




































American Interdisciplinary Journal of Business 

and Economics 
ISSN: 2837-1909| Impact Factor : 6.71 

Volume. 11, Number 3; July-September, 2024; 

Published By: Scientific and Academic Development Institute (SADI) 

8933 Willis Ave Los Angeles, California 

https://sadijournals.org/index.php/AIJBE| editorial@sadijournals.org 

 

 

17 
American Interdisciplinary Journal of Business and Economics | 

https://sadijournals.org/index.php/AIJBE 

 

GLOBAL ECONOMIC POLICY UNCERTAINTY, OIL SHOCKS, AND 

VOLATILITY IN SELECTED SOUTHERN AFRICAN DEVELOPMENT 

COMMUNITY STOCK MARKETS: A GARCH-MIDAS APPROACH 

 

David M. Reynolds 

Department of Economics, Faculty of Social Sciences, University of Nairobi, PO Box 30197, Nairobi, Kenya. 

DOI: https://doi.org/10.5281/zenodo.12799663 

Abstract: This study investigates the effects of global economic policy uncertainty and oil shocks on stock 

market volatility in Botswana, Mauritius, and South Africa. Datasets from periods preceding and during the 

COVID-19 pandemic are utilized to provide evidence on the impact of global economic policy uncertainty 

(GEPU) and oil shocks on stock returns volatility in these countries. The examination employs a mixed data 

sampling model based on generalized autoregressive conditional heteroskedasticity (GARCH-MIDAS). The 

GARCH-MIDAS approach allows for combining high-frequency stock data with low-frequency GEPU and 

oil shock data to forecast the long-term component of volatility. Additionally, this method demonstrates a 

better fit for that relationship when compared to traditional GARCH. The results indicate that both GEPU and 

oil consumption demand shocks have positive and significant impacts on stock volatility for the three countries 

in our in-sample case (which corresponds to the period before the COVID-19 pandemic). The volatility 

coefficient estimates for Botswana, Mauritius, and South Africa are 0.076, 0.001 and 0.119, respectively, all 

significant at the 1% level. This suggests that stock returns in these countries react positively to changes in oil 

demand shocks. Forecasting data during the COVID-19 period also shows that incorporating global economic 

policy uncertainty and oil shocks using a GARCH-MIDAS approach improves forecasting accuracy. The 

application of the GARCH-MIDAS approach in this study facilitates the separation of short-term and long-

term volatility components effectively, thus enabling us to address a significant shortfall of previous research 

that has explored the impact of economic policy uncertainty on stock market returns.  

Keywords: Global economic policy uncertainty, oil consumption demand shock, generalized autoregressive 

conditional heteroskedasticity (GARCH-MIDAS), Southern African Development Community (SADC) 

countries, stock market volatility.  

 

INTRODUCTION  

The COVID-19 pandemic has raised questions about its Community (SADC) markets. The seminal paper by 

impact on the correlation between global economic policy Baker et al. (2016) defines economic policy uncertainty 

uncertainty (GEPU) and the volatility of stock market (EPU) as uncertainty arising from fiscal, monetary, or 

returns within   specific   Southern African Development regulatory   policy,   with their EPU index calculated by 



David M. Reynolds (2024) 
 

18 
American Interdisciplinary Journal of Business and Economics | 

https://sadijournals.org/index.php/AIJBE 

 

assessing the relative frequency of terms pertaining to economics (E), politics (P), and uncertainty (U). 

Subsequently, numerous studies have investigated the correlation between EPU and the stock market (Sharif et 

al., 2020; Yu et al., 2021). Consequently, numerous studies have explored the correlation between these two 

variables from various perspectives and employing a range of methodologies. Practitioners such as Ko and Lee 

(2015), Wu et al. (2015), Christou et al. (2017a), Cheng (2017), Phan et al. (2018), Mei et al. (2018), Xiong et al. 

(2018), and Yu et al. (2018) showcase pertinent research in this regard. The initial segment of the literature 

concentrates on the impacts of Economic Policy Uncertainty on stock markets. More specifically, Wu et al. (2016) 

employ a panel Granger causality method to evaluate the influence of EPU on stock markets across eight OECD 

nations, comprising India, Italy, Spain, the UK, Canada, France, Germany, the United States, and China. Christou 

et al. (2017b) employ a Bayesian panel vector autoregression model to investigate the impact of US EPU shock 

on the stock market returns of Pacific Basin countries, such as Australia, Canada, China, Japan, Korea, and the 

US. Additionally, their research explores the effects of EPU on other financial markets. For instance, Fang et al. 

(2018) conducted research on the futures market, while Demir and Ersan (2017) focused on the currency market, 

and Reboredo and Naifar (2017) examined the bond market.  

Krol (2014) and Beckmann and Czudaj (2017) centered their study on foreign exchange markets. The second part 

of the empirical literature explores the correlation between these two variables. Bekiros et al. (2016) and Caggiano 

et al. (2017) investigate the correlation between the United States' EPU and the American stock market. Xiong 

and Yu (2018) employ a dynamic conditional correlation multivariate generalized autoregressive conditionally 

heteroskedastic model to examine the correlation between China's EPU and its stock market. Previous literature 

has also explored the correlation between EPU and various markets, including the stock-bond correlation (Fang 

et al., 2017) and goldstock correlation (Gao and Zhang, 2016).  

However, previous studies have some limitations. Firstly, the short-term volatility component in stock returns is 

linked to its own past information, while the long-term component of volatility is associated with macroeconomic 

fundamentals (Asgharian et al., 2013; Engle et al., 2013). Given this observation, it is fitting to deploy a model 

that distinguishes between the overall volatility of stocks into its long-term and short-term components by 

incorporating the GEPU variable in the former. This model is known as the generalized autoregressive conditional 

heteroskedasticity extended mixed data sampling (GARCH-MIDAS) model. These studies examine the 

relationship between the variables, factoring in global financial crises. Further research shows that COVID-19 

significantly affects the correlation between GEPU and stock market returns' volatility. Notably, Sharif et al. 

(2020), Yang and Yang (2021), Ahmed and Sarkodie (2021), Youssef et al. (2021), and Apostolakis et al. (2021) 

have reported such findings. Previous research has given greater attention to developed countries and less to 

emerging ones. Therefore, our study focuses on the SADC, a market that has received limited examination and 

integration.  

Thus, a major query arises from our reflections: Has the COVID-19 outbreak changed the dynamics of the link 

between GEPU and the instability of stock market returns in particular SADC markets? The aim of this study is 

to investigate the effects of global economic policy uncertainty and oil shocks on stock market volatility in 

Botswana, Mauritius, and South Africa both before and during the COVID-19 pandemic. The study has two 

specific objectives as follows: To investigate the effects of GEPU and shocks in oil consumption demand on stock 

volatility in the SADC nations prior to and following the COVID-19 outbreak, and to demonstrate the dynamic 

connection between the pandemic, GEPU, and stock market return volatility in these same regions. To achieve 

our objective, we examine the following hypotheses in our study: i) The impact of GEPU and oil consumption 



David M. Reynolds (2024) 
 

19 
American Interdisciplinary Journal of Business and Economics | 

https://sadijournals.org/index.php/AIJBE 

 

demand shocks on stock volatility in SADC countries is positive and significant; ii) The relationship between the 

COVID-19 pandemic, GEPU, and stock market volatility is ever-changing.  

This study’s choice of the SADC is underlined by its status as one of the major players in the exploration and 

export of crude oil, and that its main trading and investment partner (Europe) is suffering from the devastating 

COVID-19 pandemic (McKibbin and Fernando, 2021). Subsequently, this development constrains them to certain 

policies in favor of intraregional trade and investment, which should have an impact on its stock market. 

Furthermore, we find the impact of health responses taken by some of the SADC countries, on the stock market 

to be worth studying. Indeed, the Tanzanian government officially declared the virus to be over and stopped 

recording cases towards the end of April 2020. Similarly, Madagascar has also become a center of attention with 

its claim to have discovered a cure for the deadly COVID-19 pandemic (Mthembu, 2020). There is agreement 

that these events may have an impact on the link between oil and stock prices in the region, which necessitates 

the present study.   

Two important contributions can be drawn from this study. First, we take into account the COVID-19 health crisis 

in the analysis of the relationship between GEPU and the returns of certain Southern African Development 

Community (SADC) countries' stock exchanges. The analysis of this study is relevant insofar as this pandemic 

has had very costly repercussions on the stock market returns of these places. Second, in this article, we employ 

a model that is underutilized in the literature, namely the GARCH-MIDAS model of Engle et al. (2013).   

REVIEW OF LITERATURE  

EPU may impact stock prices. Although many studies have investigated the impact of EPU on numerous 

macroeconomic variables, research into the link between EPU and stock prices or returns only emerged after the 

2008 global financial crisis (Li et al., 2016). Baker et al. (2016) made a significant contribution by developing 

EPU or GEPU indexes, which have been utilized in various recent empirical studies. The EPU index calculates 

the average of three main indicators of uncertainty: major news on the EPU, the expiry of tax provisions, and 

forecasters’ disagreements about government purchases and inflation. Recently, investors, policymakers, and 

academics have shown a great deal of interest in the effects of EPU on the stock market (Jin et al., 2019). It is 

conceivable that the uncertainty of a country may affect the stock prices in another country. Mensi et al. (2014) 

conducted a thorough analysis of quantile regressions for the BRICS nations, encompassing Brazil, Russia, India, 

China, and South Africa, using data spanning from September 1997 to September 2013. The study concluded that 

US EPU did not have any impact on the BRICS stock markets. Momin and Masih (2015) carried out a study on 

the impact of US EPU on the stock returns of BRICS countries, employing an autoregressive distributed lag 

model for the period between January 2000 and March 2015. They ascertained that solely the Indian stock market 

was affected by the US EPU. Dakhlaoui and Aloui (2016) investigated the impact of the US EPU on stock returns 

of BRICS countries, using daily data from July 4, 1997 to July 27, 2011.  

The study discovered a negative correlation between BRICS stock indices and EPU in the US, with volatility 

distribution varying between negative and positive values. Moreover, the link between uncertainty and stock 

returns was inconsistent during periods of global economic crisis. Aydin et al. (2022) posited that political 

volatility within a nation could affect its stock prices and yields. Ozoguz (2009) utilized Markov switching and 

intertemporal capital asset pricing models to examine the relationships between the aforementioned variables in 

the US during the period of January 1961 to December 2001. It was observed that there existed a negative 

correlation between insecurity and stock prices. Sum (2012) conducted an analysis using ordinary least squares 

(OLS) methodology to examine data from February 1993 to April 2012. The results showed that EPU has an 



David M. Reynolds (2024) 
 

20 
American Interdisciplinary Journal of Business and Economics | 

https://sadijournals.org/index.php/AIJBE 

 

adverse impact on stock market returns in various countries, including the European Union, Turkey, Ukraine, 

Switzerland, Russia, and Norway. The findings indicate a negative association between the two variables. 

Antonakakis et al. (2013) applied a dynamic conditional    correlation     model     to    investigate    the relationship 

between S&P500 returns and EPU in the United States from January 1985 to January 2013. Bijsterbosch and 

Guérin (2013) employed a Markov regime-switching model on US variables ranging from January 1986 to 

January 2012, and ascertained that high episodes of EPU cause a reduction in stock prices and bond yields. As 

per Kang and Ratti's (2013) analysis, which was conducted through a vector autoregression (VAR) model, a 

favorable oil demand shock against the US oil demand led to an increase in apprehension regarding future oil 

supply and concomitantly, induced EPU that ultimately impacted stock proceeds in a negative manner. Brogaard 

and Detzel’s (2015) research indicated that EPU had a significant impact on stock returns in Europe and Canada. 

The authors employed the generalized method of moments to investigate the relationship between share market 

returns and EPU in the USA, using monthly data ranging from May 1985 to December 2012. They observed a 

negative association between the fluctuations of EPU and stock market returns that was contemporaneous. Chang 

et al. (2015) conducted a panel causality test using bootstrap methods on seven nations of the Organization for 

Economic Cooperation and Development from January 2001 to April 2013. Their findings indicate that 

government policy uncertainty was provoked by stock price volatility in the USA and the UK, while stock price 

indices influenced government policy uncertainty in Italy and Spain. However, no causal relationship was 

detected between the factors in Canada, Germany, and France. Ko and Lee (2015) utilized wavelet analysis to 

examine eleven countries in Asia, Europe, and North America, from January 1998 to December 2012. Their 

findings indicate that stock prices decrease after an upsurge in EPU. The study follows a conventional academic 

structure, employing clear, concise language, and technical terms where necessary. There is no biased or 

ornamental language within the text, and any abbreviations are adequately explained at first use. Adequate 

spelling, grammar, and punctuation are observed, adhering to the standards for British English.   

Xiong et al. (2018) conducted a study on the impact of EPU on the stock returns of companies in the tourism 

industry. The study utilized multiple regression approaches to analyze data from January 2002 to December 2013. 

Xiong et al. (2018) conducted a study on the impact of economic policy uncertainty (EPU) on the stock returns 

of companies in the tourism industry. Xiong et al. (2018) conducted a study on the impact of EPU on the stock 

returns of companies in the tourism industry. Findings revealed that changes in EPU negatively affected the stock 

returns of Turkish tourism firms. The researchers implemented the dynamic conditional correlation-bivariate 

generalized autoregressive conditional heteroskedasticity model spanning January 1995 to December 2016. The 

findings revealed that the EPU's absolute variations held greater impact on Shanghai stock market returns as 

opposed to Shenzhen's. Moreover, the study unveiled increased volatility of stock returns in periods of financial 

crises. Guo et al. (2018) conducted a quantitative regression analysis to investigate the correlation between EPU 

and stock yields in G7 and BRICS countries from February 1985 to August 2015. The study yielded important 

findings highlighting asymmetrical association between EPU and stock markets of the USA and Italy. In contrast, 

EPU had a detrimental impact on stock markets of Germany, Japan, India, and China. Furthermore, there was 

moderate impact of uncertainty on the Canadian and Russian stock exchanges, whereas no association between 

EPU and stock prices was observed in the UK and France. Chiang (2019) examined the correlation between EPU, 

risk and additional stock returns in G7 countries from January 1997 to June 2016, using a generalized mistake 

distribution GARCH model. The outcomes revealed that an increase in EPU contributes to a reduction in excess 

stock returns. Gao et al. (2019) investigated the associations among stock prices, economic policy uncertainty, 



David M. Reynolds (2024) 
 

21 
American Interdisciplinary Journal of Business and Economics | 

https://sadijournals.org/index.php/AIJBE 

 

and global oil prices in China from January 2005 to December 2017. They used a rolling window Toda-

Yamamoto causality test to determine that the bidirectional causality between the variables was mainly associated 

with the 1997 Asian crisis, the 2008 financial crisis, and China's economic structural reforms.  

In the present global economic landscape, the rise of globalization has bolstered the connections among nations 

and heightened their reciprocal impact. This is especially evident in the instance of developing countries, which 

possess vast populations and offer significant prospects for economic expansion. It is crucial to investigate the 

impact of global economic policy uncertainty on the stock markets of these countries. This becomes particularly 

relevant considering the limited research conducted on these markets in comparison to those of developed 

countries.  

Several recent studies have highlighted the volatility of oil prices. Two sets of research studies use different 

methods to examine the reasons for instability in the oil market. The first set applies GARCH-class models and 

cites examples including Chan and Grant (2016), Nomikos and Pouliasis (2011), Wang and Wu (2012), Wang et 

al. (2016), and Sadorsky (2006). The second set of papers relies on recognized volatility models, including 

Haugom et al. (2014) and Sevi (2014). Both sets of models gather insightful data from recorded unpredictability 

or costs. The efficient market hypothesis of Fama (1970) justifies the predictive power of fundamental variables, 

while commodity markets are not as efficient as more developed financial markets (Chen et al., 2010). 

Furthermore, several studies have suggested inefficient weak-form markets for crude oil (Tabak and Cajueiro, 

2007; Wang and Liu, 2010), meaning that the current   oil    price   does   not   encompass   all    of   the fundamental 

information available. It may be inferred that the current unpredictability in oil prices does not encompass all 

previous information pertaining to macroeconomic instability. There have been many attempts to understand and 

forecast fluctuations in oil prices based on supply and demand fundamentals (Baumeister and Kilian, 2012; Boelli 

et al., 2016). However, to our knowledge, the financial origins of price volatility have not been fully considered 

in scholarly literature, except for significant contributions from Conrad et al. (2014) and Pan et al. (2017). Conrad 

et al. (2014) analyzed the effect of macroeconomic factors on oil price volatility from a sample-based approach. 

Other scholars have re-evaluated this issue through an out-of-sample outlook by testing whether the incorporation 

of macroeconomic elements into volatility models can produce more precise forecasts. Concentrating on daily 

volatility which is of significant interest to option market traders, the study observed that objective evaluations 

of the data are imperative for accurate predictions. Including macroeconomic data in a GARCH or realized 

volatility model is challenging due to the incompatible data frequencies of the oil price and its fundamental 

factors.  

To clarify, while oil price data is available daily, data on oil output and demand is obtained monthly or even less 

frequently. Fortunately, the GARCH-MIDAS class specifications recommended by Engle et al. (2013) effectively 

resolve the mixed-frequency problem in volatility modelling. This model divides daily conditional volatility into 

two parts: a short-term volatility element that adheres to the standard daily GARCH process (Bollerslev, 1986) 

and a long-term component that considers mixed-frequency data sampling (MIDAS) regression with monthly, 

quarterly, or even lower frequency variables (Ghysels et al., 2004). In recent times, GARCH-MIDAS models 

have gained popularity for identifying links between high-frequency volatility and low-frequency macroeconomic 

variables (Conrad et al., 2014; Conrad and Loch, 2015a, b). Yu et al. (2018) and Yu and Huang (2021) deployed 

the GARCH-MIDAS approach to demonstrate that GEPU increases Chinese stock market volatility and has 

predictive capabilities. Li et al. (2020) examined the impact of GEPU on the volatility of China's stock market by 

analyzing the directional effects (up and down) and found that both up and down GEPU positively affect Chinese 



David M. Reynolds (2024) 
 

22 
American Interdisciplinary Journal of Business and Economics | 

https://sadijournals.org/index.php/AIJBE 

 

stock market volatility. Moreover, Li et al. (2020) established that the GEPU index can anticipate shifts in Chinese 

stock market volatility.   

Wang et al. (2021) used a GARCH-MIDAS model with a skew student’s t-distribution to examine the impact of 

domestic and foreign EPU on China's financial stocks. In a recent study, Li et al. (2019) analyzed the effects of 

EPU on Chinese stock market volatility through a predictive regression method. The results indicated that the 

EPU index had a significantly negative influence on the future volatility of the Chinese stock market. 

EMPIRICAL METHODOLOGIES  

The study adopts two major empirical methodologies. Firstly, it employs the generalized autoregressive 

conditional heteroscedasticity model with mixed data sampling. Secondly, it employs loss functions.  

Specifically, the study uses a novel component, the generalized autoregressive conditional heteroskedasticity 

(GARCH) model based on mixed data sampling (MIDAS) regression. The new component GARCH model is 

known as MIDAS-GARCH, wherein macroeconomic variables are directly incorporated into the longterm 

component's specifications. The MIDAS regression models, introduced by Ghysels et al. (2006), provide a 

framework for integrating macroeconomic variables sampled at varying frequencies with the financial series. 

Additionally, Forsberg and Ghysels (2006) demonstrate that MIDAS has a relative advantage over Anderson et 

al. (2007) proposed Heterogeneous Autoregressive Realized Volatility (HAR-RV) model, as shown through 

simulation.  

To explore the correlation between GEPU/oil consumption shock and stock markets in SADC nations, the 

GARCH-MIDAS model proposed by Engle et al. (2013) is utilized. Monthly frequency data for GEPU and oil 

consumption shocks, along with daily frequency data for stock returns, are utilized in this model (Appendix 

Figures 1, 2 and 3). The model assumes that stock returns on specific days, i within a given month,t follow a 

specific equation process.   

     i =1,...,Nt                                  (1)  

        

where r
i,t is the logarithmic return on a specific day i within a given montht . The total 

volatility of daily returns can be defined as ( i
2 

,t i gi,t ) which is the sum of two 

components: t is the long-term component that is believed to reveal the source of stock 

market volatility, and the short-term component that accounts for short-lived daily fluctuations (Engle et al., 2013; 

Yu et al., 2021). The value of N
t represents the number of trading days in the montht , and i−1,t represents the 

information set available up (i−1)th to the given day of the periodt . The conditional variance g 
i,t of the short-term 

component follows a daily GARCH (1, 1)  

process (Bollerslev, 1986):  

  

(ri−1 , t )2 

gi,t =(1 )+  g i−1,t                    (2)  

 

t  

Low-frequency variables, such as realized volatility or macro variables, can describe the long-term component t 

. Two different specifications for the long-term component without changes in the short-term equation exist. The 

first specification examines the effect of realized volatility on the long-term component of the total volatility. 

  

, , , i t t i t i t r g 
   = + 

  

  
  



David M. Reynolds (2024) 
 

23 
American Interdisciplinary Journal of Business and Economics | 

https://sadijournals.org/index.php/AIJBE 

 

Smoothed realized volatility is defined as the variable in the spirit of MIDAS regression. The following t is the 

specification for the MIDAS filtering.  

Drama          89 

K 

t =m rv k (w1)RVt−k                                                  (3)  

k−1 

  

Monthly smoothed realized volatility is denoted by  

N t 

(RVt ri
2
, t ) with a fixed span of time representing the number  

i=1 

of periods K used to smooth the realized volatility. The second specification involves directly inserting 

macroeconomic variables into the long-term component.  

  

K 

t =m rv k (w1)RVt−k gepu k (w1)GEPUt−k                   

k=1 k=1 

                           (4)   

The log difference of GEPU
t−k denotes the level of change rate of monthly global economic policy uncertainty. 

Equation 4, as used by Yu et al. (2021), captures information explained by both the realized volatility and 

economic policy uncertainty and is compared to a basic model in which the long-term component does not involve 

GEPU information. The weightage method utilized in both Equations 3 and 4 is explained using a beta lag 

polynomial as:  

  

k (w1) = K (K / k)w1−1                                         (5)  

 
( j / K)w1−1 

j=1  

where the coefficients in Equation 5 equate to 1, we examine how GEPU affects stock volatility. We utilize the 

estimated daily total variance i
2

,t as a measure of the total variance's accuracy. The realized total volatility is 

denoted ri
2

,t as the variable. The GARCH- 

MIDAS-RV+GEPU model, developed using Equations 1, 2, 4, and 5, is assessed against the conventional 

GARCH-MIDAS-RV model that is constructed by Equations 1, 2, 3, and 5, to determine its predictive capability. 

Comparison is also made with a simplistic GARCH (1, 1) model (Bollerslev, 1986), shown.   

rt t                                                             (6) 

    

where t t z t , , and the conditional variance  

process, t
2 , has the form:  

  

t
2 =k t

2
−1 t

2
−1                                     (7)                        



David M. Reynolds (2024) 
 

24 
American Interdisciplinary Journal of Business and Economics | 

https://sadijournals.org/index.php/AIJBE 

 

Secondly, in order to assess the predictability of volatility in a particular model, we utilize various loss functions 

that compare the estimated predicted variance to the realized volatility. The six loss functions employed in this 

study are presented in the following equations.  

  

1 T 2 2)2          (8)  

The data  

Three stock markets in SADC countries, namely, Botswana, Mauritius and South Africa were considered. The 

countries were selected based on data availability. The authors use daily data from the http://www.investing.com/ 

database for the period from 01/05/2008 to 24/04/2022. Their data are divided into two periods. The first period 

(before COVID-19 pandemic) goes from 01/05/2008 to 04/03/2020, the second period (during COVID-19) goes 

from 05/03/2020 to 24/04/2022. They opt for the monthly GEPU index computed by Baker et al. (2016), which 

is deemed a reputable proxy for real-world economic policy uncertainty. It can be obtained from their website 

(http://www.policyuncertainty.com/). Additionally, we incorporate the monthly oil consumption shock, available 

on Baumeister and Hamilton's (2019) website. The analysis encompasses GEPU and oil consumption shocks that 

occurred from May 2008 to April 2022, including the financial crisis around June 2009, the European Sovereign 

debt crises, US-China trade tensions, Brexit, and the new context of COVID-19 as a global pandemic. The study 

utilized a total of 3,258 observations.  

RESULTS AND DISCUSSION  

Table 1 displays the descriptive statistics for three data series of stock indices and the GEPU index. The sample 

size for each selected SADC stock market's stock index series is 3258, while the GEPU index and oil consumption 

each have 168 observations. The stock index has a daily data frequency, whereas the GEPU index has a monthly 

frequency. Table 2 presents the descriptive statistics for the stock returns of the SADC markets examined, along 

with the logarithms of the GEPU change rate and oil consumption. The table includes 3257 stock return 

observations for each country, 167 GEPU observations and 93 oil consumption observations. Tables 1 and 2 

indicate that the kurtosis values of both the stock index and return series are positive. However, the South Africa 

stock index series and logarithm of oil consumption exhibit negative skewness, while the remaining stock index 

and return series for all SADC stock markets have positive skewness. Statistical properties of the variables in our 

study are presented in Table 3. Based on the results from the Augmented Dickey-Fuller (ADF), Phillips-Perron 

(PP), and Vratio (VR) tests, all statistics significantly reject the null hypothesis of a unit root at the 1% level. This 

confirms that all of our series are stationary. Additionally, the Jarque-Bera (JB) test of all stock returns, GEPU, 

and oil consumption indicates that all of their distributions deviate significantly from normality at the 1% level.   

Furthermore, the Autoregressive Conditional Heteroscedasticity (ARCH) test statistics for each individual stock 

return exceeded 100 according to Engle's (1982) analysis, with critical values of the ARCH test at the 1% level 

of 6.6635. This implies noteworthy heteroskedastic effects. Notably, the descriptive statistics obtained over the 

study period take precedence for both in-sample (before COVID-19) and out-of-sample (during COVID-19) data.  

Furthermore, the estimated parameters of the impact that GEPU and oil consumption demand shock (OCDS) 

have on stock market volatility in three SADC countries are presented. Tables 4 and 5 display the findings from 

the GARCH and GARCH-MIDAS models, which include the entire sample (01/05/2008 to 24/04/2022) divided 

1  
ˆ ( t t 

i 
MSE 

T   
= 

= −  

  

2 2 2 

1  

1 
ˆ ( ) 

T 

t t 
i 

RMSE 
T   

= 
− = 

 
                                             (9 )   

http://www.investing.com/
http://www.investing.com/


David M. Reynolds (2024) 
 

25 
American Interdisciplinary Journal of Business and Economics | 

https://sadijournals.org/index.php/AIJBE 

 

into subsamples. The study consists of two datasets: insample data (01/05/2008 to 04/03/2020), representing the 

pre-COVID-19 period, and out-of-sample data (05/03/2020 to 24/04/2022), representing the duringCOVID-19 

period.  

The GARCH (1, 1) model parameters are significant at the 1% level in all cases, except for  in South Africa, 

where they are significant at the 5% level, and for k, which is non-significant in Botswana. These findings suggest 

that the GARCH (1, 1) model is a good fit for the daily data. The GARCH-MIDAS model, , , RV , and mshows 

positive and significant coefficients, confirming its suitability for the mixed data sampling model. The importance 

lies in examining the statistical significance of the coefficient RV to understand if OCDS or GEPU impact the 

long-term volatility of SADC countries.  

Additionally, the sum of ARCH and GARCH terms and  is less than one( 1) , inferring that OCDS does 

not have a permanent effect on stock market returns. The empirical results from Table 5 for the  

GARCH-MIDAS-RV+OCDS model demonstrate significant coefficients RV for Botswana, Mauritius, and 

South Africa at 1%, indicating a positive response in stock returns to changes in oil consumption demand shocks. 

The estimated coefficients for Botswana, Mauritius, and South Africa were 0.076, 0.001, and 0.119, respectively. 

This contradicts the findings of Salisu and Gupta (2021), who reported a negative response for South Africa.  

The impact of GEPU on stock market returns is not permanent. In the GARCH-MIDAS-RV+GEPU model, the 

estimated coefficients for Botswana, Mauritius, and South Africa are 0.056, 4.052e-04, and 0.033, respectively. 

These outcomes indicate that GEPU has a significant and positive effect on SADC stock markets for the insample 

data. This conclusion aligns with the findings of Yu et al. (2021).   

The same positive impact of OCDS and GEPU on stock markets is obtained for all three countries in the full 

sample (Table 6). The estimated coefficient RV , which is realized volatilities, is 0.117 for Botswana, 0.170 for 

Mauritius, and 0.119 for South Africa in for OCDS. For GEPU case, RV is 0.016, 0.099, and 0.014 for    

The levels of significance are represented by ***, **, and *, respectively, indicating 1, 5, and 10%. 

GARCH-MIDAS refers to generalized autoregressive conditional heteroskedasticity extended mixed data 

sampling, while GEPU refers to global economic policy uncertainty.  

MIDAS-RV+GEPU models shows that  the  latter  has superior fitness for the given countries. This is  owing to 

the smaller AIC and BIC, and larger Log-  

Table 7.  In-sample evaluation results for the GARCH and GARCH-MIDAS models.  

Variable   
GARCH       GARCH-MIDAS    

AIC   BIC    Log-L    AIC  BIC   Log-L   

     Oil consump tion demand shoc k    

Botswana   -1.057   -1.044   1461.079  

   

-23207.7   -23172.2   11609.9   

Mauritius   2.594   2.607   -3563.671  

   

-23024   -22988.5   11518   

South Africa   

  

  

1.358   

  

1.371   

  

-1862.799  

   

    

-17745.3   

  

GEPU   

-17709.8   

  

8878.67   

  

Botswana           -21337.2   -21301.7   10674.6   

Mauritius           -22525.4   -22490   11268.7   

South Africa           -17364.1   -17328.7   8688.06   

  

AIC, BIC, and Log-L values are used to compare fitness of the two models. AIC is the Akaike Information 

Criteria, BIC is the Bayesian Information Criteria, and Log-L is the optimal log-likelihood function. GARCH-

MIDAS =generalized autoregressive conditional heteroskedasticity extended mixed data sampling.  



David M. Reynolds (2024) 
 

26 
American Interdisciplinary Journal of Business and Economics | 

https://sadijournals.org/index.php/AIJBE 

 

Table 8. Results of out-of-sample volatility forecast validation.  

  

MSE  RMSE  Model   

 Botswana   

GARCH   4.248e-05  6.517e-03   

GARCH-MIDAS   

  

  

306.600e-12#  1.751e-05#   

    

Mauritius   

GARCH   0.018”  1.341e-01   

GARCH-MIDAS   

  

  

0.761  8.726e-01#   

    

South Africa   

GARCH   8.843e-02  2.973e-01   

GARCH-MIDAS   7.322e-09#  8.557e-05#   

  
# denotes that the GARCH-MIDAS model outperforms the GARCH model and ”denotes that the 

GARCH model outperforms the GARCH-MIDAS model. GARCH-MIDAS=generalized 

autoregressive conditional heteroskedasticity extended mixed data sampling.

L than those of the traditional GARCH (1,1) model. Involving OCDS and/or GEPU in the GARCH-MIDAS-RV 

model leads to improved fitness compared to the GARCH (1,1) model. In conclusion, GARCH-MIDAS-

RV+GEPU are recommended for better model fitness. The out-ofsample projection pertains to the period from 

05/03/2020 to 24/04/2022, which coincides with the COVID-19 era. To assess the out-of-sample forecast 

capability of a volatility model, the loss function is utilised. It pertains to the anticipation of OCDS as well as 

GEPU variables concerning stock volatility in Botswana, Mauritius, and South Africa.  

Table 8 displays the mean square error (MSE) and root mean square error (RMSE), providing insight into the 

effectiveness of two models in predicting the daily total volatility of stock in particular countries.  The 

GARCHMIDAS-RV+OCDS model outperforms the GARCH model for SADC countries when considering both 

MSE and RMSE 

CONCLUSION AND POLICY IMPLICATIONS   

The response of stock market volatility in Botswana, Mauritius, and South Africa to GEPU and oil consumption 

demand shocks (OCDS) was examined by using the GARCH-MIDAS approach. Our dataset comprises 

subsamples from 01/05/2008 to 24/04/2022, with the first being the in-sample data (01/05/2008 to 04/03/2020) 

corresponding to the pre-COVID-19 period and the second being the out-of-sample data (05/03/2020 to 

24/04/2022)   corresponding   to    the    during-COVID-19  period. Our study presents evidence of the effects of 

GEPU and oil shocks on stock market volatility in three SADC nations, utilizing empirical analysis within the 

sample and prediction outside it.  

During the in-sample analysis, the findings indicate that the GARCH (1, 1) model is a good fit for daily data, 

displaying significant parameters for all targeted SADC countries with the exception of one. The GARCH-

MIDAS model also demonstrates a good data fit, with a positive and significant coefficient for either OCDS or 

GEPU on the countries' long-term volatility. These results suggest that both OCDS and GEPU have a noteworthy 



David M. Reynolds (2024) 
 

27 
American Interdisciplinary Journal of Business and Economics | 

https://sadijournals.org/index.php/AIJBE 

 

and positive influence on the SADC stock market in the insample data. The models' fitness performance is 

evaluated using optimum log-likelihood function, AIC, and  

BIC. The models GARCH-MIDAS-RV+OCDS and GARCH-MIDAS-RV+GEPU outperform the traditional 

GARCH (1, 1) model. The results of out-of-sample prediction indicate the GARCH-MIDAS-RV+OCDS model 

showing better performance than the GARCH model for SADC countries when MSE and RMSE are used. 

Furthermore, the influence of OCDS and GEPU on stock market returns appears to be transient rather than 

enduring, indicating that these factors do not have a lasting effect.  

Given these results, it is advisable that policymakers in the chosen SADC nations focus on the effects of oil 

consumption and global economic policy uncertainty and their possible influence on stock market instability. As 

the SADC countries are net importers of oil and have a fragile economic context post COVID-19, global economic 

policy uncertainty shocks have significant effects: i) an increase in risk premium leads to more volatility and 

correlation in stock markets, especially in weaker economic conditions; ii) generally, lesser developed countries' 

stock returns experience negative effects; iii) crude oil price volatility is also impacted negatively, and is directly 

linked to major events, with varying impacts depending on the type of event.  

This suggests that measures to stabilize oil prices and promote economic stability and transparency could help in 

reducing stock market volatility. Moreover, policymaker could consider implementing policies to encourage 

investment diversification to reduce the impact of global shocks on the domestic stock market. Policies that 

promote the development of financial markets, including stock markets, could also increase resilience to external 

shocks and contribute to overall economic growth.  

Furthermore, given the better fitness performance of the  

GARCH-MIDAS-RV+OCDS and GARCH-MIDAS- 

RV+GEPU models compared to the traditional GARCH (1, 1) model, policymakers could consider using these 

models in their forecasting and risk management processes. Finally, the study highlights the importance of 

considering the impact of external factors on domestic stock market volatility and the need for policymakers to 

implement policies that promote economic stability and financial market development. 

CONFLICT OF INTERESTS  

The author has not declared any conflict of interests. 

REFERENCES   

Ahmed MY, Sarkodie SA (2021). COVID-19 pandemic and economic policy uncertainty regimes affect 

commodity market volatility. Resources Policy 74:102303.   

Anderson T, Bollerslev T, Diebold F (2007). Roughing it up: Including jump component in the measurement, 

modeling and forecasting of return volatility. The Review of Economics and Statistics 89:701-720.   

Antonakakis N, Chatziantoniou I, Filis G (2013). Dynamic comovements of stock market returns, implied 

volatility and policy uncertainty Economics Letters 120(1):87-92.  

Apostolakis GN, Floros C, Gkillas K, Wohar M (2021). Political uncertainty, COVID-19 pandemic and stock 

market volatility transmission. Journal of International Financial Markets, Institutions and Money 

74:101383.  



David M. Reynolds (2024) 
 

28 
American Interdisciplinary Journal of Business and Economics | 

https://sadijournals.org/index.php/AIJBE 

 

Asgharian H, Hou AJ, Javed F (2013). The importance of the macroeconomic variables in forecasting stock return 

variance: A GARCH MIDAS approach. Journal of Forecasting 32(7):600-612.   

Aydin M, Pata UK, Inal V (2022). Economic policy uncertainty and stock prices in BRIC countries: evidence 

from asymmetric frequency domain causality approach. Applied Economic Analysis 30(89):114129.   

Baker SR, Bloom N, Davis SJ (2016). Measuring economic policy uncertainty The Quarterly Journal of 

Economics 131(4):1593-1636.   

Baumeister C, Hamilton JD (2019). Structural Interpretation of Vector Autoregressions with Incomplete 

Identification: Revisiting the Role of Oil Supply and Demand Shocks. American Economic Review 

109(5):1873-1910.  

Baumeister C, Kilian L (2012). Real-time forecasts of the real price of oil Journal of Business and Economic 

Statistics 30(2):326-336. https://www.bankofcanada.ca/wp-content/uploads/2011/08/wp201116.pdf  

Beckmann J, Czudaj R (2017). Exchange rate expectations and economic policy uncertainty. European Journal 

of Political Economy 47:148-162.   

Bekiros S, Gupta R, Kyei C (2016). On economic uncertainty, stock market predictability and nonlinear spillover 

effects. The North American Journal of Economics and Finance 36:184-191.  

Bijsterbosch M, Guérin P (2013). Characterizing very high uncertainty episodes. Economics Letters 121(2):239-

243.   

Boffelli S, Skintzi VD, Urga G (2016). High-and low-frequency correlations in European government bond 

spreads and their macroeconomic drivers. Journal of Financial Econometrics 15(1):62105.   

Bollerslev  T  (1986).  Generalized  autoregressive  conditional heteroskedasticity. 

Journal of Econometrics, pp. 307-327.   

Brogaard J, Detzel A (2015) The asset-pricing implications of government economic policy uncertainty. 

Management Science 61(1):3-18.   

Caggiano G, Castelnuovo E, Figueres JM (2017). Economic policy uncertainty and unemployment in the United 

States: A nonlinear approach. Economics Letters 151:31-34.   

Chan JC, Grant AL (2016). Modeling energy price dynamics: GARCH versus stochastic volatility. Energy 

Economics 54:182-189.   

Chang T, Chen WY, Gupta R, Nguyen DK (2015). Are stock prices related to the political uncertainty index in 

OECD countries? Evidence from the bootstrap panel causality test. Economic Systems 39(2):288-300.  



David M. Reynolds (2024) 
 

29 
American Interdisciplinary Journal of Business and Economics | 

https://sadijournals.org/index.php/AIJBE 

 

Chen YC, Rogoff KS, Rossi B (2010). Can exchange rates forecast commodity prices? The Quarterly Journal of 

Economics 125(3):11451194.   

Cheng CHJ (2017). Effects of foreign and domestic economic policy uncertainty shocks on South Korea. Journal 

of Asian Economics 51:1-11.  

Chiang TC (2019). Economic policy uncertainty, risk and stock returns: Evidence from G7 stock markets. Finance 

Research Letters 29:4149.  

Christou C, Cunado J, Gupta R, Hassapis C (2017a). Economic policy uncertainty and stock market returns in 

Pacific Rim countries: Evidence based on a Bayesian panel VAR model. Journal of Multinational 

Financial Management 40:92-102.   

Christou C, Gupta R, Hassapis C (2017b). Does economic policy uncertainty forecast real housing returns in a 

panel of OECD countries? A Bayesian approach. The Quarterly Review of Economics and Finance 65:50-

60.   

Conrad C, Loch K (2015a). Anticipating long term stock market volatility. Journal of Applied Econometrics 

30(7):1090-1114.   

Conrad C, Loch K (2015b). The variance risk premium and fundamental uncertainty Economics Letters 132:56-

60.   

Conrad C, Loch K, Rittler D (2014). On the macroeconomic determinants of long-term volatilities and 

correlations in US stock and crude oil markets. Journal of Empirical Finance 29:26-40.  

Dakhlaoui I, Aloui C (2016). The interactive relationship between the US economic policy uncertainty and BRIC 

stock markets. International Economics 146:141-157.  

Demir E, Ersan O (2017). Economic policy uncertainty and cash holdings: Evidence from BRIC countries. 

Emerging Markets Review 33:189-200.  

Demir E, Ersan O (2017). Economic policy uncertainty and cash holdings: Evidence from BRIC countries. 

Emerging Markets Review 33:189-200.  

Engle RF, Ghysels E, Sohn B (2013). Stock market volatility and macroeconomic fundamentals. Review of 

Economics and Statistics 95(3):776-797.   

Fama EF (1970). Efficient Capital Market: A Review of Theory and Empirical Work. The Journal of Finance 

25(2):383-417.  

Fang L, Chen B, Yu H, Qian Y (2018). The importance of global economic policy uncertainty in predicting gold 

futures market volatility: A GARCH MIDAS approach. Journal of Futures Markets 38(3):413-422.   



David M. Reynolds (2024) 
 

30 
American Interdisciplinary Journal of Business and Economics | 

https://sadijournals.org/index.php/AIJBE 

 

Fang L, Yu H, Li L (2017). The effect of economic policy uncertainty on the long-term correlation between US 

stock and bond markets. Economic Modelling 66:139-145.  

Forsberg L, Ghysels E (2006). Why do absolute returns predict volatility so well? Journal of Financial 

Econometrics 6:31-67.   

Gao R, Zhang B (2016). How does economic policy uncertainty drive gold–stock correlations? Evidence from 

the UK. Applied Economics 48(33):3081-3087.  

Gao X, Ren Y, Li X (2019) The interdependence of global oil price, China's stock price and economic policy 

uncertainty. Australian Economic Papers 58(4):398-415.  

Ghysels E, Sinko A, Valkanov R (2006). MIDAS regression: Further results and new directions. Econometric 

Reviews 26:53-90.  

Ghysels E, Santa-Clara P, Valkanov R (2004). The MIDAS touch: Mixed Data Sampling Regression Models, 

CIRANO Working Papers.   https://EconPapers.repec.org/RePEc:cir:cirwor:2004s-20      

Guo P, Zhu H, You W (2018). Asymmetric dependence between economic policy uncertainty and stock market 

returns in G7 and BRIC: A quantile regression approach. Finance Research Letters 25:251-258.  

Haugom E, Langeland H, Molnár P, Westgaard S (2014). Forecasting volatility of the US oil market. Journal of 

Banking and Finance 47:114.   

Jin X, Chen Z, Yang X (2019). Economic policy uncertainty and stock price crash risk. Accounting and Finance 

58(5):1291-1318.  

Kang W, Ratti RA (2013). Oil shocks, policy uncertainty and stock market return. Journal of International 

Financial Markets, Institutions and Money 26:305-318  

Ko JH, Lee CM (2015). International economic policy uncertainty and stock prices: Wavelet approach. 

Economics Letters 134:118-122.  

Krol R (2014). Economic policy uncertainty and exchange rate volatility International  Finance 

 17(2):241-256. https://EconPapers.repec.org/RePEc:bla:intfin:v:17:y:2014:i:2:p:241-256  

Li T, Ma F, Zhang X, Zhang Y (2020). Economic policy uncertainty and the Chinese stock market volatility: 

novel evidence. Economic Modelling 87:24-33.  

Li XL, Balcilar M, Gupta R, Chang T (2016). The causal relationship between economic policy uncertainty and 

stock returns in China and India: Evidence from a bootstrap rolling window approach. Emerging Markets 

Finance and Trade 52(3):674-689.  



David M. Reynolds (2024) 
 

31 
American Interdisciplinary Journal of Business and Economics | 

https://sadijournals.org/index.php/AIJBE 

 

Li Y, Ma F, Zhang Y, Xiao Z (2019). Economic policy uncertainty and the Chinese stock market volatility: new 

evidence. Applied Economics 51(49):5398-5410.  

McKibbin W, Fernando R (2021). The global macroeconomic impacts of COVID-19: Seven scenarios. Asian 

Economic Papers 20(2):1-30.  Mei D, Zeng Q, Zhang Y, Hou W (2018). Does US Economic Policy 

Uncertainty matter for European stock markets volatility? Physica A: Statistical Mechanics and its 

Applications 512:215-221.  

Mensi W, Hammoudeh S, Reboredo JC, Nguyen DK (2014). Do global factors impact BRICS stock markets? A 

quantile regression approach. Emerging Markets Review 19:1-17.  

Momin E, Masih M (2015). Do US policy uncertainty, leveraging costs and global risk aversion impact emerging 

market equities? An application of bounds testing approach to the BRICS available at: https://mpra.ub.uni-

muenchen.de/65834/1/MPRA_paper_65834.pdf/  (Accessed 10 February 2020).  

Mthembu P (2020). The Impact of COVID-19 in the SADC region: Building  resilience  for  Future 

pandemics. https://www.southsouthgalaxy.org/news/article-the-impact-of-COVID-19-in-the-sadc-

region-building-resilience-for-future-pandemics/  

Nomikos NK, Pouliasis PK (2011). Forecasting petroleum futures markets volatility: The role of regimes and 

market condition. Energy Economics 33(2):321-337.  

Ozoguz A (2009). Good times or bad times? Investors' uncertainty and stock returns. The Review of Financial 

Studies 22(11):4377-4422.  

Pan Z, Wang Y, Wu C, Yin L (2017). Oil price volatility and macroeconomic fundamentals: A regime switching 

GARCH-MIDAS model. Journal of Empirical Finance 43:130-142.  

Phan DHB, Sharma SS, Tran VT (2018). Can economic policy uncertainty predict stock returns? Global evidence. 

Journal of International Financial Markets, Institutions and Money 55:134-150.  

Reboredo JC, Naifar N (2017). Do Islamic bond (sukuk) prices reflect financial and policy uncertainty? A quantile 

regression approach. Emerging Markets Finance and Trade 53(7):1535-1546.   

Sadorsky P (2006). Modelling and forecasting petroleum futures volatility. Energy Energy Economics 28:467-

488.   

Salisu A, Gupta R (2021). Oil shocks and stock market volatility of the BRICS: A GARCH-MIDAS approach. 

Global Finance Journal, Elsevier 48(C).  

Sevi B (2014) Forecasting the volatility of crude oil futures using intraday data. European Journal of Operational 

Research 235(3):643659.  

https://mpra.ub.uni-muenchen.de/65834/1/MPRA_paper_65834.pdf/
https://mpra.ub.uni-muenchen.de/65834/1/MPRA_paper_65834.pdf/
https://www.southsouthgalaxy.org/news/article-the-


David M. Reynolds (2024) 
 

32 
American Interdisciplinary Journal of Business and Economics | 

https://sadijournals.org/index.php/AIJBE 

 

Sharif A, Aloui C, Yarovaya L (2020). COVID-19 pandemic, oil prices, stock market, geopolitical risk and policy 

uncertainty nexus in the US economy: Fresh evidence from the wavelet-based approach. International 

Review of Financial Analysis 70:101496.   

Sum V (2012). Economic policy uncertainty and stock market performance: evidence from the European union, 

Croatia, Norway, Russia, Switzerland, Turkey and Ukraine. Journal of Money, Investment and Banking 

25:99-104.  

Tabak BM, Cajueiro DO (2007). Are the crude oil markets becoming weakly efficient over time? A test for time-

varying long-range dependence in prices and volatility. Energy Economics 29(1):28-36.   

Wang X, Luo Y, Wang Z, Xu Y, Wu C (2021). The impact of economic policy uncertainty on volatility of China’s 

financial stocks: An empirical analysis. Finance Research Letters 39:101650.  

Wang Y, Liu L (2010). Is WTI crude oil market becoming weakly efficient over time? New evidence from 

multiscale analysis based on detrended fluctuation analysis. Energy Economics 32(5):987-992.  

Wang Y, Wu C (2012). Forecasting energy market volatility using GARCH models: Can multivariate models 

beat univariate models? Energy Economics 34(6):2167-2181.  

Wang Y, Wu C, Yang L (2016). Forecasting crude oil market volatility: A Markov switching multifractal 

volatility approach. International Journal of Forecasting 32(1):1-9.  

Wu TP, Liu SB, Hsueh SJ (2016). The causal relationship between economic policy uncertainty and stock market: 

A panel data analysis. International Economic Journal 30(1):109-122.  

Wu C, Che H, Chan TY, Lu X (2015). The Economic Value of Online Reviews, Marketing Science 34(5):739-

754. 

Xiong X, Bian Y, Shen D (2018). The time-varying correlation between policy uncertainty and stock returns: 

Evidence from China. Physica A: Statistical Mechanics and Its Applications 499:413-419.  

Yang J, Yang C (2021). Economic policy uncertainty, COVID-19 lockdown, and firm-level volatility: evidence 

from China. Pacific-Basin Finance Journal 68:101597.   

Youssef M, Mokni K, Ajmi AN (2021). Dynamic connectedness between stock markets in the presence of the 

COVID-19 pandemic: does economic policy uncertainty matter? Financial Innovation 7(1):1-27. 

Yu H, Fang L, Sun W (2018). Forecasting performance of global economic policy uncertainty for volatility of 

Chinese stock market. Physica A: Statistical Mechanics and its applications 505:931-940.  

Yu X, Huang Y (2021). The impact of economic policy uncertainty on stock volatility: Evidence from GARCH–

MIDAS approach. Physica A: Statistical Mechanics and its Applications 570:125794.   



David M. Reynolds (2024) 
 

33 
American Interdisciplinary Journal of Business and Economics | 

https://sadijournals.org/index.php/AIJBE 

 

Yu X, Huang Y, Xiao K (2021). Global economic policy uncertainty and stock volatility: evidence from emerging 

economies. Journal of Applied Economics 24(1):416-440.  

Appendix Figures   

 
  

  

  
  

  
Mauritius   

       

Figure 2 .   GARCH - MIDAS - RV+GEPU   GARCH - MIDAS - RV + oil consumption shock .   



David M. Reynolds (2024) 
 

34 
American Interdisciplinary Journal of Business and Economics | 

https://sadijournals.org/index.php/AIJBE 

 

  

  

  

  

  

  

  

  

  

  

  

  

  

  

  

  

  

  

  

 

 

Botswana   

    
  
Figure 3.   GARCH - MIDAS - RV+GEPU   GARCH - MIDAS - RV + oil consumptio n shock.   


