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This study seeks to examine the relationship between macroeconomic shock indicators, namely geopolitical risk (GPR), global economic policy uncertainty (GEPU) and financial stress (FS), and returns as well as volatilities on seven carefully selected stock markets in Africa. Specifically, the study intends to unravel the co-movement and interdependence between the respective macroeconomic shock indicators and each of the stock markets under consideration across time and frequency.

This study employed wavelet coherence approach to examine the strength and stability of the relationships across different time scales and frequency components, thereby providing valuable insights into specific periods and frequency ranges where the relationships are particularly pronounced.

The study found that GEPU, Financial Stress (FS) and GPR failed to induce significant influence on African stock market returns in the short term (0–4 months band), but tend to intensify in the long-term band (after 6th month). On the contrary, stock market volatilities exhibited strong coherence and interdependence with GEPU, FSI and GPR in the short-term band.

This study happens to be the first of its kind to comprehensively consider how the aforementioned macro-economic shock indicators impact stock markets returns and volatilities over time and frequency. Further, none of the earlier studies has attempted to examine the relationship between macro-economic shocks, stock returns and volatilities in different crisis periods. This study is the first of its kind in to employ data spanning from May 2007 to April 2023, thereby covering notable crisis periods such as global financial crisis (GFC) and the COVID-19 pandemic episodes.

This paper aims to analyze the connectedness between Gulf Cooperation Council (GCC) stock market index and cryptocurrencies. It investigates the relevant impact of RavenPack COVID sentiment on the dynamic of stock market indices and conventional cryptocurrencies as well as their Islamic counterparts during the onset of the COVID-19 crisis.

The authors rely on the methodology of Diebold and Yilmaz (2012, 2014) to construct network-associated measures. Then, the wavelet coherence model was applied to explore co-movements between GCC stock markets, cryptocurrencies and RavenPack COVID sentiment. As a robustness check, the authors used the time-frequency connectedness developed by Barunik and Krehlik (2018) to verify the direction and scale connectedness among these markets.

The results illustrate the effect of COVID-19 on all cryptocurrency markets. The time variations of stock returns display stylized fact tails and volatility clustering for all return series. This stressful period increased investor pessimism and fears and generated negative emotions. The findings also highlight a high spillover of shocks between RavenPack COVID sentiment, Islamic and conventional stock return indices and cryptocurrencies. In addition, we find that RavenPack COVID sentiment is the main net transmitter of shocks for all conventional market indices and that most Islamic indices and cryptocurrencies are net receivers.

This study provides two main types of implications: On the one hand, it helps fund managers adjust the risk exposure of their portfolio by including stocks that significantly respond to COVID-19 sentiment and those that do not. On the other hand, the volatility mechanism and investor sentiment can be interesting for investors as it allows them to consider the dynamics of each market and thus optimize the asset portfolio allocation.

This finding suggests that the RavenPack COVID sentiment is a net transmitter of shocks. It is considered a prominent channel of shock spillovers during the health crisis, which confirms the behavioral contagion. This study also identifies the contribution of particular interest to fund managers and investors. In fact, it helps them design their portfolio strategy accordingly.

Climate change and its imminent threat to human survival adversely impact the agriculture sector. In an impoverished country like The Gambia, economic costs of climate change are colossal. This study aims to establish a computable general equilibrium (CGE) model for The Gambia’s agriculture sector to examine the effects of climate change on crops, livestock and sea-level rise.

This study used a CGE model with other climate change impact models to compute the impacts of climate change on The Gambia’s agriculture sector. The social accounting matrix calibrates the results from the various models, thereby generating the baseline results which exemplify a “steady-state” and policy shock results illustrating the medium- and long-term effects of climate change on the country’s agriculture sector.

The baseline results indicate the status quo showing the neglect of the agriculture sector due to limited investment in the sector. Hence, the sector is the “hardest hit” sector as a result of climate change. When the model factored in climate change in the medium term (2055) and long term (2085), the macroeconomic indicators of gross domestic product, national savings, wages, disposable income and consumer price index deteriorated, elucidating the vulnerability of the economy to climate change. The consumption of groundnuts, cattle and fish will decline by 5%, 5% and 4%, respectively, in the long term. However, the production of all agricultural commodities will decline by an average of 35% for the same period. The results for international trade show that exportation would decline while importation will increase over time. The general price level for agricultural commodities would increase by 3% in 2055 and 5% in 2085. Generally, the results manifest the severity of climate change in the agriculture sector which will have a multiplier effect on the economy. The impact of climate change would result in agriculture and economic decline causing hunger, poverty and human misery.

The caveat of this study revealed the nuances not captured by previous Gambian climate change studies, thus the novelty of the study.