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This paper aims to model the relationship between oil price and stock returns for selected sectors in Nigeria using monthly data from January 2007 to December 2016.

The authors use both the linear (symmetric) autoregressive distributed lag (ARDL) by Pesaran et al. (2001) and non-linear (asymmetric) ARDL by Shin et al. (2014), and they also account for structural breaks using the Bai and Perron (2003) test that allows for multiple structural changes in regression models.

The results indicate that the strength of this relationship varies across sectors, albeit asymmetric and breaks. The authors identify two structural breaks that occur in 2008 and 2010/2011, which coincidentally correspond to the global financial crisis and the Arab spring (Libyan shutdowns), respectively. Moreover, the authors observe strong support for asymmetry and structural breaks for some sectors in the reaction of sector returns to movement in oil prices. These findings are robust and insensitive when considering different oil proxies. While further extensions can be pursued, the consideration of asymmetric effects as well as structural breaks should not be jettisoned when modelling this nexus.

This study is one of the very few studies that have investigated the sectoral behaviour of stocks to oil price shocks, particularly in Nigeria. This paper contributes to the oil stock literature using the recent technique of asymmetry and also considering the role structural breaks play in the relationship between oil price and stock returns.

As financial markets for environmentally friendly investment grow in both scope and size, analyzing the relationship between green financial markets and African stocks becomes an important issue. Therefore, this paper examines the role of infectious disease-based uncertainty on the dynamic spillovers between African stock markets and clean energy stocks.

The authors employ the dynamic spillover in time and frequency domains and the nonparametric causality-in-quantiles approach over the period of November 30, 2010, to August 18, 2021.

These findings are discernible in this study's analysis. First, the authors find evidence of strong connectedness between the African stock markets and the clean energy market, and long-lived but weak in the short and medium investment horizons. Second, the BDS test shows that nonlinearity is crucial when examining the role of infectious disease-based equity market volatility in affecting the interactions between clean energy stocks and African stock markets. Third, the causal analysis provides evidence in support of a nonlinear causal relationship between uncertainties due to infectious diseases and the connection between both markets, mostly at lower and median quantiles.

Considering the global and recent use of clean energy equities and the stock markets for hedging and speculative purposes, one may argue that rising uncertainties may significantly influence risk transmissions across these markets. This study, therefore, is the first to examine the role of pandemic uncertainty on the connection between clean stocks and the African stock markets.

In this paper, the author examines the role of uncertainty due to pandemic on the predictability of sectoral stock returns in South Africa. This is motivated by the ongoing global pandemic, COVID-19, in predicting sector stock returns.

The study considers estimation of dynamic panel data with dynamic common correlated effects estimator and two pair-wise forecast measures, namely Campbell and Thompson (2008) and Clark and West (2007) tests in dealing with the nested predictive models.

The results show that pandemic uncertainty has a negative and statistically significant effect on the different sector returns, implying that sector stock returns decline as the pandemic outbreak becomes more pronounced. While the single predictor model consistently outperforms the historical average model both for in-sample and out-of-sample, controlling for other macroeconomic variables effect improves the forecast accuracy of infectious diseases uncertainty. These results are consistently robust to both the in-sample and out-of-sample forecast periods, outliers and heterogeneity. These results have implications for portfolio diversification strategies, which we set aside for future research.

The empirical literature is satiated with studies on how news can predict economic and financial variables, however, the role of uncertainty due to infectious diseases in the stock return predictability especially at the sectoral level is less understudied, this is the main contribution of the study.

This paper examines the return and volatility spillovers among major cryptocurrency using daily data from 10/08/2015 to 15/04/2018.

The authors employ the Dielbold and Yilmaz (2012) spillover approach and rolling sample analysis to capture the inherent secular and cyclical movements in the cryptocurrency market.

The authors show that there is substantial difference between the behaviour of the cryptocurrency portfolios return and volatility spillover indices over time. The authors find evidence of interdependence among cryptocurrency portfolios given the spillover indices. While the return spillover index reveals increased integration among the currency portfolios, the volatility spillover index experiences significant bursts during major market crises. Interestingly, return and volatility spillovers exhibit both trends and bursts respectively.

This study makes a methodological contribution by adopting Dielbold and Yilmaz (2012) approach to quantify the returns and volatility transmissions among cryptocurrencies. To the best of our knowledge, little or no study has adopted the Dielbold and Yilmaz (2012) methodology to investigate this dynamic relationship in the cryptocurrencies market. The Dielbold and Yilmaz (2012) approach provides a simple and intuitive measure of interdependence of asset returns and volatilities by exploiting the generalized vector autoregressive framework, which produces variance decompositions that are unaffected by ordering.

This paper aims to model the relationship between oil price and six major agricultural commodity prices using monthly data from January 1997 to December 2016.

The authors use both the linear autoregressive distributed lag by Pesaran et al. (2001) and the nonlinear autoregressive distributed lag by Shin et al. (2014), and they also account for structural breaks using the Bai and Perron (2003) test that allows for multiple structural changes in regression models.

These findings are discernible from the authors’ analyses. First, the linear analysis indicates a significant positive effect of oil prices on the agricultural commodity prices, which supports evidence on the non-neutrality hypothesis. Second, oil price asymmetries seem to matter more when dealing with agricultural commodity prices, except for groundnut. Third, it may be necessary to pre-test for structural breaks when modelling the relationship between oil price and agricultural prices regardless of the commodity being analysed. Fourth, the asymmetric effect for the agricultural commodity prices is non-neutral to oil prices, except for rice in the case of structural breaks.

This paper contributes to the on-going debate on the oil–agricultural commodity nexus using the recent technique of asymmetry and also considering the role structural breaks play in the relationship between oil price and agricultural commodity prices.

The COVID-19 pandemic has led to global economic policy uncertainty, which has increased the need to investigate ways to mitigate the uncertainty. This study aims to examine the potential of cryptocurrencies as a hedge and safe haven avenue against economic policy uncertainty.

This study investigates the behavior of the five leading cryptocurrencies in relation to country-level and group-level economic policy uncertainty indices, as measured by the text-based method developed by Baker et al. (The Quarterly Journal of Economics, 2016, 131, 1593–1636). The research covers a broad range of emerging and developed economies from July 2013 to September 2020. The study employs the approach of Narayan et al. (Economic Modelling, 2016, 53, 388–397) to examine the hedging and safe-haven properties of cryptocurrencies.

This study finds that the top cryptocurrencies play a hedging role against economic policy uncertainty, with some exceptions. Additionally, there is evidence to support the idea that cryptocurrencies can serve as a safe haven during the COVID-19 pandemic. As a result, investors may benefit from using cryptocurrencies as a risk-management avenue during times of uncertainty.

This research contributes to the existing literature by testing the cryptocurrencies' hedging and safe haven properties in a new way, by analyzing their lead and lag behaviors using a recent and innovative approach. Additionally, it examines a wide range of emerging and advanced markets, providing insight into the potential of using cryptocurrencies as a risk mitigation avenue.

The issue of foreign aid has continued to gain renewed economic cum political attention in the early years of the twenty-first century. At a summit, popularly known as the Millennium Summit, which took place in 2000, there was an agreement by the international community concerning some goals known as the Millennium Development Goals which were targeted to be reached by the year 2015 but have now been replaced by the Sustainable Development Goals. Against this background, it becomes pertinent to ascertain the contributions and impact of foreign aid in the form of Overseas Development Assistance (ODA) on capital formation in Nigeria. This is an area of foreign aid studies that has been ignored by many researchers. Most studies are seen delving into analyzing the aid-growth nexus without evaluating the transmission link through which foreign aid transmits to affect economic growth. There is paucity of studies on the aid-capital nexus. The paper aims to discuss these issues.

The empirical method used was autoregressive distributed lag (ARDL) model.

The empirical results from the ARDL model estimations show that foreign aid, which is proxied by ODA, has a positive and significant impact on capital formation in Nigeria for the years under analysis. The result of the Granger causality test shows that a bi-directional granger causality exists between foreign aid and gross fixed capital formation (GFCF).

Empirical results from the ARDL model estimations show that foreign aid, which is proxied by ODA, has a positive and significant impact on capital formation in Nigeria for the years under analysis. The result of the Granger causality test shows that a bi-directional Granger causality exists between foreign aid and GFCF. It is therefore recommended that government should make serious efforts toward the implementation and effective utilization of foreign aid. Appropriate policy measures that would monitor the maximum and effective utilization of foreign aid are also required.