Felix Okoe Mettle

Climate change presents a cosmopolitan challenge with far-reaching macroeconomic implications, demanding immediate action. Thus, this study sought to offer a statistical analysis exploring the macroeconomic effects of climate change in Ghana using monthly observations from 2000 to 2023. By utilizing Canonical Correlation Analysis (CCA), the study examined the statistical relationship between climate variables (represented by Global CO2 levels, temperature, rainfall, and humidity obtained from GMet’s 22 monitored synopsis stations) and key macroeconomic indicators (including economic growth rates, inflation rates, real effective exchange rates, and interest rates). The short-run and long-term impacts of climate change on these economic variables were assessed by applying Vector Autoregression (VAR). The observed pattern among the examined economic and climate metrics suggests a symbiotic influence, where fluctuations in these variables have interconnected impacts. Notably, the VAR analysis results disclosed that a 1% increase in relative humidity in Ada and a unit rise in temperature across Abetifi, Bole, Kumasi, and Tema weather stations are associated with adverse effects on certain macroeconomic indicators, particularly immediate and long-term interest rates and inflation rates, albeit with some degree of uncertainty. The study advises that strengthening Ghana’s economic resilience to climate change requires targeted investments in climate-smart agriculture and infrastructure, particularly in vulnerable communities. Prioritizing smallholder farmers through adaptation financing and insurance is critical. Integrating climate risk into monetary and fiscal policy, alongside improved data systems and research, will support responsive and inclusive planning.

The Clayton and Joe copulas serve as mathematical constructs utilised within copula theory to explicate the interrelationships among stochastic variables. Renowned for their efficacy in capturing extreme tail occurrences, these copulas are recognised members of the Archimedean copula family. Their utility spans diverse domains: genetics, neuroscience, finance, insurance, hydrology, environmental research, telecommunications, and reliability engineering. To augment their flexibility, we present in this discourse an elevated mixture of the Clayton and Joe copulas, denoted as the CJM copula. This convex mixture offers a direct linear interpolation between the Clayton and Joe copulas, modulated by a blending parameter. Such integration facilitates a more nuanced depiction of the copula density function. We scrutinise its attributes, including determining the blending parameter, Kendall’s tau, and quadrant-dependent coefficients, while introducing three innovative copulas: the x-flipping CJM, y-flipping CJM, and survival CJM copulas. The collective discoveries significantly contribute to advancing the theoretical underpinnings governing copula-based modelling methodologies and their ensuing applications.

Climate change presents a cosmopolitan challenge with farreaching macroeconomic implications, demanding immediate action. Thus, this study sought to offer a statistical analysis exploring the macroeconomic effects of climate change in Ghana using monthly observations from 2000 to 2023. By utilizing Canonical Correlation Analysis (CCA), the study examined the statistical relationship between climate variables (represented by Global CO2 levels, temperature, rainfall, and humidity obtained from GMet's 22 monitored synopsis stations) and key macroeconomic indicators (real economic growth rates, inflation rates, real effective exchange rates, and interest rates). The short-run and long-term impacts of climate change on these variables were assessed by applying Vector Autoregression (VAR). The observed pattern among the examined economic and climate metrics suggests a symbiotic influence, where fluctuations in these variables have interconnected impacts. Notably, the VAR analysis results disclosed that a 1% increase in relative humidity in Ada and a unit rise in temperature across Abetifi, Bole, Kumasi, and Tema weather stations are associated with adverse effects on certain macroeconomic indicators, particularly immediate and long-term interest rates and inflation rates, albeit with some degree of uncertainty. The study advises that strengthening Ghana's economic resilience to climate change requires targeted investments in climate-smart agriculture and infrastructure, particularly in vulnerable communities. Prioritizing smallholder farmers through adaptation financing and insurance is critical. Integrating climate risk into monetary and fiscal policy, alongside improved data systems and research, will support responsive and inclusive planning.

Modeling financial data often relies on assumptions that may prove insufficient or unrealistic in practice. The Geometric Brownian Motion (GBM) model is frequently employed to represent stock price processes. This study investigates whether the behavior of weekly and monthly returns of selected equities listed on the Ghana Stock Exchange conforms to the GBM model. Parameters of the GBM model were estimated for five equities, and forecasts were generated for three months. Evaluation of estimation accuracy was conducted using mean square error (MSE). Results indicate that the expected prices from the modeled equities closely align with actual stock prices observed on the Exchange. Furthermore, while some deviations were observed, the actual prices consistently fell within the estimated confidence intervals.

The main objective of this paper is to analyse investment returns using a stochastic model and inform investors about the best stock market to invest in. To this effect, a Markov chain random walk model was successfully developed and implemented on 450 monthly market returns data spanning from January 1976 to December 2020 for Canada, India, Mexico, South Africa, and Switzerland obtained from the Federal Reserves of the Bank of St. Louis. The limiting state probabilities and six-month moving crush probabilities were estimated for each country, and these were used to assess the performance of the markets. The Mexican market was observed to have the least probabilities for all the negative states, while the Indian market recorded the largest limiting probabilities. In the case of positive states, the Mexican market recorded the highest limiting probabilities, while the Indian market recorded the lowest limiting probabilities. The results showed that the Mexican market performed better than the others over the study period, whilst India performed poorly. These findings provide crucial information for market regulators and investors in setting regulations and decision-making in investment.

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.

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.

Irrespective of whether the test for homogeneity is significant or not, most researchers assume time-homogeneity in analysing Markov chains due to scanty literature on the analysis of time-inhomogeneous Markov chains. Based on the assumption that, for each point in time in the future, a stochastic process will be subjected to a randomly selected transition matrix from an ergodic set of transition matrices the process was subjected to in the recent past, a methodology was proposed for analysing the long-run behaviours of time-inhomogeneous Markov chains. The proposed model was implemented to historical data consisting of the exchange rate of cedi-dollar, cedi-pound, and cedi-euro spanning over 6 years (January 2012 to December 2017). The results show that under certain “closeness” conditions, the long-run behaviours of the time-inhomogeneous case are almost identical to those of the time-homogeneous case. The paper asserted that even if the Markov chain exhibit time-inhomogeneity, analysing the Markov chain under the assumption of time-homogeneity is a step in the right direction under certain “closeness” conditions; otherwise, the proposed method is recommended. It was also found that investing in dollars yields better returns than the other currencies in Ghana.