The fixed and floating exchange rate systems exhibit essential differences. The paper attempts to provide empirical clarification concerning the type of exchange rate regime that has the most favourable impact on stock market returns and prices of the BRICS and African markets using the GLM regression method. The results are based on the inverse Gaussian functions and also identity and 1og functions of the estimated generalized linear model. The fixed exchange regime impacted adversely and significantly on the stock prices of both stock markets. The floating regime impacted favourably and significantly on the stock prices of the BRICS and African stock markets at the inverse Gaussian and Gamma identities. Whereas the fixed regime impacted adversely on stock returns of the BRICS markets at Gaussian identity, it impacted positively but rather insignificantly on the performance of stock returns of African markets. For African stock markets, both the floating and fixed regimes impacted stock returns positively, but the impact of the fixed regime is significant and also of a higher magnitude compared to that of the floating regime (17.313>10.885) HIV and Covid-19 deaths have significant inverse effects on stock prices of African stock markets. For the BRICS markets, the effect of Covid-19 on prices and returns was negative but insignificant. Only stock return effect of HIV was adverse and significant for the BRICS markets. The research findings will be useful for financial marketers involved in international financial trade and seeking to align with developments in international financial markets.

Системы фиксированного и плавающего валютного курса имеют существенные различия. В статье предпринята попытка эмпирического уточнения, какой тип режима обменного курса оказывает наиболее благоприятное влияние на доходность и цены фондовых рынков стран БРИКС и Африки, с использованием метода регрессии GLM. Результаты основаны на обратных гауссовых функциях, а также тождествах и 1og-функциях оцененной обобщенной линейной модели. Фиксированный валютный режим оказал негативное и значительное влияние на цены акций обоих фондовых рынков. Плавающий режим оказал благоприятное и значительное влияние на котировки акций фондовых рынков стран БРИКС и Африки при обратных тождествах Гаусса и Гаммы. В то время как фиксированный режим негативно повлиял на доходность акций рынков стран БРИКС при гауссовом тождестве, он оказал положительное, но довольно незначительное влияние на доходность акций африканских рынков. Для африканских фондовых рынков как плавающий, так и фиксированный режимы положительно повлияли на доходность акций, но влияние фиксированного режима оказалось значительным и большим по величине по сравнению с плавающим режимом (17,313>10,885) ВИЧ и смертность от Covid-19 оказывают существенное обратное влияние на котировки акций африканских фондовых рынков. Для рынков стран БРИКС влияние Covid-19 на цены и доходность было отрицательным, но незначительным. Только влияние ВИЧ на доходность акций было отрицательным и значимым для рынков БРИКС. Результаты исследования имеют большое значение для финансовых маркетологов, которые занимаются управлением своим финансовым бизнесом с целью соответствия изменениям в конкурентоспособности международных финансовых рынков.

Stock returns, stock prices, fixed regime, floating regime, Gaussian and Gamma identity

Доходность акций, цены акций, фиксированный режим, плавающий режим, изменение курсовой политики, доходность акций, цены акций, развитые и развивающиеся рынки, тождество Гаусса и Гаммы

According to the World Bank (2024), China is the biggest of the BRICS countries in terms of GDP, ranking second globally with the United States holding the top spot. China’s GDP was US$18 trillion in 2023. In 2023, India’s economy expanded by 7.6%, with a GDP of US$3.6 trillion, making it the world’s fifth largest economy. Also, in 2023, the economy of Brazil grew by 2.9% with a GDP of US$2.2 trillion. This makes it the ninth leading economy in the world. Russia’s GDP grew at the rate of 3.6% annually in 2022 to US$2 trillion, making it the eleventh largest economy. The smallest economy of the BRICS grouping is that of South Africa. As of 2023, South Africa commands a GDP of US$377.8, and it grew by 0.6% in the same year. The aforementioned countries aside from the US constitute the BRICS association. Africa now has a chance to deepen its connections with the BRICS developing economies thanks to the group’s 5^th summit, held in South Africa on March 26-27, 2013 and entitled BRICS and Africa: Partnerships for Integration and Industrialization. The summit aimed to unlock possibilities for engagement between BRICS and Africa.

The BRICS countries are currently the biggest trading partners of the majority of African nations and their significant foreign direct investment (FDI) source. It makes sense that trade between Africa and China increased substantially, reaching a peak of US$257.67 billion in overall trade in 2022 from $11.67 billion in 2000. Sadly, the global financial crisis of 2008, the COVID-19 epidemic, and the decline in commodity prices caused Africa’s persistent trade deficit that has grown to 2.6 billion dollars. The governments of Brazil and Russia have entered the mining and energy industries in Africa through joint ventures; India’s government has made significant efforts to support the growth of medium-sized enterprises throughout the continent. Compared to the stock markets of BRICS, African stock markets suffer substantial prices’ swings in their financial market trading and investing. Similar to African economies that most often depend on oil and commodity exports, the treasures of the BRICS countries are defined by their commodity exports. Direct stock purchases bring about African investors’ exposure to the BRICS business.

China maintained a peg for the Yuan from 1995 to 2005, after which it gradually moved towards liberalizing its currency policy by implementing a narrow trading band. Financial crises have directly and adversely affected many emerging market economies linked to the global financial markets (IMF, 2023a; Al-Sadiq et al. 2021) In 1997, Indonesia shifted to a floating exchange rate system and Thailand decided to adopt a managed-float system for the Baht, which aligns with inflation-targeting objectives. The Philippine government initiated the float of the peso against the US dollar almost 30 years previously, in 1970. South Korea allowed the Won to float freely against other currencies in 1990. Vietnam, on the other hand, has not yet moved to allow the Dong to float freely, citing as reasons the underdeveloped state of its financial market and concerns about the associated risks of foreign currency trade. Malaysia replaced the Malaysian Dollar with the Ringgit in 1993, transitioning from a free-float period until 1998 to a pegged era and eventually de-pegging from the dollar. Myanmar introduced a managed float for the Kyat in 2012 to protect the economy from various exchange rate regimes that could hinder economic growth. Singapore allowed the Singapore dollar to float freely, with the Monetary Authority of Singapore (MAS) monitoring its strength based on the Singapore dollar nominal effective exchange rate (S$NEER) Finally, Brunei operates a currency board system, effectively pegging its exchange rates (IMF, 2023b)

The study employs empirical methods to identify the type of exchange rate regime (fixed or floating) that has the most impact on stock market indices; for this purpose it uses the GLM regression. Hence the need to determine the impact of exchange rate policy regimes as a study variable on stock indices using the GLM before ascertaining the effects of specific regimes. The exchange rate regime adopted by any country is a key factor that determines the exchange rate, performance of the stock market and, ultimately, the welfare of citizens; it is therefore essential for policymakers to be aware of its possible impacts when making their decisions. The conventional standards governing the selection of an exchange rate regime primarily stem from the Optimal Currency Area (OCA) theory, which determines the choice between a fixed and a flexible exchange rate regime. The theory posits that countries with immobile capital and labour factors are more inclined to adopt a flexible exchange rate regime, whereas those characterised by mobile factors of production are more likely to opt for a fixed exchange rate regime. Similarly, Dąbrowski et al. (2024) argue that the degree of openness in the economy directly influences the preference for a fixed exchange rate regime. Sussman & Wyplosz (2024), however, suggest that it is the level of economic diversification which is the key factor influencing the choice of a fixed or flexible exchange rate regime.

Moreover, maintaining a fixed exchange rate regime necessitates a sufficient level of foreign exchange reserves, which is considered a crucial characteristic. Carvalho & D’Amato (2021) emphasize the role of capital mobility as a pivotal criterion in determining the optimal exchange rate regime and, to explicate the choice, introduces the concept of the impossible trinity. The impossible trinity, or trilemma theory, posits that it is unfeasible for a country to simultaneously achieve three objectives: a fixed exchange rate, independent monetary policy, and integrated financial markets. This framework categorises three scenarios, illustrating the constraints faced when balancing these three goals. Another factor that influences the choice of an exchange rate regime is the nature of shocks. A contemporary version of the OCA theory emphasises the impact and nature of shocks, indicating their pivotal role in determining the appropriate exchange rate regime.

Although the OCA theory primarily addresses countries within the same region sharing a single currency like the European Union with the Euro, it is also relevant for the present study because it focuses on the interconnectedness of the stock markets across countries, and this paper examines the connections among the markets of developing and developed countries. Countries with fixed exchange rates or common currency arrangements surrender control over their exchange rates to non-market regulation, which prevents them from adjusting independently to economic shocks but, presumably, leads to a relative stability. This stability can boost investor confidence and reduce business risk contributing to a more predictable, or less volatile, stock market environment. The opposite will be true if floating regimes are adopted. The OCA theory is extended in its application by integrating stock market investments and exchange rate regimes that influence exchange rate fluctuations in association with their sensitivity to macroeconomic health concerns, which are capable of causing disruptions in the countries involved (Buiter, 2000) Mundell (Miteza et al. 2023) has shown that a flexible exchange rate regime is more suitable for managing real shocks, whereas a fixed exchange rate regime is better equipped to handle nominal shocks.

The study hypothesizes the following: neither the shifts in exchange rate policy regimes, nor infections or deaths from HIV/AIDS and Covid-19 pandemics produced significant effects on the stock prices and capital gains in the BRICS and African stock markets. Assessing the impact of a particular system on financial performance has become most useful for managing the financial market for the purpose of aligning with advancements in international financial market competitiveness. The selection of an appropriate exchange rate regime also requires the ability to foresee how it will impact the level of exchange rate instability and the effect of such instability on the overall macroeconomic performance. Section Two gives an overview of theoretical and empirical literature, emphasizing the relevant concepts and gap analysis. Section three discusses the paper’s theoretical framework, model specification, and sources of data. Section four presents the outcomes of the empirical research. The final section summarizes the research findings, formulates policy recommendations, and defines the scope of further study.

Literature Review

The optimal currency area (OCA) theory provides theoretical justification for the present research. Within a fixed exchange rate system, government intervention assumes a pivotal role in maintaining exchange rate stability, contingent upon the maintenance of robust foreign reserves. In contrast, the floating exchange rate system operates with unrestricted autonomy, devoid of government oversight. Exchange rate fluctuation, characterised by appreciation or depreciation, serves as a hallmark of this inherent instability. The exchange rate oscillations introduce a dimension of risk, indicative of the extent to which a currency’s value can undergo substantial fluctuations. Heightened volatility implies greater potential for significant abrupt currency price shifts over short periods of time. Conversely, lower volatility signals a more stable scenario where currencies undergo gradual fluctuations (Nguyen et al. 2024) The choice of an exchange rate regime has a profound impact on a nation’s political and economic landscape. It ultimately determines the approach and mechanics of macroeconomic adjustments (Al-Sadiq et al., 2021)

African stock markets were not vulnerable to the pandemic’s spreading effect, according to Owusu et al. (2024) Gbadebo (2023) established the negative impact of exchange rate fluctuation on Nigerian stock returns, market cap, and volume. Erhijakpor & Diebogheneruo (2023) showed that exchange rate swings have a minimal negative impact on the volatility of the All Share Index over the short and long terms. Agyei et al. (2022) conducted a study to examine the co-movement of exchange rate returns and stock returns in Africa amid Covid-19 in a time-frequency domain. They employed the bi and partial wavelet and wavelet multiple correlation techniques using daily data from February 13, 2013 to May 6, 2021. They found out that the Covid-19 effect did not increase the intensity of the relationship between EXR and STK returns in Africa but caused a significant difference in the lead-lag relationship between the two assets. They also found a strong likelihood of high market integration between African markets in the long run, regardless of markets’ conditions and behaviours, South African (Namibian) equities having the lead lag potential in the short run.

In Bizuneh’s study (2022), the author examines the duration of fixed exchange rate regimes and identifies the factors that affect the likelihood of moving away from a pegged exchange rate. Using a de facto exchange rate regime classification, the paper comes to several conclusions. First, it discovers that the duration of a pegged exchange rate is non-monotonic, meaning it is not consistently increasing or decreasing over time. Second, the researcher’s semi-parametric proportional hazard model reveals that factors such as GDP growth and economic openness are associated with a reduced likelihood of exiting a pegged exchange rate, while rising unemployment and an increase in government claims can heighten the probability of abandoning the peg. The study also points out that the negative impact of economic growth on the hazard rate remains robust when using marginal risk analysis. Factors such as net foreign assets and inflation are found to exert influence on the duration of pegged regimes. Overall, the findings suggest that various factors, including GDP growth, unemployment rate, economic openness, and government budget deficits, play a crucial role in determining the likelihood of a transition from a fixed to flexible exchange rate regime. Economic growth continues to be a significant covariate, even when analysed through marginal risk analysis, alongside the variables of net foreign assets and inflation rate.

In their research into the effects of the pandemics outbreak on stock markets in developed countries, Umar et al. (2022) analysed the impact of Covid-19 on the stock market liquidity of China and the four worst-hit countries. Using daily data for the stock market liquidity spanning from July 1, 2019 to July 10, 2020, and the data for new cases and deaths over the period from December 31, 2019 to July 2020, the GARCH analysis shows that liquidity in the stock markets of all sampled countries was hit hard by the news of the Covid-19 outbreak. They found that for all sampled countries, the increase in liquidity due to temporary shocks reverted to a long-term trend shortly, suggesting that the liquidity shocks due to the incidence of Covid-19 were short-lived. The findings of the VAR analysis show an absence of any short-term relationship between COVID-19 new cases or deaths and liquidity. It was concluded that, since the series are not integrated at the same level, a long-term relationship between Covid-19 and stock market liquidity did not exist, suggesting no evidence of the effect of Covid-19 on stock market liquidity under a high stress regime. They found that the Covid-19 pandemic had a weak impact on Chinese and Italian national currencies but could negatively influence stock market prices. Marozva & Magwedere (2021) concluded that there existed a positive relationship between stock market liquidity and the incidence of the Covid-19 pandemic. Gubareva (2021) shows that despite improvements owing to the bailout packages, liquidity in the bond market has not returned to the pre-Covid-19 level. She also reported a decoupling in the dynamics of credit risk and liquidity risk metrics.

At the individual country level, Takyi & Bentum-Ennin (2020) revealed that the Covid-19 plague had a major and negative impact on the stock markets of ten different countries.

Takyi & Bentum-Ennin (2020) empirical estimates showed that the Covid-19 virus had had a large and negative impact on the stock markets of ten different countries. Vengesai (2022) discovered that Covid-19 had raised return volatility for most sectors using the MDCC-GARCH model. As demonstrated by the findings of Kumeka et al. (2021), the foreign currency market and fluctuating commodity prices had an impact on African stock markets. Khalid et al. (2021) examined the stock market worst affected by Covid-19 among GCC (Gulf Cooperation Council) stock markets and extracted factors that increased the adverse effects. The analytical results show that Bahrain was most affected by the Covid-19 pandemic, with 47351 confirmed cases per million population. They built a forecasting model using an artificial neutral network to establish how the virus would continue spreading until the end of May 2020. The results showed that the Kuwait stock market was the most affected by the pandemic. Also, research around Ebola outbreak events throughout 2014-2016 revealed that US companies with exposure to their operations in West African countries were experiencing negative returns and increased volatility because of those events. Those Ebola-related events affected investors’ perceived risk (Silverstone, 2021)

For all companies listed on the Shanghai Stock Exchange and Shenzhen Stock Exchange in China, Maquieira & Espinosa (2022) revealed a more pronounced herd behaviour tendency when market return is high and volatility is low. In another, relatively broader study, Liu et al. (2020) investigated the impact of Covid-19 on the world’s 21 leading stock exchanges. Their findings suggest that the stock markets across the globe responded immediately to the threat of COVID-19 and fell across the board. Muigai & Cherono (2019) used a survey of all 61 publicly traded companies in Kenya as well as a follow-up study approach. According to the study’s findings, share prices were significantly impacted negatively by exchange rate fluctuation. Moagăr-Poladian et al. (2019) studied the co-movement of exchange rates and stock markets in Central and Eastern Europe simultaneously at the cross-country level. In their study, they employed a dynamic conditional correlation mixed data sampling (DCC-MIDAS) model. The study reveals significant differences between the patterns of correlation in the in the sampled countries. First, the study finds quite a low degree of convergence between foreign exchange markets, with rising correlations during some episodes of the crisis. Secondly, both the 2004 European Union enlargement and the European sovereign debt crisis underpin the stock market co-movements in the central and eastern European countries. Thirdly, the correlations between exchange rate returns and stock markets rose mostly during the European sovereign debt crisis and, to a lesser extent, during the global financial crisis, revealing signs of contagion and lower portfolio diversification opportunities. Nagmi (2019) examined the impact of exchange rate volatility on stock prices. The GARCH model and Granger causality test were applied. The results of the analysis show strong evidence of causation running from exchange rate to stock prices, implying several variations, such as Dubai stock prices experiencing exchange rate volatility while the Saudi stock market witnessed a unidirectional relationship running from stock prices to exchange rate, implying that variations in the Saudi exchange rate determined the stock price volatility.

In sum, the relationship between variables as described in previous literature was between one dependent and one independent variables. The present research combines many variables, which include: the fixed exchange rate system, floating exchange rate system, number of infections and deaths from HIV/AIDS and Covid-19 viruses (as control variables), stock prices and stock returns of BRICS markets and stock markets of selected African countries. It should be noted that serious work on exchange rate regimes and stock markets, as well as on pandemics, has been done in a few developing and more developed countries but the scope of research has been comparatively small. The present study focused on the emerging markets of Africa/ BRICS countries, expanded the number of these markets and also increased the data range from 1995 to 2024.

This research is a comparative analysis of the impact of various fixed and floating exchange rate regimes, HIV infections and the Covid-19 pandemic on the financial market in developing and wealthy nations between 1995 and 2024. It uses the Bovespa Brazil stock market index, MOEX Russia index, S&P BSE Sensex index of India, Shanghai stock market index of China, and FTSE/JSE All Share Index of South Africa, i.e. the stock market indices of the developing BRICS markets. Its sample of African stock market indices includes the NSEASI of Nairobi, the CFG 25 market index of Casablanca, Morocco, the EGX 30 index traded on the Egyptian Exchange, the All-Share index (ASI) of the Nigerian Stock Exchange, and the Stoxx index of Tunisia. To determine the regime that has greater influence on stock market performance, it is necessary to gauge the separate effects of exchange rate regimes on stock prices and stock returns of BRICS and stock markets of the developing African countries; for the same purpose, the exchange rate policy regime has been unbundled into the globally practiced fixed and floating ones.

We executed the generalized linear model to accommodate the interactions between dummy and other continuous variables used to determine the unique impacts of the exchange rate regime (floating and fixed) on stock prices and stock returns. When the coefficients of the models are positive, it reveals that the predictor variable enhances the performance of the response variable (SPP, SRR) Contrarily, a negative coefficient of a predictor variable (exchange rate regime: floating and fixed) dampens the performance of the response variables (SPP, SRR) In what follows, we have the generalized linear model (GLM) specification given as:

SPP = ∅ = f (X’β) + ε (1)

SRR = ∅ = f (X’β) + ε (2)

f = g^–1 (3)

Based on the Gaussian family GLM model with identity link function, the canonical parameter transformation ∅ the variance of outcome variable is modelled as in equation (4)

E (Stock Price|X) = ∅ = g^–1 (Xβ) (4)

E (Stock Return|X) = ∅ = g^–1 (Xβ) (4!)

where, X = design matrix of regressors; X’β = linear predictor, ∅ is the fitted values transforming linear predictor; β is estimated vector of coefficients with maximum likelihood; Y is the dependent variable (stock returns (SR)/stock prices (SP)) conditional on the vector of predictor variables denoted as X; E (Y|X) is the expected value of Y conditional on values of X; g is the link function. The mean of the response variable is modelled as in equation (5)

∅(SPP_ij) = f (X’β) + ε (5)

∅(SRRij) = f (X’β) + ε (5!)

where: X’ =

g (E (SPP_d|X’) = g (∅)R = β_0d + β_1dERR_d + β_2dHIV_d + β_3dCOVCASES_d + β_4dCOVDEATHS_d (6)

g (E (SRR_d|X’) = g (∅) = β_0d + β1dERR_d + β_2dHVV_d + β_3dCOVCASES_d + β_4dCOVDEATHS_d (7)

g (E (SPP_d|X’) = g (∅) = β_0u + β_1uERR_u + β_2uHVV_u + β_3uCOVCASES_u + β_4uCOVDEATHS_u (8)

g (E (SRR_u|X’) = g (∅) = β_0u + β_1uERR_u + β_2uHVV_u + β_3uCOVCASES_u + β_4uCOVDEATHS_u (9)

By definition, g (E (Stock Price_d|X’) is the expected value of stock indices conditional on the values of exchange rate regimes and pandemics namely: (HVV, COVCASES and COVDEATHS), β_0,τ is the constant term of the τ-thquantile; β_1,τ to β_4,τ are coefficients of independent variables at the τ^th quantile; d = BRICS countries; u = African countries. Accordingly, under the Gaussian (Normal) family GLM model with Identity link, the means and variances of the response variables are as specified:

E (SPP_d) = ∅ = β_0d + β_1dERR_d + β_2dHVV_d + β_3dCOVCASES_d + β_4dCOVDEATHS_d (10)

E (SPP_u) = η = ∅ = β_0u + β_1uERR_u + β_2uHVV_u + β_3uCOVCASES_u + β_4uCOVDEATHS_u (11)

E (SRR_u) = η = ∅ = β_0d + β_1dERR_d + β_2dHVV_d + β_3dCOVCASES_d + β_4dCOVDEATHS_d (12)

E (SRR_u) = η = ∅ = β_0u + β_1uERR_u + β_2uHVV_u + β_3uCOVCASES_u + β_4uCOVDEATHS_u (13)

where: η is the linear predictor, β₀ is the intercept, and β₁ – β₄ are coefficients for the independent variables. The variances of response variables are as specified here:

Var (SPP_d) = σ*1 (14)

Var (SPP_u) = σ*1 (15)

Var (SRR_d) = σ*1 (16)

Var (SRR_u) = σ*1 (17)

where: ∅ is a dispersion parameter that needs to be estimated from the data, which describes the relationship between the mean and variance of the response variable. Hence, for the Gaussian (Normal) family GLM model with log link, the mean of the response variables are:

E (SPP_d) = ∅ = exp(β_0d + β_1dERR_d + β_2dHVV_d + + β_3dCOVCASES_d + β_4dCOVDEATHS_d) (18)

E (SPP_u) = ∅ = exp(β_0u + β_1uERR_u + β_2uHVV_u + + β_3uCOVCASES_u + β_4uCOVDEATHS_u) (19)

E (SRR_d) = ∅ = exp(β_0d + β_1dERR_d + β_2dHVV_d + + β_3dCOVCASES_d + β_4dCOVDEATHS_d) (18!)

E (SRR_u) = ∅ = exp(β_0u + β_1uERR_u + β_2dHVV_u + + β_3uCOVCASES_u + β_4uCOVDEATHS_u) (19!)

where: η is the linear predictor, β₀ is the intercept, and β₁ – β₄ are coefficients for the independent variables. The variances of response variables are:

Var(SRR_d) = σ*1 (20)

Var(SRR_u) = σ*1 (21)

With the inverse Gaussian family GLM model with identity link, the means of the response variables are:

E (SPP_d) = ∅^–2 = η = β_0d + β_1dERR_d + + β_2dHVV_d + β_3dCOVCASES_d + β_4dCOVDEATHS_d (22)

E (SPP_u) = ∅^–2 = η = β_0u + β_1uERR_u + β_2uHVV_u + +β_3uCOVCASES_d + β_4uCOVDEATHS_u (23)

E (SRR_d) = ∅^–2 = η = β_0d + β_1dERR_d + β_2dHVV_d + + β_3dCOVCASES_d + β_4dCOVDEATHS_d (22!)

E (SRR_u) = ∅^–2 = η = β_0u + β_1uERR_u + β_2uHVV_u + + β_3uCOVCASES_u + β_4uCOVDEATHS_u (13!)

where: 𝜂 is the link or linear predictor, 𝛽₀​ is the intercept, and 𝛽₁-𝛽₄ are coefficients for the independent variables. Accordingly, the variances of response variables are:

Var (SPP_d) = σ∅² (24)

Var (SPP_u) = σ∅² (25)

Var (SRR_d) = σ∅² (24!)

Var (SRR_u) = σ∅² (25!)

Basing specifications on the inverse Gaussian family GLM model with log link, the means of the response variables are specified as follows:

E (SPP_d) = ∅^–2 = η = exp(β_0d + β_1dERR_d + β_2dHVV_d + β_3dCOVCASES_d + β_4dCOVDEATHS_d) (26)

E (SPP_u) = ∅^–2 = η = exp(β_0u + β_1uERR_u + β_2uHVV_u + β_3uCOVCASES_u + β_4uCOVDEATHS_u) (27)

E (SRR_d) = ∅^–2 = η = exp(β_0d + β_1dERR_d + β_2dHVV_d + β_3dCOVCASES_d + β_4dCOVDEATHS_d) (26!)

E (SRR_u) = ∅^–2 = η = exp(β_0u + β_1uERR_u + β_2uHVV_u + β_3uCOVCASES_u + β_4uCOVDEATHS_u) (27!)

where η is the link or linear predictor, β₀ is the intercept, and β₁ – β₄ are coefficients for the independent variables. Accordingly, the variances of response variables are given in equations (28) and (29) respectively.

Var (SPP_d) = σ∅² (28)

Var (SPP_u) = σ∅² (29)

With the Gamma family GLM model with identity link, linear predictor of the response variables given by:

E (SPP_d) = η = β_0d + β_1dERR_d + β_2dHVV_d + β_3dCOVCASES_d + β_4dCOVDEATHS_d (30)

E (SPP_u) = η = β_0u + β_1uERR_u + β_2uHVV_u + β_3uCOVCASES_u + β_4uCOVDEATHS_u (31)

Here, η is the linear predictor, β₀ is the intercept, and β₁ – β₄ are coefficients for the independent variables. The means of the response variables are given by equations (32) and (33):

E (SRR_d) = ∅^–1 = η = β_0d + β_1dERR_d + β_2dHVV_d + + β_3dCOVCASES_d + β_4dCOVDEATHS_d (32)

E (SRR_u) = ∅^–1 = η = β_0u + β_1uERR_u + β_2uHVV_u + + β_3uCOVCASES_u + β_4uCOVDEATHS_u (33)

Consequently, the variances of response variables are given in equations (34) and (35) respectively.

Var (SPP_d) = σ∅² (34)

Var (SPP_u) = σ∅² (35)

Var (SRR_d) = σ∅² (34!)

Var (SRR_u) = σ∅² (35!)

Data for the study were mostly downloaded from the World Bank database. Table 1 provides the clue to variables definitions and measurements.

A period of 1990-2023 was covered to reveal a reasonable trend in the analysis. After retrieval of data from respective databases as specified in the previous section, data were fed into statistical calculators following specific econometric techniques to investigate the relationship that exists across exchange rate regimes, pandemics, and financial market indicators.

There is no significant effect of the shifts in exchange rate policy regime and infections or deaths from HIV/AIDS and Covid-19 pandemics on the stock prices and returns of BRICS and African stock markets. With p values less than the study’s adopted level of significance, the null hypothesis is rejected, while the alternative hypothesis is accepted: there is a significant effect of the shift in exchange rate policy regime on stock prices in the BRICS stock markets. Conversely, the hypothesis of a significant effect of infections/deaths from HIV/AIDS and Covid-19 pandemics on stock prices in BRICS stock markets is rejected. The estimated results also confirmed a notable impact of shifts in exchange rate policy regimes and occurrences of infections and deaths from HIV/AIDS and Covid-19 pandemics on stock prices in African markets. Covid-19 cases impacted negatively on stock prices but the impact was not substantial. Additionally, the results confirm that the shifts in exchange rate policy regime and infections and deaths from HIV/AIDS and COVID-19 pandemics significantly affected the stock returns of African stock markets (p > 0.05)

The study attempted to find out empirically, which types of exchange rate regime most favorably impact the stock prices and returns across emerging BRICS and African stock markets given the outbreak of pandemics. The pandemics considered in this study were HIV/AIDS and the Covid pandemic. The generalized linear model estimation was executed. The presented findings highlight the importance of considering both fixed and floating exchange rate regimes and pandemics as a critical factor affecting stock price and return movements. The generalized linear model estimations show that the exchange rate policy regime shift had a favourable impact on stock prices and stock returns in BRICS and African stock markets. The impact, however, was found to be significant only for the stock prices. The floating regime impacted favourably and significantly on the stock prices of BRICS and African stock markets. In terms of stock returns, floating had a favourable but insignificant impact. HIV and Covid-19 deaths have significant inverse effects on stock prices of African stock markets. The effect of Covid-19 on prices and returns was negative but insignificant for the BRICS markets, while the effect of HIV on stock returns was significant and adverse. Both BRICS and African countries should adopt exchange rate regimes that are appropriate for their countries and avoid unwarranted shifts in exchange rate policy as these may have undesirable impact on stock prices and stock returns.