In this paper, we apply Markov chain techniques to select the best financial stocks listed on the Ghana Stock Exchange based on the mean recurrent times and steady-state distribution for investment and portfolio construction. Weekly stock prices from Ghana Stock Exchange spanning January 2017 to December 2020 was used for the study. A three-state Markov chain was used to estimate the transition matrix, long-run probabilities, and mean recurrent times for stock price movements from one state to another. Generally, the results revealed that the long-run distribution of the stock prices showed that the constant state recorded the highest probabilities as compared to the point loss and point gain states. However, the results showed that the mean recurrent time to the point gain state ranges from three weeks to thirty-five weeks approximately. Finally, Standard Chartered Bank, GCB, Ecobank, and Cal Bank emerged as the top best performing stocks with respect to the mean recurrent times and steady-state distribution, and therefore, these equities should be considered when constructing asset portfolios for higher returns.

This study examines the relationship between environmental quality and unemployment in Ghana using annual data spanning the period from 1990 to 2019. It also assesses the impact of gender-segregated unemployment rate on environmental quality. The study employed the Autoregressive Distributive Lag (ARDL) error correction model to estimate the relationship among the variables. In addition, the Fully Modified Ordinary Least Squares (FMOLS) and the Dynamic Ordinary Least Squares (DOLS) estimation procedures were employed to check for robustness of the ARDL results. Findings indicate a positive effect of total unemployment rate on environmental quality in Ghana in the long-run and also in the short-run. In the case of the gender-segregated unemployment, the findings reveal that in both short-run and long-run, a rise in female unemployment causes a deterioration in environmental quality in Ghana. The results also validated the Environmental Phillips Curve (EPC) hypothesis in the case of male unemployment. Thus, given that there is no general pattern in the findings, the study concludes that the Environmental Phillips Curve (EPC) hypothesis does not hold within the Ghanaian context.

This study investigates whether there are significant differences in investment returns between emerging markets and industrialized markets in terms of stochastic seasonality, January effect and market efficiency. Data on investments, and returns for nine emerging countries and eleven industrialized countries spanning January 1990 to December 2020, were obtained from the Organization for Economic Cooperation and Development (OECD). The spectral nonparametric test was used to determine the presence of stochastic seasonality for each market while the regression test was used to determine the presence of January effect. In the case of determining the efficiency status of the markets, the variance ratio test and the runs test were used. In cases where there appeared to be differences between the two types of market, Fisher's exact test was used to verify such differences. The results show no significant differences between the two types of markets in terms of seasonality, January effect and efficiency statuses. Apart from Brazil which recorded stochastic seasonality, all others are not stochastically seasonal. In the case of the January effect, it was a mixed bag; five emerging markets had January effect while two industrialized markets had January effect.

Non-Pharmaceutical Interventions (NPI) are used in public health to mitigate the risk and impact of epidemics or pandemics in the absence of medical or pharmaceutical solutions. Prior to the release of vaccines, COVID-19 control solely depended on NPIs. The Government of Ghana after assessing early NPIs introduced at the early stage of the pandemic began to ease some restrictions by the opening of international borders with isolation and quarantine measures enforced. It was argued by some experts that this was a hasty decision. In this study, we assessed the impact of the opening of borders to ascertain if this action caused a surge or otherwise in cases in the country. Using data from the database on Africa’s records of COVID-19 from the John Hopkins University, the Generalized Linear Model (GLM) time-series regression model for count data was applied to study effects in Ghana during a 4-month and 8-month period post-opening of borders. The study showed that after the decision of the government to open international borders, Ghana's expected case count declined by $72.01\%$ in the 4-month period and $54.44\%$ in the 8-month period. This gives an indication of the gradual reversal of the gains made due to the early implementation of NPIs. Notably, this may not only be attributed to the opening of borders but the relaxation of the strict enforcement measures that were put in place at the onset of the pandemic in Ghana. There is therefore the need for continuous enforcement of intervention measures to reduce case counts, particularly with the emergence of new COVID-19 virus strains. The study provides some recommendations for policy and improvements in model building such as developing better data collection system in Ghana, investigating more control variables, estimating the decaying effect of interventions, and ensuring better preparations prior to easing of public health restrictions.

The main objective of this study is to propose a method of analysing the volatility of a seemingly random walk time series with correlated errors without transforming the series as performed traditionally. The proposed method involves the computation of moving volatilities based on sliding and cumulative data matrices. Our method rests on the assumption that the number of subperiods for which the series is available is the same for all periods and on the assumption that the series observations in each subperiod for all the periods under consideration are a random sample from a particular distribution. The method was successfully implemented on a simulated dataset. A paired sample -test, Wilcoxon signed rank test, repeated measures (ANOVA), and Friedman tests were used to compare the volatilities of the traditional method and the proposed method under both sliding and cumulative data matrices. It was found that the differences among the average volatilities of the traditional method and sliding and cumulative matrix methods were insignificant for the simulated series that follow the random walk theorem. The implementation of the method on exchange rates for Canada, China, South Africa, and Switzerland resulted in adjudging South Africa to have the highest fluctuating exchange rates and hence the most unstable economy.