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President Trump's tenure was accompanied by a series of protectionist measures that intended to reinvigorate US-based production and make manufacturing supply chains more “local”. Amidst these increasing institutional pressures to localise, and the business uncertainty that ensued, this study investigates the extent to which manufacturers reconfigured their supply bases.

Bloomberg's Supply Chain Function (SPLC) is used to manually extract data about the direct suppliers of 30 of the largest American manufacturers in terms of market capitalisation. Overall, the raw data comprise 20,100 quantified buyer–supplier relationships that span seven years (2014–2020). The supply base dimensions of spatial complexity, spend concentration and buyer dependence are operationalised by applying appropriate aggregation functions on the raw data. The final dataset is a firm-year panel that is analysed using a random effect (RE) modelling approach and the conditional means of the three dimensions are plotted over time.

Over the studied timeframe, American manufacturers progressively reduced the spatial complexity of their supply bases and concentrated their purchase spend to fewer suppliers. Contrary to the aims of governmental policies, American manufacturers increased their dependence on foreign suppliers and reduced their dependence on local ones.

The research provides insights into the dynamics of manufacturing supply chains as they adapt to shifting institutional demands.

The purpose of this research is to primarily understand how crypto traders can use the Bitcoin as a hedge or safe haven asset to reduce their losses from crypto trading. The study also aims to provide insights to crypto investors (portfolio managers) who wish to maintain a crypto portfolio for the medium term and can use the Bitcoin to minimize their losses. The findings of this research can also be used by policymakers and regulators for accommodating the Bitcoin as a medium of exchange, considering its safe haven nature.

This study applies the cross-quantilogram (CQ) approach introduced by Han et al. (2016) to examine the safe-haven property of the Bitcoin against the other selected crypto assets. This method is robust for estimating bivariate volatility spillover between two markets given unusual distributions and extreme observations. The CQ method is capable of calculating the magnitude of the shock from one market to another under different quantiles. Additionally, this method is suitable for fat-tailed distributions. Finally, the method allows anticipating long lags to evaluate the strength of the relationship between two variables in terms of durations and directions simultaneously.

The Bitcoin acts as a weak safe haven asset for a majority of new crypto assets for the entire study period. These results hold even during greed and fear sentiments in the crypto market. The Bitcoin has the ability to protect crypto assets from sharp downturns in the crypto market and hence gives crypto traders some respite when trading in a highly volatile asset class.

This study is the first attempt to show how the Bitcoin can act as a true matriarch/patriarch for crypto assets and protect them during market turmoil. This study presents a clear and concise representation of this relationship via heatmaps constructed from CQ analysis, depicting the quantile dependence association between the Bitcoin and other crypto assets. The uniqueness of this study also lies in the fact that it assesses the protective properties of the Bitcoin not only for the entire sample period but also specifically during periods of greed and fear in the crypto market.

This paper examines the time-varying efficiency of nine major Middle East and North Africa (MENA) stock markets namely Egypt, Bahrain, UAE, Jordan, Saudi Arabia, Oman, Qatar, Morocco and Tunisia during times of COVID-19 pandemic outbreak and vaccines.

The authors use two econometric approaches: (1) autocorrelation tests including the wild bootstrap automatic variance ratio test, the automatic portmanteau test and the Generalized spectral test, and (2) a non-Bayesian generalized least squares-based time-varying model with statistical inferences.

The results show that the degree of stock market efficiency of Egyptian, Bahraini, Saudi, Moroccan and Tunisian stock markets is influenced by the COVID-19 pandemic crisis. Furthermore, the authors find a tendency toward efficiency in most of the MENA markets after the announcement of the COVID-19's vaccine approval. Finally, the Jordanian, Omani, Qatari and UAE stock markets remain globally efficient during the three sub-periods of the COVID-19 pandemic outbreak.

The results have important implications for asset allocations and financial risk management. Portfolio managers may maximize the benefit of arbitrage opportunities by taking strategic long and short positions in these markets during downward trend periods. Policymakers should implement the action plans and reforms to protect the stock markets from global shocks and ensure the stability of the stock markets.

The purpose of this paper is to introduce a generalization of the time-varying correlation elliptical copula models and to analyse its impact on the tail risk of a portfolio of foreign currencies during the Covid-19 pandemic.

The authors consider a multivariate time series model where marginal dynamics are driven by an autoregressive moving average (ARMA)–Glosten-Jagannathan-Runkle–generalized autoregressive conditional heteroscedastic (GARCH) model, and the dependence structure among the residuals is given by an elliptical copula function. The correlation coefficient follows an autoregressive equation where the autoregressive coefficient is a function of the past values of the correlation. The model is applied to a portfolio of a couple of exchange rates, specifically US dollar–Japanese Yen and US dollar–Euro and compared with two alternative specifications of the correlation coefficient: constant and with autoregressive dynamics.

The use of the new model results in a more conservative evaluation of the tail risk of the portfolio measured by the value-at-risk and the expected shortfall suggesting a more prudential capital allocation policy.

The main contribution of the paper consists in the introduction of a time-varying correlation model where the past values of the correlation coefficient impact on the autoregressive structure.

In India, the operational performance of the refinery is influenced by many factors. It is important to identify those key drivers which can assist the refineries to uphold and succeed in day-to-day production activities. Therefore, the purpose of this study is to evaluate the operational efficiency of seven Indian oil refineries during the period 2010 to 2018.

In this work, a two-stage empirical analysis is proposed. In the first stage, the data envelopment analysis (DEA) – variable return to scale model is used to evaluate the operational efficiency of the Indian oil refineries. The ordinary least square (OLS), random effect generalized least square (GLS) and Tobit model are used in the second stage to identify the key determinants of efficiency and to explain the variation in refinery efficiency.

The first-stage DEA results showed that the Numaligarh Refinery Limited and Chennai Petroleum Corporation Limited are found to be more efficient than the rest of the sampled refineries and attained their efficiency scores of 0.993 and 0.981, respectively, during the study period. The second-stage regression analysis suggested three explanatory variables: refinery structure, utilization rate and distillate yield, which are found to be significant in explaining variations in refinery efficiency.

This study provides valuable information that would help policymakers to formulate policies toward improving the efficiency of underperforming Indian refineries, which reduces the excessive use of resources and gives a competitive advantage.

This study proposes the first-ever application of the profit frontier DEA model for assessing the operational efficiency of oil refineries and explains the variation in refinery’s efficiency using OLS, GLS as well as the Tobit model.

This study reviews existing cryptocurrency research to provide answers to three puzzles in the literature. First, is cryptocurrency more like gold (i.e., a commodity) or should it be classified as a new financial asset? Second, can we apply our knowledge of the traditional capital market to the emerging cryptocurrency market? Third, what might be the future of cryptocurrency?

Bibliometric analysis is used to assess 2,098 finance-related cryptocurrency publications from the Web of Science (WoS) Core Collection database from January 2009 to April 2022. Three key research streams are identified, namely, (1) cryptocurrency features, (2) behaviour of the cryptocurrency market and (3) blockchain implications.

First, cryptocurrency should be viewed and regulated as a new asset class rather than a currency or a new commodity. While it can provide diversification benefits to the portfolio, cryptocurrency cannot work as a safe haven asset. Second, crypto markets are typically inefficient. Asset bubbles exist and are exacerbated by behavioural finance factors. Third, cryptocurrency demonstrates increasing potential as a medium of exchange and store of value.

Extant review papers primarily study one or two particular research topics, overlooking the interaction between topics. The few existing systematic literature reviews in this area typically have a narrow focus on trend identification. This study is the first study to provide a comprehensive review of all financial-related studies on cryptocurrency, synthesising the research findings from 2,098 publications to answer three cryptocurrency puzzles.

The purpose of this study is to investigate the nexus between financial inclusion and human development for countries belonging to different income groups during 2009–2019, and whether this relation differs across these groups.

The paper constructs an index of financial inclusion (IFI) for different income group countries employing dynamic panel data models estimated by generalized method of moments (GMM) to analyse the relation between financial inclusion and human development.

Financial inclusion in low and lower-middle-income countries has higher effect on human development than in high and upper-middle income countries.

The study examines the effect of IFI on the human development index (HDI) at the aggregate level. Future research can tackle the IFI effect on every component of HDI and other aspects of financial inclusion could be incorporated like financial technology.

The originality lies in constructing an index for financial inclusion using the most recent data for a wide range of countries, in addition to examining the impact of financial inclusion on the human development levels of different income groups allowing for more accurate analysis tackling the differences in terms of adopted policies across various income groups; unlike other studies that are carried out on a one country basis or only across one or two country groups that do not allow for comparison across various groups of countries.

The COVID-19 pandemic and the onset of the conflict in Ukraine led to a sustained downturn in tourist arrivals (TA) in Russia. This paper aims to explore the influence of geopolitical risk (GPR) and other indices on TA over 1995–2023.

We employ a nonlinear autoregressive distributed lag (NARDL) model to analyze the effects, capturing both the positive and negative shocks of these variables on TA.

Our research demonstrates that the NARDL model is more effective in elucidating the complex dynamics between macroeconomic factors and TA. Both an increase and a decrease in GPR lead to an increase in TA. A 1% negative shock in GPR leads to an increase in TA by 1.68%, whereas a 1% positive shock in GPR also leads to an increase in TA by 0.5%. In other words, despite the increase in GPR, the number of tourists coming to Russia increases by 0.5% for every 1% increase in that risk. Several explanations could account for this phenomenon: (1) risk-tolerant tourists: some tourists might be less sensitive to GPR or they might find the associated risks acceptable; (2) economic incentives: increased risk might lead to a depreciation in the local currency and lower costs, making travel to Russia more affordable for international tourists; (3) niche tourism: some tourists might be attracted to destinations experiencing turmoil, either for the thrill or to gain firsthand experience of the situation; (4) lagged effects: there might be a time lag between the increase in risk and the actual impact on tourist behavior, meaning the effects might be observed differently over a longer period.

Our study, employing the NARDL model and utilizing a dataset spanning from 1995 to 2023, investigates the impact of GPR, gross domestic product (GDP), real effective exchange rate (REER) and economic policy uncertainty (EPU) on TA in Russia. This research is unique because the dataset was compiled by the authors. The results show a complex relationship between GPR and TA, indicating that factors influencing TA can be multifaceted and not always intuitive.

Shadow banks or non-bank financial intermediaries (NBFIs) are facilitators of credit, especially in emerging market economies (EMEs). However, there are certain risks associated with them, such as their unchecked leverage and interconnectedness with the rest of the financial system. In light of this, the present study analyses the impact of the growth of shadow banks on the stability of the banking sector and the overall stability of the financial system. The authors further examine the effect of the growth of finance companies (a type of NBFIs) on financial stability.

The study employs data of 11 EMEs (monitored by the Financial Stability Board (FSB)) for the period 2002–2020 to examine the above relationships. Panel-corrected standard errors method and Driscoll–Kray standard error estimation are deployed to conduct the analysis.

The results signify that the growth of the shadow banking sector and the growth of lending to the shadow banking sector are negatively associated with the stability of the banking sector and increases the vulnerability of the financial system (overall instability). This implies that the higher the growth of the shadow banks, the higher the financial fragility. Finance companies are also found to negatively affect financial stability. These findings are validated by different estimation methods and point out the risks posed by the NBFI sector.

The extant study builds a composite index (Financial Vulnerability Index (FVI)) to measure financial stability; thus, the findings contribute to the evolving literature on shadow banks.

This paper aims to explore whether environmental, social and governance (ESG) performance is positively associated with firm investment efficiency (IE) in emerging economies. It also examines whether board cultural diversity can moderate the ESG–IE relationship.

This paper uses a cross-country sample of listed firms located in seven emerging countries over the 2011–2019 period. The authors use a fixed effect panel regression to empirically test the hypotheses. The authors also use a lagged model and a Heckman’s (1979) two-stage procedure to mitigate potential endogeneity issues. In addition, a two-stage least squares regression analysis was done as an additional robustness check.

This study finds that firms with stronger ESG performance have a higher investment efficiency. Interestingly, this study finds that board cultural diversity negatively moderates the impact of ESG performance on IE for firms operating in settings prone to overinvestment. This result suggests that ESG performance plays a less important role in mitigating managers' tendencies to overinvest when corporate boards have more foreign directors. However, the authors do not find such evidence in firms prone to underinvestment. These findings hold after using an alternative measure of IE and controlling for endogeneity concerns.

This paper adds to the existing body of knowledge in three dimensions. First, to the best of the authors’ knowledge, this is the first cross-country study that investigates the linkage between ESG performance and corporate IE in the context of emerging countries. Second, the authors have enriched the prior literature by examining the moderating effect of board cultural diversity on the positive association between ESG performance and corporate IE. Finally, this study has important implications for policymakers and capital suppliers in emerging countries, which strive to facilitate the efficient allocation of scarce resources.