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This paper aims to identify and assess global risks in the supply chain performance.

First, global risks are identified and classified according to three criteria: content, probability and context. A set of supply chain performance indicators are then defined by the theory of resource-based view and balanced scorecard. Structural equation modeling is adopted to access risks in the global supply chain.

This article contributes to the supply chain risk management literature by providing a detailed operationalization of global supply chain risk constructs, e.g. natural disasters, war and terrorism, fire accidents, economic and political instability, social and cultural grievances, decease. Empirical results reveal that the supply chain is predominantly regarded as being vulnerable as the proposed model of risks can explain up to 12.6% variance of supplier performance, 25.2% innovation and learning, 23% internal business, 40.6% customer service and 32.4% finance.

These risks are relevant contextual variables in strategic supply chain decisions. Supply chain managers should keep in mind acceptable cost/benefit tradeoffs in their firms' mitigation efforts associated with major contingency risks. This research advocates the allocation of scarce resources to adopt the supply chain strategies of avoidance, speculative and postponement.

The application of the strategic content/process/context to explain global supply chain performance is an interesting approach. Moreover, globalization trends and the COVID-19 perspectives are considered to be the main reasons for increasing such complex factors. Data on validating research models collected during the COVID-19 pandemic reflect the topicality of this study.

This article aims to examine the simultaneous effect of risks on physical and intangible dimensions of supply chain performance under the globalization and Covid-19 perspectives.

The manipulation of literature reviews together with the combination of Q-sort and empirical data in the construction industry to identify and assess risks and supply chain performance, is a novel approach in the supply chain risk management area. The analysis of Structural Equation Modeling that is able to calculate the simultaneous impact of various risks on supply chain performance, is used to validate this relationship.

Global supply chains are currently facing interruptions caused by several sources of inherent uncertainties, e.g. natural disasters, war and terrorism, external legal issues, economic and political instability, social and cultural grievances, and diseases. The weaknesses of the current global supply chain have been revealed, resulting in delays, supply unfulfillment, labor shortages and demand fluctuation. These supply chain risks have a great on supply chain performance indicators, and the magnitude of their impact tends to increasingly impact in the context of globalization and the Covid-19 pandemic. Findings showed that the proposed risk models can be explained with Variance of supplier performance (25.5%), Innovation and learning (21.2%), Internal business (61.9%), Customer service (39.4%) and Finance (39.7%).

Supply chain managers should keep in mind acceptable cost/benefit trade-offs in corporate risk mitigation efforts associated with major contingency risks. In doing so, the proposed hypothesized model can be “a road map” to achieve this purpose. Our research favors the adoption of supply chain management strategies, e.g. postponement, speculation and avoidance.

The trend toward globalization and the emergence of the Covid-19 pandemic increasing supply chain complexity are regarded as key drivers of supply chain risk and therefore enhance vulnerability to supply chain.

The authors analyse the interactive influence of energy use, capital investment and finance on pollution in energy-dependent African countries.

The study analyses data from 5 selected energy-dependent African nations (i.e. Algeria, Egypt, Nigeria, Morocco and South Africa) between 1981 and 2020 using the fully modified ordinary least squares (FMOLS) approach.

The panel result reveals that capital investment and energy interaction and financial development and capital investment moderation reduce pollution in all the countries. However, for country-specific results, the interaction of investment and energy lowers emissions in Algeria, South Africa, Nigeria and Morocco but increases pollution in Egypt. Similarly, except for Egypt, financial development and capital investment interaction offset pollution in Algeria, Nigeria, South Africa and Morocco.

The limitation of the study stems from the inability to extend the scope to cover the entire African region. However, the fact that the authors selected the most prominent African nations in the sample to enable us to set the template for other smaller nations to follow makes the study tenable in its present form.

Energy-dependent African countries should invest in eco-friendly machines, technologies and equipment to lower pollution vis-à-vis production expansion.

The present research is more expansive by combining the finance and capital investment channels in the quest for decarbonising emerging African nations. Moreover, this is a comparative study, unlike past studies that mainly deploy a one-size-fits-all approach.

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 paper examines the extreme dependence and asymmetric risk spillovers between crude oil futures and ten US stock sector indices (consumer discretionary, consumer staples, energy, financials, health care, industrials, information technology, materials, telecommunication and utilities) before and during COVID-19 outbreak. This study is based on the rationale that stock sectors exhibit heterogeneity in their response to oil prices depending on whether they are classified as oil-intensive or non-oil-intensive sectors and the possible time variation in the dependence and risk spillover effects.

The authors employ static and dynamic symmetric and asymmetric copula models as well as Conditional Value at Risk (VaR) (CoVaR). Finally, they use robustness tests to validate their results.

Before the COVID-19 pandemic, crude oil returns showed an asymmetric tail dependence with all stock sector returns, except health care and industrials (materials), where an average (symmetric tail) dependence is identified. During the COVID-19 pandemic, crude oil returns exhibit a lower tail dependency with the returns of all stock sectors, except financials and consumer discretionary. Furthermore, there is evidence of downside and upside risk asymmetric spillovers from crude oil to stock sectors and vice versa. Finally, the risk spillovers from stock sectors to crude oil are higher than those from crude oil to stock sectors, and they significantly increase during the pandemic.

There is heterogeneity in the linkages and the asymmetric bidirectional systemic risk between crude oil and US economic sectors during bearish and bullish market conditions; this study is the first to investigate the average and extreme tail dependence and asymmetric spillovers between crude oil and US stock sectors.

This study aims to examine the multiscale predictability power of COVID-19 deaths and confirmed cases on the S&P 500 index (USA), CAC30 index (France), BSE index (India), two strategic commodity futures (West Texas intermediate [WTI] crude oil and Gold) and five main uncertainty indices Equity Market Volatility Ticker (EMV), CBOE Volatility Index (VIX), US Economic Policy Uncertainty (EPU), CBOE Crude Oil Volatility Index (OVX) and CBOE ETF Gold Volatility Index (GVZ). Furthermore, the authors analyze the impact of uncertainty indices and COVID-19 deaths and confirmed cases on the price returns of stocks (S&P500, CAC300 and BSE), crude oil and gold.

The authors used the wavelet coherency method and quantile regression approach to achieve the objectives.

The results show strong multiscale comovements between the variables under investigation. Lead-lag relationships vary across frequencies. Finally, COVID-19 news is a powerful predictor of the uncertainty indices at intermediate (4–16 days) and low (32–64 days) frequencies for EPU and at low frequency for EMV, VIX, OVX and GVZ indices from January to April 2020. The S&P500, CAC30 and BSE indexes and gold prices comove with COVID-19 news at low frequencies during the sample period. By contrast, COVID-19 news and WTI oil moderately correlated at low frequencies. Finally, the returns on equity and commodity assets are influenced by uncertainty indices and are sensitive to market conditions.

This study contributes to the literature by exploring the time and frequency dependence between COVID-19 news (confirmed and death cases) on the returns of financial and commodity markets and uncertainty indexes. The findings can assist market participants and policymakers in considering the predictability of future prices and uncertainty over time and across frequencies when setting up regulations that aim to enhance market efficiency.

The purpose of this paper is to check the impact of female directors on the board and foreign institutional investors on earnings manipulation.

The data sample includes Chinese listed companies on the Shenzhen and Shanghai stock exchanges. The data are collected from China Stock Market and Accounting Research database covering the period from 2010 to 2017. The authors use a dynamic generalized method of moments in the study.

The findings show that the presence of female director on the board has a significant negative impact on both discretionary accruals and real earning management. However, the authors obtain different results for foreign institutional investor investors. This may be the result of myopia as the foreign institutional stockholders in Chinese companies are looking for quick profit encouraging management to manipulate earnings. the findings survive several robustness tests.

The authors expect the research results provide ample evidence about how female directors affects earnings manipulation, and also hope the research helps to understand how, in China, institutional ownership affects earnings manipulation.

This study aims to contribute to the literature on migration by examining the nexus between migration-related fears and housing affordability in France, Germany, the UK and the USA using new datasets for migration-related fears.

This study adopts the feasible quasi-generalized least squares approach wherein a predictor can be isolated in the estimation process. Thus, rather than specifying a multi-predictor model that may also lead to parameter proliferation, a single-predictor model (for the predictor of interest) is formulated while also accounting for other salient features resulting from suppressing other important factors that may not be of interest to the current study. Such salient features include persistence, endogeneity and conditional heteroscedasticity issues.

Overall, the results show heterogeneous responses of housing affordability to migration fears across the four developed countries, as the latter deteriorates housing affordability in Germany and the USA and improves it in France and the UK. Similarly, the GFC makes housing less affordable in all four countries as low interest rate passes the mediation test in the nexus. The results, especially for low interest rates, are robust to different uncertainty measures.

As is often the case with economic phenomena, no single model can capture all the factors influencing an economic variable. Thus, besides examining the nexus between migration fears and housing affordability, the authors also account for the role of GDP per capita, given the influence of population and income dynamics on housing affordability. However, incorporating GDP per capita alone does not substantially enhance the model’s ability to predict housing affordability. Future research should explore additional macroeconomic and social factors, such as human capital development, to further enhance this subject.

The findings have significant implications for policymakers regarding the use of low interest rates to counteract the adverse effects of migration-related fear on housing affordability. Specifically, to mitigate the potential negative impact of migration and the associated fear on housing affordability, monetary authorities could adopt a more accommodative stance on mortgages. By allowing real estate investors to obtain loans at lower rates, this approach would help increase housing supply and reduce the housing gap exacerbated by migration influx.

The values of this study lie in its examination of housing affordability in relation to migration fears from both the demand and supply sides of the market. Furthermore, the analyses are conducted to cover out-of-sample forecast evaluation as in-sample predictability may not guarantee out-of-sample prediction.

This paper aims to examine the return and volatility transmission among economic policy uncertainty (EPU), geopolitical risk (GPR), their interaction (EPGR) and five tradable precious metals: gold, silver, platinum, palladium and rhodium.

Applying time-varying parameter vector autoregression (TVP-VAR) frequency-based connectedness approach to a data set spanning from January 1997 to February 2023, the study analyzes return and volatility connectedness separately, providing insights into how the data, in return and volatility forms, differ across time and frequency.

The results of the return connectedness show that gold, palladium and silver are affected more by EPU in the short term, while all precious metals are influenced by GPR in the short term. EPGR exhibits strong contributions to the system due to its elevated levels of policy uncertainty and extreme global risks. Palladium shows the highest reaction to EPGR, while silver shows the lowest. Return spillovers are generally time-varying and spike during critical global events. The volatility connectedness is long-term driven, suggesting that uncertainty and risk factors influence market participants’ long-term expectations. Notable peaks in total connectedness occurred during the Global Financial Crisis and the COVID-19 pandemic, with the latter being the highest.

Using the recently updated news-based uncertainty indicators, the study examines the time and frequency connectedness between key uncertainty measures and precious metals in their returns and volatility forms using the TVP-VAR frequency-based connectedness approach.

This study examines the dynamics of the comovement and causal relationship between conventional (Bitcoin, Ethereum and Binance coin) and Islamic (OneGram, X8X token and HelloGold) cryptocurrencies.

This study uses wavelet coherence approach to examine the time-varying lead-lag relationship between conventional and Islamic cryptocurrencies. Furthermore, the authors use BEKK-GARCH model to estimate the optimal weights, hedge ratio and hedging effectiveness in pre-COVID-19 and during the COVID-19 period.

The authors find no significant comovement in pre-COVID-19. However, the authors find significant positive comovement in conventional and Islamic cryptocurrencies at the beginning of the pandemic, and in most cases, conventional cryptocurrencies are leading. X8X and HelloGold have no/weak correlation with conventional cryptocurrencies, implying that investors can diversify the risk by making an Islamic and conventional cryptocurrencies portfolio. The authors also calculate the optimal weights, hedge ratio and hedging effectiveness using the BEKK-GARCH model. Based on the optimal weights, for the portfolios of conventional–Islamic cryptocurrencies, investors are suggested to increase their investment in Islamic cryptocurrencies during the COVID-19 than normal period. The results of hedge ratios show that hedging costs are higher during COVID-19 than before.

The findings of the paper offer several practical policy implications for investors, portfolio manager, Shariah advisors and policymakers pertaining to asset allocation, risk management, forecasting and diversification. Specifically, investors can maximize the risk adjusted returns of their conventional cryptocurrencies portfolio by adding some portions of Islamic cryptocurrencies. Considering the comovement is time-varying, investors/manager should adjust their investment strategies frequently. For the entrepreneurs in crypto-industry, it is advised to introduce new Islamic cryptocurrencies, as it has a huge growth potential because of their distinct features and performance.

This is the first study that explores the linkages between conventional and Islamic cryptocurrencies, therefore this study extends the literature of Islamic finance, stablecoins and cryptocurrencies in pre-COVID-19 and during COVID-19 period. The study results provide insights to conventional crypto investor on how to manage their portfolio during normal and turbulent period.