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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 study comprehensively aims to review the key influential and intellectual aspects of spillovers between Islamic and conventional financial markets.

The study uses the bibliometric and content analysis methods using the VOSviewer software to analyse 52 academic documents derived from the Web of Sciences (WoS) between 2015 and June 2022.

The results demonstrate the influential aspects of spillovers between Islamic and conventional financial markets, including the leading authors, journals, countries and institutions and the intellectual aspects of literature. These aspects are synthesised into four main streams: research between stock indexes; studies between stock indexes, oil and precious metal; works between Sukuk, bond and indexes; and empirical studies review. The authors also propose future research directions in spillovers between Islamic and conventional financial markets.

Our study is subject to several limitations. Firstly, the authors only used the WoS database. Secondly, the study only includes papers and reviews written in English from the WoS. This study assists academic scholars, practitioners and regulatory bodies in further exploring the suggested issues in future studies and improving and predicting economic and financial stability.

To the best of the authors’ knowledge, no extant empirical studies have been conducted in this area of research interest.

This paper aims to explore the impact of the coronavirus on the volatility spillovers of 10 selected developed markets hit by this pandemic (e.g. the USA, Canada, Korea, Japan, the UK, Germany, Italy, Spain, France and China).

The database consists of daily data from January 1, 2020, to December 31, 2022. The data used are the precise daily closing prices of various indices of selected markets gathered from the DataStream and Investing.com databases. The authors use the VAR model to study the transmission of volatility between stock markets and analyze the dynamic links between them. Then, the Granger causality test is used to study the volatility movements and determine which of these markets is likely to influence the others. Then, impulse response functions are used to understand the reactions of the studied markets following shocks in the two most important markets, namely, the American and Chinese markets. Finally, forecast errors variance decomposition is used to measure the dynamic interactions that characterize the relationships between the studied markets.

Empirical results reveal instability in the returns of various indexes and the existence of causal relationships between standardized volatility of markets. The reactions of some markets following a shock in American and Chinese markets differ among markets. The empirical results also show that forecast errors variance of some markets begin coming from their own innovations during first periods. These shares decrease then in favor of other markets interventions.

The findings have significant practical implications for governments around the world as well as for financial investors. The successful practice of China’s pandemic prevention and control efforts may inspire governments to determine how to overcome panic and strengthen confidence in victory. Policymakers can use the insights from our study to design more effective economic policies and regulations to mitigate the negative impact of future pandemics on the financial system. Regulators can use these results to identify areas of weakness in the financial system and take proactive measures to address them. Financial investors may use the outcomes of our result to better understand the impact of global pandemics on financial markets. They may know which markets are the most active, which ones are causing considerable effects on the others and which ones show resilience and an anti-risk capacity. This may help them to make appropriate decisions about their investments.

It has become imperative to estimate the impact of this pandemic on the behavior of financial markets to prevent the deterioration and dysfunction of the global financial system. The findings have important implications for financial investors and governments who should know which markets are the most shaken, which cause remarkable effects on others and which show resilience and anti-risk capacity. Countries could follow China in some measures taken to moderate the negative effects of this epidemic on national economies.

The purpose of this study is to unfold the existing information channel in the higher moments of currency futures for different time horizons.

The authors use a quasi-Bayesian local likelihood approach within a time-varying parameter vector autoregression (TVP-VAR) framework and a dynamic connectedness measure to study the volatility, skewness and kurtosis of most traded currency futures.

The authors’ results suggest a time-varying presence of dynamic connectedness within higher moments of currency futures. Most spillovers pertain to shorter time horizons. The authors find that in net terms, CHF, EUR and JPY are the most important contributors to the system, while the authors emphasize that the role of being a transmitter or a receiver varies for pairwise interactions and time windows.

To the best of the authors’ knowledge, this is the first study that looks upon the connectivity vis-á-vis uncertainty, asymmetry and fat tails in currency futures within a dynamic Bayesian paradigm. The authors extend the current literature by proposing new insights into asset distributions.

In an era marked by heightened geopolitical uncertainties, such as international conflicts and economic instability, the dynamics of energy markets assume paramount importance. Our study delves into this complex backdrop, focusing on the intricate interplay the between traditional and emerging energy sectors.

This study analyzes the interconnections among green financial assets, renewable energy markets, the geopolitical risk index and cryptocurrency carbon emissions from December 19, 2017 to February 15, 2023. We investigate these relationships using a novel time-frequency connectedness approach and machine learning methodology.

Our findings reveal that green energy stocks, except the PBW, exhibit the highest net transmission of volatility, followed by COAL. In contrast, CARBON emerges as the primary net recipient of volatility, followed by fuel energy assets. The frequency decomposition results also indicate that the long-term components serve as the primary source of directional volatility spillover, suggesting that volatility transmission among green stocks and energy assets tends to occur over a more extended period. The SHapley additive exPlanations (SHAP) results show that the green and fuel energy markets are negatively connected with geopolitical risks (GPRs). The results obtained through the SHAP analysis confirm the novel time-varying parameter vector autoregressive (TVP-VAR) frequency connectedness findings. The CARBON and PBW markets consistently experience spillover shocks from other markets in short and long-term horizons. The role of crude oil as a receiver or transmitter of shocks varies over time.

Green financial assets and clean energy play significant roles in the financial markets and reduce geopolitical risk. Our study employs a time-frequency connectedness approach to assess the interconnections among four markets' families: fuel, renewable energy, green stocks and carbon markets. We utilize the novel TVP-VAR approach, which allows for flexibility and enables us to measure net pairwise connectedness in both short and long-term horizons.

This study aims to investigate the impact of the COVID-19 pandemic on the time-frequency connectedness between green bonds, stock markets and commodities (Brent and Gold), with a particular focus on China and its implication for portfolio diversification across different frequencies.

To this end, the authors implement the frequency connectedness approach of Barunik and Krehlik (2018), followed by the network connectedness before and during the COVID-19 outbreak. In particular, the authors implement more involvement in portfolio allocation and risk management by estimating hedge ratios and hedging effectiveness for green bonds and other financial assets.

The time-frequency domain spillover results show that gold is the net transmitter of shocks to green bonds in the long run, whereas green Bonds are the net recipients of shocks, irrespective of time horizons. The subsample analysis for the pandemic crisis period shows that green bonds dominate the network connectedness dynamic, mainly because it is strongly connected with the SP500 index and China (SSE). Thus, green bonds may serve as a potential diversifier asset at different time horizons. Likewise, the authors empirically confirm that green bonds have sizeable diversification benefits and hedges for investors towards stock markets and commodity stock pairs before and during the COVID-19 outbreak for both the short and long term. Gold only offers diversification gains in the long run, while Brent does not provide the desired diversification gains. Thus, the study highlights that green bonds are only an effective diversified.

This study contributes to the existing literature by improving the understanding of the interconnectedness and hedging opportunities in short- and long-term horizons between green bonds, commodities and equity markets during the COVID-19 pandemic shock, with a particular focus on China. This study's findings provide more implications regarding portfolio allocation and risk management by estimating hedge ratios and hedging effectiveness.

This paper aims to examine potential diversification benefits between Eurozone (i.e. EURO STOXX 50) and key Asia markets: HSI (Hong Kong), KOSPI (South Korea), NIKKEI 225 (Japan) and TSEC (Taiwan). The sample covers the period from 04-01-2008 to 19-10-2023 in daily frequency.

The empirical investigation is based on the wavelet coherence analysis, which is a localized correlation coefficient in the time and frequency domain.

The results provide evidence that long-term diversification benefits exist between EURO STOXX and NIKKEI, EURO STOXX and KOSPI (after 2015) and there are signs for the pair and EURO STOXX-TSEC (after 2014). During the short term, there are signs of diversification benefits during the sample period. However, during the medium term, the diversification benefits seem to diminish.

These results have crucial implications for investors regarding the benefits of international portfolio diversification.

This study aims to scrutinize time-varying return and volatility interlinkages among major cryptocurrencies, NFT tokens and DeFi assets between 1 July 2018 and 19 February 2023 and determine optimal portfolio allocations and hedging effectiveness under different portfolio construction techniques.

This work examines time-varying return and volatility interlinkages among major cryptocurrencies, NFT tokens, and DeFi assets between 1 July 2018 and 19 February 2023. To this end, the time-varying parameter-vector autoregression (TVP-VAR)-based connectedness methodology of Antonakakis et al. (2020) This approach is an extended version of the Diebold–Yilmaz (DY) method (Diebold and Yılmaz, 2014) and has advantages over the original DY. First, unlike the DY, it is free of the selection of a particular window size. Second, it has robustness for the outliers. Furthermore, following Broadstock et al. (2022), the author estimates time-varying optimal portfolio weights and hedging effectiveness under different portfolio construction scenarios.

This study's results indicate the following results: (1) The overall connectedness indices prominently capture well-known financial/geopolitical distress incidents; (2) the leading cryptocurrencies (ETH, BTC and BNB) are the largest transmitter of return shocks, while LINK and BTC are the largest transmitters/recipients of volatility shocks; (3) cryptocurrencies, NFTs and DeFi form distinct cluster groups in terms of return and volatility connectedness; (4) the connectedness networks estimated around the 2022 cryptocurrency crash and the FTX's filing for the bankruptcy are characterized by the strongest return and volatility interlinkages; (5) optimal portfolio strategies computed by different portfolio construction techniques display similar motifs and have sustained growth paths except for some short-lived drop backs.

This study's findings imply several policy suggestions for investors, stakeholders and policymakers. First, the study's time-based dynamic interlinkages can help market participants in their optimal portfolio decisions. In particular, the persistent net receiving roles of the DeFi assets and the NFTs throughout the episode, especially around the financial/geopolitical turmoil, underpin their safe haven potentials (Umar et al., 2022a, b). Finally, since the total connectedness indices (TCIs) are prone to significantly increase around financial/geopolitical burst times, these tools can be valuable for policy makers to monitor risk.

The contribution of knowledge is at least threefold. First, the author focuses on the dynamic time interlinkages among major cryptocurrencies, NFTs and DeFi assets in July 2018 and February 2023 considering the prominent recent financial/geopolitical incidents. Second, the author estimates network topologies of dynamic connectedness around financial/geopolitical bursts and compared them in terms of interlinkages. Finally, the author calculates the time-varying optimal portfolio allocations and hedging effectiveness under different portfolio construction techniques.

Investors aim for returns when investing in stocks, making return volatility a crucial concern. This study compares symmetric and asymmetric GARCH models to forecast volatility in emerging nations like the G4 countries. Accurate volatility forecasting is vital for investors to make well-informed investment decisions, forming the core purpose of this study.

From January 1993 to May 2021, the study spans four periods, focusing on the global economic crisis of 2008, the Russian crisis of 2015 and the COVID-19 pandemic. Standard generalized autoregressive conditional heteroscedasticity (GARCH), exponential GARCH (E-GARCH) and Glosten-Jagannathan-Runkle GARCH models were employed to analyse the data. Robustness was assessed using the Akaike information criterion, Schwarz information criterion and maximum log-likelihood criteria.

The study's findings show that the E-GARCH model is the best model for forecasting volatility in emerging nations. This is because the E-GARCH model is able to capture the asymmetric effects of positive and negative shocks on volatility.

This unique study compares symmetric and asymmetric GARCH models for forecasting volatility in emerging nations, a novel approach not explored in prior research. The insights gained can aid investors in constructing more effective risk-adjusted international portfolios, offering a better understanding of stock market volatility to inform strategic investment decisions.

This paper aims to investigate the role of price-based information from major cryptocurrencies, foreign exchange, equity markets and key commodities in predicting the next-minute Bitcoin (BTC) price. This study answers the following research questions: What is the best sparse regression model to predict the next-minute price of BTC? What are the key drivers of the BTC price in high-frequency trading?

Least absolute shrinkage and selection operator and Ridge regressions are adopted using minute-based open-high-low-close prices, volume and trade count for eight major cryptos, global stock market indices, foreign currency pairs, crude oil and gold price information for February 2020–March 2021. This study also examines whether there was any significant break and how the accuracy of the selected models was impacted.

Findings suggest that Ridge regression is the most effective model for predicting next-minute BTC prices based on BTC-related covariates such as BTC-open, BTC-high and BTC-low, with a moderate amount of regularization. While BTC-based covariates BTC-open and BTC-low were most significant in predicting BTC closing prices during stable periods, BTC-open and BTC-high were most important during volatile periods. Overall findings suggest that BTC’s price information is the most helpful to predict its next-minute closing price after considering various other asset classes’ price information.

To the best of the authors’ knowledge, this is the first paper to identify the covariates of major cryptocurrencies and predict the next-minute BTC crypto price, with a focus on both crypto-asset and cross-market information.