronavirus media coverage index (MCI).

To that purpose, this study applies the fresh time-varying parameter vector autoregression methodology (TVP–VAR model) during the sample period between 2 January, 2020, and 16 April, 2021, that is, covering the three waves of the COVID-19 pandemic crisis.

This study’s results show interesting findings. First, dynamic total return and volatility connectedness changes over time, highlighting a significant increase during the third wave of the pandemic. Second, the MCI index is a leading net transmitter in terms of return and volatility at the introduction of the SARS-CoV-2 coronavirus crisis. Third, this study clearly distinguishes two profiles among industrial metals: copper and tin/zinc as net transmitters and lead and aluminium as net receivers. Finally, the most relevant differences between them are concentrated not only at the beginning of the COVID-19 pandemic (first wave) but also during the second and third waves of the coronavirus outbreak.

To the best of the authors’ knowledge, this is the first research that explores the dynamic return and volatility connectedness in the industrial metal market, applying the TVP–VAR methodology during the first waves of the COVID-19 pandemic crisis.

This paper examines the time-varying return connectedness between renewable energy, oil, precious metals, the Gulf Council Cooperation region and the United States stock markets during two successive crises: the pandemic Covid-19 and the 2022 Russo-Ukrainian war. The main objective is to investigate the effect of the Covid-19 pandemic and the Russo-Ukrainian war on the connectedness between the considered stock markets.

This paper uses the time-varying parameter vector autoregression approach, which represents an extension of the Spillover approach (Diebold and Yilmaz, 2009, 2012, 2014), to examine the time-varying connectedness among stock markets.

This paper reflects the effect of the two crises on the stock markets in terms of shock transmission degree. We find that the United States and renewable energy stock markets are the main net emitters of shocks during the global period and not just during the two considered crises sub-periods. Oil stock market is both an emitter and a receiver of shocks against Gulf Council Cooperation region and United States markets during the full sample period, which may be due to price fluctuation especially during the two crises sub-periods, which suggests that the future is for renewable energy.

This paper examines the effect of the two recent and successive crises, the Covid-19 pandemic and the 2022 Russo-Ukrainian war, on the connectedness among traditional stock markets (the United States and Gulf Council Cooperation region) and commodities stock markets (renewable energy, oil and precious metals).

The purpose of this paper is to revisit the dynamic linkages between the US and the national securitized real estate markets of each of the nine Asian-Pacific (APAC) economies in time-frequency domain.

Wavelet decomposition via multi-resolution analysis is employed as an empirical methodology to consider time-scale issue in studying the dynamic changes of the US–APAC cross-real estate interdependence.

The strength and direction of return correlation, return exogeneity, shock impulse response, market connectivity and causality interactions change when specific time-scales are involved. The US market correlates with the APAC markets weakly or moderately in the three investment horizons with increasing strength of lead-lag interdependence in the long-run. Moreover, there are shifts in the net total directional volatility connectivity effects at the five scales among the markets.

Given the focus of the five approaches and associated indicators, the picture that emerges from the empirical results may not completely uniform. However, long-term investors and financial institutions should evaluate the time-scale based dynamics to derive a well-informed portfolio decision.

Future research is needed to ascertain whether the time-frequency findings can be generalizable to the regional and global context. Additional studies are required to identify the factors that contribute to the changes in the global and regional connectivity across the markets over the three investment horizons.

This study has successfully decomposed the various market linkage indicators into scale-dependent sub-components. As such, market integration in the Asia-Pacific real estate markets is a “multi-scale” phenomenon.

The purpose of this study is to investigate the dynamic return volatility connectedness among S&P, Dow Jones (DJ) sustainability indices and their conventional counterparts.

This study uses time-series daily data for 10 S&P and DJ indices over the period of December 1, 2012 to December 8, 2021. The authors divide the data into three periods; over the whole sample, pre and during the Covid-19 pandemic. The study adopts the connectedness approach developed by Diebold and Yilmaz (2014).

The results reveal a high degree of connectedness between S&P and DJ indices and their relative sustainability indices over the whole sample, pre and during the Covid-19 pandemic, indicating that the sustainability indices converge toward their conventional peers. The results further show that the conventional S&P500, S&P Euro 50 and DJWI are the main transmitters of shocks, whereas the S&P400, S&P500 and S&P50 sustainability indices are the main receivers of shocks.

The study provides novel insights in terms of shock transmission of S&P and DJ sustainability indices and their conventional counterparts, where there is a lack of investigation of the connectedness between indices in this field.

The study has significant implications for investors and portfolio managers to devise portfolio strategies to minimize risk and trace the cause, the direction and the magnitude of risk transmission among different indices. Also, the results help policymakers to manage diverse types of risks associated with S&P and DJ indices. Finally, faith-based and ethical investors would be able to predict the pairwise spillover connectedness between these indices.

The purpose of this paper is to extend the literature on the spillovers across economic policy uncertainty (EPU) and cryptocurrency uncertainty indices.

This paper uses cross-country economic policy uncertainty indices and the novel data measuring the cryptocurrency price uncertainties over the period 2013–2021 to construct a sample of 946 observations and applies the time-varying parameter vector autoregression (TVP-VAR) model to do an empirical study.

The findings suggest that there are cross-country spillovers of economic policy uncertainty. In addition, the total uncertainty spillover between economic policies and cryptocurrency peaked in 2015 before gradually decreasing in the following periods. Concomitantly, the cryptocurrency uncertainty has acted as the “receiver.” More importantly, the authors found the predictive power of economic policy uncertainty to predict the cryptocurrency uncertainty index. This paper’s results hold robust when using alternative measurement of cryptocurrency policy uncertainty.

This study is the first research that deeply investigates the association between two uncertainty indicators, namely economic policy uncertainty and the cryptocurrency uncertainty index. We provide fresh evidence about the dynamic connectedness between country-level economic policy uncertainty and the cryptocurrency index. Our work contributes a new channel driving the variants of uncertainties in the cryptocurrency market.

The increasing globalization and technological advancements have increased the information spillover on stock markets from various variables. However, there is a dearth of a comprehensive review of how stock market volatility is influenced by macro and firm-level factors. Therefore, this study aims to fill this gap by systematically reviewing the major factors impacting stock market volatility.

This study uses a combination of bibliometric and systematic literature review techniques. A data set of 54 articles published in quality journals from the Australian Business Deans Council (ABDC) list is gathered from the Scopus database. This data set is used to determine the leading contributors and contributions. The content analysis of these articles sheds light on the factors influencing market volatility and the potential research directions in this subject area.

The findings show that researchers in this sector are becoming more interested in studying the association of stock markets with “cryptocurrencies” and “bitcoin” during “COVID-19.” The outcomes of this study indicate that most studies found oil prices, policy uncertainty and investor sentiments have a significant impact on market volatility. However, there were mixed results on the impact of institutional flows and algorithmic trading on stock volatility, and a consensus cannot be established. This study also identifies the gaps and paves the way for future research in this subject area.

This paper fills the gap in the existing literature by comprehensively reviewing the articles on major factors impacting stock market volatility highlighting the theoretical relationship and empirical results.

Guar Gum (GG) is used in Shale oil exploration. Excessive price increase in the Guar futures market had a spillover impact on Guar spot prices and affected Guar export from India as Shale oil producers started exploring alternate sources. In this paper, the role of excessive speculation in the futures market, and its adverse impact on the guar-based agri-business ecosystem have been empirically explored.

Volatility spillover dynamics between WTI crude oil and Guar futures have been explored using bivariate-Granger Causality, BEKK–GARCH models with Wavelet multi-resolution analysis. The wavelet-based models capture the multi-scale features of mean and volatility spillover to identify the effect of heterogenous investment behavior in the time and frequency domain.

The results provide evidence that excessive speculation in futures markets increases spot market volatility. The results also suggest that the excess presence of short-term investors can destabilize the futures market.

The purpose of the commodity futures market is to support price discovery and risk management. However, speculative practices can destabilize these purposes leading to the failure of the business ecosystem.

The novelty of this paper is twofold. First, it explores the economic linkages between the spot and futures market and tests whether the presence of heterogeneous traders affects the economic linkages. Second, it models the impact of short-term speculative investment on the destabilization of the spot market.

With growing interdependence between financial markets, the goal of this paper is to examine the dynamic interdependence between corporate equity and public real estate markets for the USA and a select group of seven European developed economies under a cross-country framework in crisis and boom market conditions. Dynamic interdependence is related to four measures of market linkages of “correlation, spillover, connectedness and causality”.

This study adopts a four-step investigation. The authors first estimate “time-varying variance–covariance spillovers and implied correlations” modeled with the bivariate BEKK-MGARCH methods. Second, the methods of Diebold and Yilmaz (2012, 2014) measure the conditional volatility spillover-connectedness effects across the corporate equity and public real estate markets based on a decomposition of the forecast error variance. Third, the authors implement nonlinear bivariate and multivariate causality tests to understand the lead-lag dynamics of the two asset markets' returns, volatilities and net directional volatility connectedness across different sample periods. Finally, the authors conclude the study by providing a portfolio hedging analysis.

The authors find that corporate equity and public real estate are moderately interdependent to the extent that their diversification benefits increases in the longer term. Moreover, the authors find increased corporate equity-public real estate causal dependence of the market groups of the European and international portfolios during the GFC and INTERCRISIS periods. The nonlinear causality test findings indicate that the joint information of asset markets can be a useful source of prediction for future innovation of market risks. Additionally, policy makers may also be able to employ conditional volatility and volatility connectedness as two other measures to manage market stability in the cross-asset market dependence during highly volatile periods.

One major take away from this academic research is since international portfolio investors are not only concerned the long-term price relationship but also the correlation structure and volatility spillover-connectedness, the conditional BEKK modeling, generalized risk connectedness analysis and nonlinear causal dependence explorations from this multi-country study can shed fresh light on the nature of market interdependence and magnitude of volatility connectedness effects in a multi-portfolio framework.

The hedging performance analysis for portfolio diversification and risk management indicates that industrial stocks (“pure” equities) are valuable assets that can improve the hedging performance of a well-diversified corporate equity-public real estate portfolio during crisis periods. For policymakers, the findings provide important information about the nature of causal links and predictability during the crisis and asset-market boom periods. They can then equip with this information to manage and coordinate market stability in cross corporate equity-real estate relationships effectively.

Although traditional research has in general reported at least a moderate degree of relationship between the two asset markets, investors' knowledge of stock-public real estate market linkage is somewhat inadequate and confine mostly to broad stocks (i.e. stocks that are exposed to public real estate influence) in a single-country context. In this paper, the authors examine the interdependence dynamics in a multi-country (multi-portfolio) context. A clear understanding their changing market relationships in a multi-country context is of crucial importance for portfolio investors, financial institutions and policy makers. Moreover, since the authors use an orthogonal stock market index, the authors allow global investors to understand the potential diversification benefits from stock markets that are beyond the public real estate market under different market conditions.

Recent trends and developments in Bitcoin have led to a proliferation of studies that analyzed the Bitcoin returns and volatility; however, the volatility connectedness between Bitcoin and equity market information in emerging countries quietly remains scarce. Regarding this deficiency, the purpose of this paper is to examine the dynamic connectedness between Bitcoin and equity market information.

Daily data from January 1, 2012 to May 31, 2018 are used. The paper applies a novel time-varying parameter vector autoregression (TVP-VAR) model extended by Antonakakis and Gabauer (2017). This model addresses the biases in coefficient estimates, considering innovations from sources of time variation.

The findings reveal that the volatility transmission of Bitcoin return is not an important source of shocks of market returns in Brazil, Russia, India, China and South Africa (BRICS), suggesting that Bitcoin return contributes less volatility to equity market information. The results further show that Bitcoin is the main receiver of volatility while market price risk is the dominant transmission catalysts for innovations in the rest of the stock market returns.

Important implications can be derived from these findings, signaling of the demand to develop and implement volatility connectedness policy measures in order to guarantee the stability of financial assets. However, the most significant limitation lies in the fact that the analysis of this paper is restricted to the volatility connectedness between Bitcoin and equity market information in BRICS countries.

By acknowledging the wide range of econometric models, the paper uses TVP-VAR model because this methodology is a useful and relevant tool in modeling the volatility connectedness of financial variables, thus providing meaningful information to policy makers and international investors.