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This paper aims to examine the dynamic conditional correlation (DCC) and hedging ratios between Dow Jones markets and oil, gold and bitcoin. Using daily data, including the COVID-19 pandemic and the Russia–Ukraine war. We employ the DCC-generalized autoregressive conditional heteroskedasticity (GARCH) and asymmetric DCC (ADCC)-GARCH models.

DCC-GARCH and ADCC-GARCH models.

The most of DCCs among market pairs are positive during COVID-19 period, implying the existence of volatility spillovers (Contagion-effects). This implies the lack of additional economic gains of diversification. So, COVID-19 represents a systematic risk that resists diversification. However, during the Russia–Ukraine war the DCCs are negative for most pairs that include Oil and Gold, implying investors may benefit from portfolio-diversification. Our hedging analysis carries significant implications for investors seeking higher returns while hedging their Dow Jones portfolios: keeping their portfolios unhedged is better than hedging them. This is because Islamic stocks have the ability to mitigate risks.

Our paper may make a valuable contribution to the existing literature by examining the hedging of financial assets, including both conventional and Islamic assets, during periods of stability and crisis, such as the COVID-19 pandemic and the Russia–Ukraine war.

This paper aims to investigate the relationship between sustainable investments and a series of uncertainties from January 2014 to December 2021, including many economic and political turbulences and the COVID-19 pandemic.

The authors use Rényi’s transfer entropy method, a nonparametric flexible tool that considers both the center distribution and lower quantiles, capturing extreme rare events that give additional insights to analysis.

The authors’ results indicate significant bidirectional information transmissions between the crude oil volatility and sustainability indices. The authors report information flows between the cryptocurrency uncertainty and sustainability indices considering tail events. The results are essential for market participants making decisions during turbulent times.

This paper is carried out for a variety of uncertainty measures and environmental, social and governance (ESG) portfolios of both developed and developing markets. It adds to literature in terms of methodology used. Rényi’s transfer entropy methodology is first used to measure the relationship between uncertainties and ESG investments.

This study analyses the impact of economic and trade policy uncertainty on US and Chinese stock markets. Also, this study examines the hedge and safe-haven properties of US and China stocks against both US and Chinese economic and trade policy uncertainty.

To achieve the desired goals, the authors employ Dynamic Conditional Correlation through Glosten et al. (1993) model based on the Generalized Autoregressive Conditional Heteroscedasticity (DCC-GJR-GARCH (1, 1)) and Quantile cross-spectral (QS) models. The study uses monthly observations spanning from March 2010 to June 2022.

This study evidence that the economic and trade policy uncertainty between USA and China is extremely sensitive and has high volatility clustering effects on DJChina88 and DJUS, respectively. Conversely, against the Chinese economic and trade policy uncertainty, the US stock market indexes show both hedging properties across the period and safe-haven during COVID-19 and Russia–Ukraine crises. In contrast, among the Chinese stock markets, only DJShenzhen and DJShanghai stock indices might provide strong hedging and safe-haven properties against the US economic and trade policy uncertainties; however, DJShenzhen (DJChina88) stock shows weak hedge and safe-haven properties (hedging benefits) against Chinese trade policy uncertainty (CTPU) (Chinese economic policy uncertainty [CEPU]).

The findings have significant implications for investors, portfolio managers and regulators in hedging and making proper decisions under uncertain circumstances.

The study extends the literature on stock market performance to cover the economic and trade policy uncertainty by providing novel evidence during the recent COVID-19 and Russia–Ukraine invasion.

This study aims to investigate the determinants of Islamic stock returns listed on Jakarta Islamic Index (Indonesia) between 2008 and 2018.

This study uses a quantile bounded autoregressive distributed lag (QBARDL) model to uncover relevant relationships.

This study finds that the Dow Jones Islamic Market Index, gold returns, world oil prices and exchange rates are the determinants of the Indonesia’s Islamic stock returns. However, the relationship is time varying developing intra-/inter-quantile bounded.

Integration of the Islamic stock returns with the real economic indicators changes over time. The findings have important implications for the policymakers, the fund managers and the investors to anticipate consequences when considering the macroeconomic conditions before participating in the Indonesian Islamic stock market.

Using a QBARDL, this study finds that the Islamic stock returns have on net and “time-varying intra-/inter-quantile developing” relationship with its determinants as data quantiles progressed from 25% to 75%.

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 examine the dynamic spillover effects and network connectedness between the oil prices and the Islamic and conventional financial markets in the Gulf Cooperation Council countries. The focus is on network connectedness during the 2008–2009 global financial crisis, the 2014–2016 oil crisis and the COVID-19 pandemic. The authors use daily data covering the period from January 1, 2007 to April 14, 2022.

This study applies a spillover analysis and connectedness network to investigate the risk contagion among the Islamic and conventional stock–bond markets. The authors rely on Diebold and Yilmaz’s (2012, 2014) methodology to construct network-associated measures.

The results suggest that overall connectedness among financial market uncertainties increased during the global financial crisis, the oil price collapse of 2014–2016 and the COVID-19 crisis. In addition, the authors show that the contribution of oil shocks to the financial system is limited, as the oil market was a net receiver during the 2014 oil shock and the COVID-19 crisis. On the other hand, the Islamic and conventional stock markets are extensive sources of network effects on the oil market and Islamic and conventional bond markets. Furthermore, the authors found that the Sukuk market was significantly affected by the COVID-19 pandemic, whereas the conventional and Islamic stock markets were the highest transmitters of shocks during the COVID-19 pandemic outbreak. Moreover, oil revealed a weak connectedness with the Islamic and conventional stock markets during the COVID-19 health crisis, implying that it helps provide diversification benefits for international portfolio investors.

This study contributes to this field by improving the understanding of the effect of fluctuations in oil prices on the dynamics of the volatility connection between oil and Islamic and conventional financial markets during times of stress through a network connectedness framework. The main results of this study highlight the role of oil in portfolio allocation and risk minimization when investing in Islamic and conventional assets.

This study aims to endeavour to decode artificial intelligence (AI)-based tokens' complex dynamics and predictability using a comprehensive multivariate framework that integrates technical and macroeconomic indicators.

In this study we used advance machine learning techniques, such as gradient boosting regression (GBR), random forest (RF) and notably long short-term memory (LSTM) networks, this research provides a nuanced understanding of the factors driving the performance of AI tokens. The study’s comparative analysis highlights the superior predictive capabilities of LSTM models, as evidenced by their performance across various AI digital tokens such as AGIX-singularity-NET, Cortex and numeraire NMR.

This study finding shows that through an intricate exploration of feature importance and the impact of speculative behaviour, the research elucidates the long-term patterns and resilience of AI-based tokens against economic shifts. The SHapley Additive exPlanations (SHAP) analysis results show that technical and some macroeconomic factors play a dominant role in price production. It also examines the potential of these models for strategic investment and hedging, underscoring their relevance in an increasingly digital economy.

According to our knowledge, the absence of AI research frameworks for forecasting and modelling current aria-leading AI tokens is apparent. Due to a lack of study on understanding the relationship between the AI token market and other factors, forecasting is outstandingly demanding. This study provides a robust predictive framework to accurately identify the changing trends of AI tokens within a multivariate context and fill the gaps in existing research. We can investigate detailed predictive analytics with the help of modern AI algorithms and correct model interpretation to elaborate on the behaviour patterns of developing decentralised digital AI-based token prices.

This research paper aims to explore asymmetric market efficiency of the 13 Euro countries, i.e. Austria, Belgium, Finland, France, Germany, Greece, Ireland, Italy, Netherland, Portugal, Slovakia, Slovenia and Spain, concerning the period before global financial crisis (GFC), after GFC and period of COVID-19 pandemic.

Multifractal detrended fluctuation analysis (MF-DFA) is applied to examine the persistence and anti-persistency. It also discusses the random walk behavior hypothesis of these 13 countries non-stationary time series. Additionally, generalized Hurst exponents are applied to estimate the relative efficiency between short- and long-run horizons and small and large fluctuations.

The current study results suggest that most countries' markets are multifractal and exhibit long-term persistence in the short and long run. Moreover, the results with respect to full sample confirm that Portugal is the most efficient country in short run and Austria is the least efficient country. However, in long run, Austria appeared to be highly efficient, and Slovakia is the least efficient. In the pre-GFC period, Greece is said to be the relatively most efficient market in the short run, whereas Austria is the most efficient market in the long run. In the case of Post-GFC, Netherland and Ireland are the most efficient markets in short and long run, respectively. Lastly, COVID-19 results indicate that Finland's stock market is the most efficient in short run. Whereas, in the long run, the high efficiency is illustrated by Germany. In contrast, the most affected stock market due to COVID-19 is Belgium.

This study will add value to the present knowledge on efficient market hypothesis (EMH) with the MF-DFA approach. Also, with the MF-DFA approach, potential investors will be capable of ranking the stock markets of Eurozone countries based on their efficiency in the period before and after GFC and then specifically in the period of COVID-19.

本研究旨在探討13個歐元區國家在環球金融危機前後, 以及2019新型冠狀病毒病肆虐時期之不對稱市場效率; 這13個國家包括: 奧地利、比利時、芬蘭、法國、德國、希臘、愛爾蘭、義大利、荷蘭、葡萄牙、斯洛伐克、斯洛維尼亞和西班牙。

研究人員使用多重分形去趨勢波動分析法、來探討持續性與反持續性。這分析法也用來討論正在研究中的13個國家的非平穩時間序列的隨機漫步假說; 而且, 廣義赫斯特指數被用來估算長期/短期投資與大/小波動之間的相對效率。

研究結果間接表明了大部份國家的市場都是多重分形的; 而且, 它們無論以短期抑或以長期來審視觀察, 均能展示持久性。再者, 就整體樣本而言, 研究結果確認了在短期來看, 葡萄牙是效率最高的國家, 而奧地利則效率最低。唯以長期來審視觀察, 奧地利則似乎效率很高, 而效率最低的則是斯洛伐克。在環球金融危機爆發前, 就短期而言, 希臘被認為是相對效率最高的市場, 而長期而言, 效率最高的則是奧地利。至於在環球金融危機爆發後, 就短期而言, 荷蘭是效率最高的市場, 而就長期而言, 效率最高的則是愛爾蘭。最後, 2019新型冠狀病毒病的結果顯示, 就短期而言, 荷蘭的股票市場是效率最高的, 而長期而言, 德國則展示了其高效率性。而受疫情影響最大的股票市場則是比利時。

研究採用了多重分形去趨勢波動分析法、來探討股票市場的效率, 並以此分析法來討論有關國家的非平穩時間序列的隨機漫步假說, 這使我們對效率市場假說有進一步的認識; 就此而言, 本研究為有關的探討增添價值; 而且, 有意投資者在使用多重分形去趨勢波動分析法下, 能夠基於歐元區國家的股票市場在環球金融危機前後, 以及更明確地在2019新型冠狀病毒病肆虐時期的效率, 來把這些股票市場分等級。

環球金融危機、2019新型冠狀病毒病、效率市場假說、多重分形去趨勢波動分析.

International Journal of Emerging Markets (IJoEM) is a leading journal that publishes high-quality research focused on emerging markets. In 2020, IJoEM celebrated its fifteenth anniversary, and the objective of this paper is to conduct a retrospective analysis to commensurate IJoEM's milestone.

Data used in this study were extracted using the Scopus database. Bibliometric analysis, using several indicators, is adopted to reveal the major trends and themes of a journal. Mapping of bibliographic data is carried using VOSviewer.

Study findings indicate that IJoEM has been growing for publications and citations since its inception. Four significant research directions emerged, i.e. consumer behaviour, financial markets, financial institutions and corporate governance and strategic dimensions based on cluster analysis of IJoEM's publications. The identified future research directions are focused on emergent investments opportunities, trends in behavioural finance, emerging role technology-financial companies, changing trends in corporate governance and the rising importance of strategic management in emerging markets.

To the best of the authors' knowledge, this is the first study to conduct a comprehensive bibliometric analysis of IJoEM. The study presents the key themes and trends emerging from a leading journal considered a high-quality research journal for research on emerging markets by academicians, scholars and practitioners.

The purpose of this paper is to perform Shariah review of Brownian motion that is used for prediction of Islamic stock prices and their volatility.

It uses the Shariah compliant development model guidelines to review the Brownian motion and its applications.

The model of Brownian motion does not involve any variable that renders it non-Shariah compliant; neither all applications of Brownian motion are Shariah compliant. Because the model is based on stochastic properties that involve randomness, therefore the issue of gharar takes the utmost important to handle in the applications of the model. The results need to be analyzed strictly in accordance with the Shariah whether they create any element of gharar or uncertainty in case of expected price and volatility estimates.

The research suffers from the limitation that it analyses only one model of physics, i.e. Brownian motion model from Shariah perspective.

The research opens an area for Shariah analysis of results generated from the application of advanced models of physics on matters related to Islamic financial markets.

The originality of this study stems from the fact that to the best of the authors’ knowledge, it is the first study that extends Shariah guidelines into Financial physics for making the foundations of Islamic econophysics.