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MARKET RESILIENCE: A GARCH-MIDAS EXPLORATION OF 

GLOBAL ECONOMIC POLICY UNCERTAINTY AND OIL SHOCKS 

IN SADC 

 

 
1Ibrahim Sory Konaté, 2Fatoumata Binta Diop 

1,2Department of Economics, UFR Social Sciences, Université Peleforo Gon Coulibaly,Cote D’Ivoire 

DOI: https://doi.org/10.5281/zenodo.10619108 

 

Abstract: The advent of the COVID-19 pandemic has prompted a reevaluation of its impact on the 

Southern African Development Community (SADC) markets. This study delves into the nexus between 

Global Economic Policy Uncertainty (GEPU) and stock market volatility within the specific context of 

SADC. Grounded in Baker et al.'s (2016) seminal definition, GEPU encompasses uncertainty 

emanating from fiscal, monetary, or regulatory policies. The EPU index, derived by assessing the 

relative frequency of terms related to economics, politics, and uncertainty, serves as a critical metric 

for evaluating the economic policy landscape. 

A comprehensive literature review reveals an extensive body of research examining the correlation 

between Economic Policy Uncertainty (EPU) and stock markets globally. Researchers such as Sharif 

et al. (2020) and Yu et al. (2021) have explored this relationship, employing diverse methodologies. 

Building on this foundation, practitioners including 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) 

contribute relevant insights to this discourse. 

The initial segment of the literature concentrates on assessing the impacts of Economic Policy 

Uncertainty on stock markets. Notably, Wu et al. (2016) employ a panel Granger causality method to 

analyze the influence of EPU on stock markets across eight OECD nations. Furthermore, Christou et 

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

shock on the stock market returns of Pacific Basin countries, extending their analysis to other 

financial markets. 

This study extends the existing body of knowledge by focusing specifically on the SADC markets, 

evaluating the correlation between GEPU and stock market volatility within this regional context. 

Employing a multidimensional approach, the research aims to unravel the nuanced dynamics that 

characterize the relationship between economic policy uncertainty and stock market fluctuations in 

the SADC region. 

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Keywords: Economic Policy Uncertainty (EPU), Stock Market Volatility, Southern African 

Development Community (SADC), Global Economic Policy Uncertainty (GEPU), COVID-19 Pandemic 

Impact  

 

 

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 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 

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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 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 

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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 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 

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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  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. 

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(2019) investigated the associations among stock prices, economic policy uncertainty, 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; 

Boffelli 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 

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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 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.   
ri t, t gi t,i t,     i1,...,Nt                                  (1)  

 

          

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 where ri 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 t2, = 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 Nt 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 gi t, of the short-term 

component follows a daily GARCH (1, 1)  

process (Bollerslev, 1986):  

 (ri−1,t − )2 

gi t, = − − +(1 ) gi−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. Smoothed realized volatility is defined as the variable in the spirit of 

MIDAS regression. The following t is the specification for the MIDAS filtering.  

K 

t = +m rv k ( )w RV1t k−                                                  (3)  

k−1 

 Monthly  smoothed  realized  volatility  is  denoted  by  

Nt 

(RVt ri t2, ) 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.  

t = +m rv k ( )w RV1t k− gepu k ( )w GEPU1 t k−                   

 k=1 k=1                       (4)  

The log difference of GEPUt 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 

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

utilize the estimated daily total variance i t2, as a measure of the total variance's accuracy. The realized 

total volatility is denoted ri t2, 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 zt , , and the conditional variance  

process, t2 , has the form:  

 t2 = +k t2−1 + t2−1                                       (7)                    

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.  

 MSE= 1 T ( ˆt2 − t2 2)                                                                 (8)  

T i=1 

 

RMSE= T1 iT=1 ( ˆt2 − t2 2)                                        (9)  

90          J. Econ. Int. Finance 

 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 

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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 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, 

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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  

Botswana, Mauritius, and South Africa respectively. In all cases (full sample and in-sample), the 

coefficient of the unconditional mean for stock returns  is not significant, except for South Africa.  

To assess  efficacy of  the  models  GARCH  and GARCH-MIDAS-RV+OCDS/GARCH-MIDAS-

RV+GEPU, we employed the optimal log-likelihood function (Log-L), the Akaike information criterion 

(AIC), and the Bayesian information criterion (BIC). Table 7 presents the insample   results.  Upon   

comparison   of   the   traditional GARCH (1, 1) model with GARCH-MIDAS-RV+OCDS based on 

criterion information and log-likelihood function selection, the GARCH-MIDAS-RV+OCDS model 

outperformed GARCH (1, 1).  

Comparing  the  fitness  of  GARCH  (1,1) and GARCH- MIDAS-RV+GEPU models  shows  that  the  

latter   has superior fitness for the given countries. 

Heteroscedasticity  

 5. In-sample estimates of the GARCH-MIDAS model for three stock returns.  

 
    Oil consumption 

demand shock  

  

Botswa

na  

-1.586e-06 

(3.466e-05)  

0.179*** 

(0.007)  

0.317*** 

(0.014)  

0.076*** 

(0.002)  

6.744*** 

(1.169)  

0.001*** 

(1.078e-05)  

Mauriti

us  

3.449e-06 

(2.903e-05)  

0.251*** 

(0.019)  

0.444*** 

(0.039)  

0.001*** 

(4.147e-04)  

2.096*** 

(0.501)  

0.001*** 

(2.958e-05)  

South 

Africa  

-1.991e-04** 

(8.980e-05)  

0.083*** 

(0.010)  

0.881*** 

(0.018)  

0.119*** 

(0.037)  

8.639 

(10.173)  

0.004*** 

(5.735e-04)  

           

   GEPU     

       

       

Variable         
RV   

w 
 

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Table 1. Descriptive statistics of the GEPU index, stock index series and oil consumption.  

 Variable  Obs.  Freq.  Mean  Median  Min  Max  Std.  Skew.  Kurt.  

Botswana  3258  Daily  8.074  7.599  6.074  11.097  1.205  0.610  2.292  

Mauritius  3258  Daily  617.048  1.916  1.001  9986.00  7641.027  12.347  153.547  

South Af.  3258  Daily  2.766  3.085  1.086  4.226  0.784  -0.407  1.820  

GEPUindex  168  Month  171.899  151.220  79.848  437.144  70.618  1.179  4.003  

Oilcons  168  Month  0.139  0.629  -2.420  8.732  4.210  -0.929  6.354  

 Source: Author calculations using the data of the regression (www.investing.com and 

www.policyuncertainty.com). GEPU = Global economic policy uncertainty; oilcons = oil 

consumption.  

Table 2. Descriptive statistics of the GEPU change rate, stock return series and oil consumption.  

 Variable  

Botswana  

Obs.  

3257  

Freq.  

Daily  

Mean  

-6.46E-05  

Median  

0.000  

Min -

1.897  

Max  

2.074  

Std. 

0.151  

Skew.  

0.852  

Kurt. 

63.024  

Mauritius  3257  Daily  -0.003  -0.002  -499.51  499.934  17.496  0.003  814.215  

South Af  3257  Daily  -0.011  -0.018  -3.970  4.115  0.565  0.405  9.209  

URGEPU  167  Monthly  5.073  5.019  4.380  6.080  0.376  0.462  2.376  

Oilcons  93  Monthly  0.751  0.913  -2.859  2.167  0.928  -0.869  4.260  

 Source: Author calculations using the data of the regression (www.investing.com and 

www.policyuncertainty.com). GEPU=Global economic policy uncertainty; oilcons=oil consumption; 

URGEPU=GEPU change rate.  

Table 3. Statistical properties of GEPU, stock return series and oil consumption.  

 Variable  ADF  PP  VR  JB  ARCH  

Botswana  -15.302***  -63.621***  4.819***  489629.5***  188.165***  

Mauritius  -12.903***  -57.140***  11.977***  89360408***  365.836***  

South Af  -42.784***  -57.647***  13.072***  5324.812***  687.59***  

URGEPU  -4.711***  -4.563***  3.019**  8.705**  0.308  

Oilcons  0.360**  -9.015***  3.188***  17.853***  0.110  

 *** and ** denote significance at 1 and 5% levels, respectively. ADF=Augmented Dickey-Fuller; 

PP=PhillipsPerron; VR=Vratio; JB=Jarque-Bera; GEPU=global economic policy uncertainty; 

oilcons=oil consumption; URGEPU=GEPU change rate.  

 

 

 

 

 

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Botswa

na  

-1.020e-05 

(3.861e-05)  

0.050*** 

(0.003)  

0.900*** 

(0.004)  

0.056** 

(0.025)  

6.22 

(1.620e+1

6)  

0.044** 

(0.0192)  

Mauriti

us  

8.396e-06 

(2.943e-05)  

0.265*** 

(0.020)  

0.362*** 

(0.045)  

0.001*** 

(4.052e-04)  

2.105*** 

(0.483)  

0.001*** 

(2.690e-05)  

South 

Africa  

-2.087e-04** 

(8.923e-05)  

0.087*** 

(0.013)  

0.872*** 

(0.023)  

0.126*** 

(0.033)  

8.385 

(8.906)  

0.003*** 

(5.465e-04)  

***, ** and * represent 1, 5, and 10% level of significance, respectively. GARCH = generalized 

autoregressive conditional heteroskedasticity.  

 Table 6. Full sample estimates of GARCH-MIDAS for three stock returns.  

 
Botswana  South Africa  

GARCH-MIDAS+Oil 

consumption  

  

  

  

oil  

1.510e-05 (2.680)  

0.157*** (5.975e-

03)  

0.341*** (0.014)  

0.117*** (1.971e-3)  

-1.731e-05 (2.807e-

05)  

0.316*** (0.015)  

0.441*** (0.020)  

0.170*** (5.266e-

03)  

-2.064e-04** 

(8.224e-05)  

0.100*** (0.011)  

0.843 *** (0.019)  

0.119 *** (0.019)-  

 w  6.030*** (0.388)  26.616*** (1.893)  8.168** (4.130)  

 m  1.09e-03*** 

(8.866e-06)  

1.271e-03*** 

(4.473e-05)  

4.083e-03*** 

(3.156e-4)  

          

 Table 4. In-sample estimates of the GARCH model for three stock returns.  

 
 Variabl
e  

k        

Botswana  -0.005(0.006)  0.011***(3.780e-
04)  

0.264***(0.012)  0.272***(0.021)  

Mauritius  0.004***(5.400e-
04)  

0.243***0.339)  0.006***(0.001)  8.960e-
04***(2.080e-
04)  

South Africa  0.004*** (0.001)  0.082***(0.008)  0.901***(0.009)  -0.019**(0.008)  

***, ** and * represent 1, 5, and 10% level of significance, respectively. GARCH = Generalized 

autoregressive conditional  

 

2008 - 2022   Mauritius   

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GARCH-MIDAS+GEPU  

  

  

  

gepu  

1.653e-05 (0.532)  

0.151*** (5.688e-

03) 0.342*** 

(0.0142)  

0.016*** (5.085e-

04)  

-3.119e-05 (3.227e-

05)  

0.050*** (1.720e-

03) 0.900*** 

(0.010)  

0.099*** (6.770e-

03)  

2.008e-04** 

(8.206e-05)  

0.106*** (0.011)  

0.833*** (0.020)  

0.014*** (4.737e-

03)  

 w  5.681*** (0.290)  5*** (0.133)  8.118** (4.026)  

 m  1.113e-06*** 

(1.894e-08)  

-1.028e-6*** 

(1.754e-07)  

1.711e-05*** 

(2.613e-06)  

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.  

This is owing to the smaller AIC and BIC, and larger Log-  

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 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 consumption demand shock   

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  

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 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.  

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

MSE  RMSE Model  

  

  

  

 

 

 

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 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 

  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#  

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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.  

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