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This study aims to examine whether agricultural commodities exhibited deviations in the lead-lag relationship between future and spot prices of farmer producer organizations (FPOs) traded commodities in the Indian derivative market after trade suspensions during Covid-19. The study may help buyers and sellers to get a fair price for their commodities after lockdown-trade disruptions.

The study applied the Granger causality (GC) test and the vector error correction model (VECM) to analyse short-run and long-run lead-lag relationships. Moreover, the study examined the pre-post-trade suspension effect on the lead-lag relationship of commodity prices.

The GC test results show that five out of the 13 agri-commodities have changed their lead-lag relationship from future to spot in the short run. Simultaneously, VECM captured changes in the lead-lag relationship for the same five commodities in the long run due to trade suspensions.

The findings indicate a reverse lead-lag relationship between future and spot prices for aforesaid commodities after trade suspension. The stakeholders may use the lead prices for these commodities to perform a fair trade. The study may be helpful in structuring price discovery strategy to achieve optimal price and efficient derivative trading.

To the best of the authors’ knowledge, this is the first study examining the effects of trade suspension on price discovery in FPO-traded agri-derivatives caused by the COVID-19 pandemic.

The purpose of this study investigates the short-term market reaction of three commodity futures indices for four recent events of high geopolitical risk: the Ukraine–Russia war, the Taiwan Strait crisis and the Hamas terrorist attack on Israel.

The author examines three commodity futures indices at and around the beginning of four recent events of high geopolitical risk using an event study methodology.

The results show a positive abnormal return for the commodity futures indices for three of the four recent events considered in the analysis. The exception in terms of abnormal returns observed is the visit of US Speaker of the House Nancy Pelosi to Taiwan on August 2, 2022, which resulted in statistically significant negative abnormal returns in the commodity futures around the visit. The other three geopolitical events, by causing an increase of uncertainty level and supply-side constraints, led to a rise in the price of most commodity futures. This allowed commodity-exporting countries to achieve positive and statistically significant abnormal returns. Policy implications of our findings are discussed.

The effect of high geopolitical risk events on commodity futures indices has been relatively little examined in the financial theory. This study intends to fill this gap in the literature.

This study aims to evaluate the safe haven property of several assets against the US and European stock markets during the Russo-Ukrainian War in a time–frequency framework.

This study uses the wavelet-based dynamic conditional correlation-generalized autoregressive conditional heteroskedasticity (DCC-GARCH) methodology and wavelet coherence on daily returns for the S&P500, STOXX600 and 13 possible save haven assets.

The results show that wheat and corn are the best assets to use as hedges and safe havens for all types of investors. The second-ranked are energy commodities, which are hedges and safe havens for long-term investors. Gold, silver and palladium display hedging and safe haven qualities for medium- and long-term investment. However, cryptocurrencies, the Dow Jones sustainability index and Islamic indices do not act as safe havens for most holding periods.

These findings have significant implications for portfolio investment strategies in times of geopolitical risks.

The contributions of this study are twofold. First, several assets from different classes were analyzed as possible candidates for safe havens. Second, to the best of the authors’ knowledge, this is the first study to analyze safe haven property through different investment horizons for the US and the European stock market indices during the Russo-Ukrainian War.

Under the trend of global economic integration and the new context of stagflation, frequent fluctuations in international interest rates are exerting far-reaching impacts on the world economy. In this paper, the transmission mechanism of the impact of fluctuations in international interest rates (specifically, the American interest rate) on the bankruptcy risk in China's pillar industry, the construction industry (which is also sensitive to interest rates), is examined.

Using an improved contingent claims analysis, the bankruptcy risk of enterprises is calculated in this paper. Additionally, an individual fixed-effects model is developed to investigate the mediating effects of international interest rates on the bankruptcy risk in the Chinese construction industry. The heterogeneity of subindustries in the industrial chain and the impact of China's energy consumption structure are also analysed in this paper.

The findings show that fluctuations in international interest rates, which affect the bankruptcy risk of China's construction industry, are mainly transmitted through two major pathways, namely, commodity price effects and exchange rate effects. In addition, the authors examine the important impact of China's energy consumption structure on risk transmission and assess the transmission and sharing of risks within the industrial chain.

First, in the research field, the study of international interest rate risk is extended to domestic-oriented industries. Second, in terms of the research content, this paper is focused on China-specific issues, including the significant influence of China's energy consumption structure characteristics and the risk contagion (and risk sharing) as determined by the current development of the Chinese construction industry. Third, in terms of research methods a modified contingent claim analysis approach to bankruptcy risk indicators is adopted for this study, thus overcoming the problems of data frequency, market sentiment and financial data fraud, which are issues that are ignored by most relevant studies.

Predicting commodity futures trading volumes represents an important matter to policymakers and a wide spectrum of market participants. The purpose of this study is to concentrate on the energy sector and explore the trading volume prediction issue for the thermal coal futures traded in Zhengzhou Commodity Exchange in China with daily data spanning January 2016–December 2020.

The nonlinear autoregressive neural network is adopted for this purpose and prediction performance is examined based upon a variety of settings over algorithms for model estimations, numbers of hidden neurons and delays and ratios for splitting the trading volume series into training, validation and testing phases.

A relatively simple model setting is arrived at that leads to predictions of good accuracy and stabilities and maintains small prediction errors up to the 99.273th quantile of the observed trading volume.

The results could, on one hand, serve as standalone technical trading volume predictions. They could, on the other hand, be combined with different (fundamental) prediction results for forming perspectives of trading trends and carrying out policy analysis.

The purpose of this paper is to utilize the ISM–MICMAC–AHP integrated method to determine the factors influencing consumers’ purchase intention towards e-commerce agricultural products and develop a model, as well as to explore strategies to enhance consumers’ purchase intention.

The study conducts an exhaustive literature review and gathered expert opinions to identify 20 influencing factors across five dimensions, constructing a comprehensive factor system. The Interpretative Structural Modelling (ISM) method is used determine the hierarchical structure and interrelationships among these factors. Subsequently, Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analysis is done to assess the influence and dependence relationships among the factors in the system. Finally, Analytic Hierarchy Process method is proposed to distribute assessment indicators weights.

The system of factors influencing consumers’ intention to purchase agricultural products through e-commerce is constructed as a six-level hierarchical interpretive structural model. The quality and safety of agricultural products, consumer review, taste and mouth-feel of agricultural products and e-commerce platform reputation are the main factors that affect consumers’ intention to purchase e-commerce agricultural products. Consumers’ degree of education, age and level of income exhibit the highest driving force, while e-commerce platform reputation demonstrates the highest level of dependence.

This paper represents the first attempt to employ an ISM-MICMAC-AHP integrated method to study consumers’ purchase intention for e-commerce agricultural products. The findings offer actionable insights for e-commerce platforms and policymakers, highlighting critical factors like product quality and platform reputation, which can inform strategies to enhance consumer trust and drive sustainable growth in agricultural e-commerce. This research supports evidence-based decision-making, promoting more effective management practices and policy development in the sector.

Fiat money production necessitates physical commodities, increasing costs and its flow is challenging to monitor, making it vulnerable to criminal exploitation. Cryptocurrencies offer decentralized solutions, but their decentralization has led to illegal activities. Current cross-border transactions face high costs, resource intensity and lack of instant currency transfers. Offline transactions are essential in unreliable networks.

Here, the authors proposed the methodology to perform offline transactions based on card, quick response (QR) code and a foreign transaction framework with universal identification (UID) to perform cross-border transactions using blockchain-dependent central bank digital currencies (CBDCs). Implications for the financial system are also analyzed.

The proposed CBDC framework reduces illegal transactions, corruption and the cost of producing fiat money; eases overseas transactions; and eventually increases international tourism, trade and business between countries. It also reduces the processing fees. Offline framework found useful for performing retail-level transactions.

The research methodology may face limitations due to diplomatic relations, political instability, sanctions and the need for robust offline transaction infrastructure.

The proposed CBDC framework simplifies debt and insurance management, tax collection, international trade, tourism and global stock market participation. However, implementing CBDCs in low-income countries presents challenges like extensive training, infrastructure and user acceptance issues.

The adoption of CBDCs can enhance financial stability by reducing corruption and illegal transactions through improved traceability and monitoring, thereby curbing activities like terrorism.

Common framework for foreign transactions is based on the UID, and offline transaction framework is based on the sender’s QR code for multiple user applications.

The rapid rise of Islamic crypto assets, underpinned by blockchain technology, has introduced a novel dimension to the Islamic financial landscape, raising questions about their potential as safe havens within emerging Islamic economies. However, the opportunities and challenges associated with this phenomenon remain insufficiently explored. In this context, this study aims to empirically investigate the extent to which blockchain technology can establish Islamic crypto assets as safe havens in equity markets within Islamic economies.

This study addresses the need for rigorous empirical analysis to understand the dynamics between Islamic crypto assets and stock markets in emerging Islamic economies, focusing on the transmission of volatility. While the evolving nature of the Islamic financial sector demands reliable data, the reliance on the most available data offers insights into the expected future trends in this emerging field. The research specifically focuses on three essential assets in the Islamic financial portfolio: OneGram Coin and X8XToken, both backed by gold and MRHB DeFi, an Islamic DeFi asset lacking gold backing. These crypto assets are compared with corresponding assets in seven stock markets of emerging Islamic economies. Using daily log returns of the Islamic crypto assets from various sources and seven Islamic stock indices. The data covers the period from December 27, 2021, to December 28, 2022, capturing the fluctuations in Islamic stocks and cryptocurrency markets during the post-COVID-19 era. This research uses advanced econometric techniques, including pairwise dynamic correlation and the DCC GARCH model.

The findings indicate that Islamic crypto assets exhibit distinct characteristics, with lower volatility and low correlations compared to their conventional counterparts in non-Islamic contexts. This outcome suggests that these Islamic crypto assets could potentially serve as safe havens within Islamic stock markets, offering valuable insights for various stakeholders, including investors, governments and policymakers.

The findings are based on a specific set of Islamic crypto assets and may vary with a different selection. Market dynamics can also influence the relationships observed. Nevertheless, the outcomes provide valuable insights for investors, policymakers and researchers interested in the intersection of Islamic finance, cryptocurrency and technology.

In essence, this research not only unveils the potential of Islamic crypto assets as stabilizing forces but also delineates a trajectory for subsequent research endeavours within the realm of emerging Islamic Fintech, elucidating the challenges, opportunities and benefits that lie therein. With a discerning eye on circumventing the pitfalls entrenched within conventional crypto finance, this study contributes to a heightened comprehension of the transformative role that Islamic crypto assets can assume, ultimately enriching the financial resilience of Islamic economies.

The study aims to explore how healthcare workers (HCWs) navigate and experience time when caring for older cancer patients living with other illnesses.

This paper presents findings from a qualitative study of how HCWs conceptualise and navigate the temporal aspects of delivering personalised care to older people living with multimorbidity. Building on research from organisation studies and the sociology of time, we interviewed 19 UK HCWs about their experiences of delivering care to this patient group.

Our findings illustrate how the delivery of personalised care contradicts contemporary models for healthcare delivery defined by efficiency and standardisation. We found that HCWs engage with time as both a valuable commodity to be rationed and prioritised within a constrained context and as a malleable resource for managing workload and overcoming “turbulence” in the system. However, participants in this study also shared how the simultaneous multiplicity and lack of time had a profoundly personal impact on them through the emotional toll associated with “time debt” and “lost” time.

This research presents a unique analysis of how time is conceptualised and navigated in contemporary healthcare, offering valuable insights for policy improvement. We conclude that personalised models of healthcare are incompatible with many current temporal structures of treatment trajectories and work-practices, by nature of being centred around the person and not the system of delivery.

The study examined the effect of fiscal policy on poverty in sub-Saharan Africa (SSA) while accounting asymmetric (captured by economic downturns) and spillover effects.

The study used a fixed effect (within regression) IV model to account for country-specific characteristics. The study also adopts a cross-sectional and spatial dependence-consistent model to account for the potential cross-sectional and temporal dependence in panel data modeling.

The study discovered that the effect of fiscal policy on poverty is dependent on the state of the economy. Specifically, we find that fiscal policy helps to reduce the level of poverty during an economic downturn, more than at any other time. More specifically, the findings indicate that the fiscal policy lowers the rate of poverty in SSA, following macroeconomic shocks (captured by the COVID-19 epidemic, the Global Financial Crisis, and the commodity terms of trade shocks). Our findings suggest that fiscal policy is an important policy tool to mitigate the effects of macroeconomic shocks in SSA. Further, the findings also demonstrate that there is a spillover effect of poverty in the region. This implies coordinated, constructive actions by the regional governments can help to lessen the detrimental effects of extreme poverty.

The study examined the effectiveness of fiscal policy to reduce poverty in the event of an economic downturn.