American Research Journal of Economics, Finance and Management Volume 12 Issue 1, January-February 2024 ISSN: 2836-9416 Impact Factor: 5.57 Journal Homepage: https:// https://americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com Official Journal of America Serial Publication American Research Journal of Economics, Finance and Management americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com 23 | P a g e 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. mailto:contact@americaserial.com mailto:contact@americaserial.com American Research Journal of Economics, Finance and Management Volume 12 Issue 1, January-February 2024 ISSN: 2836-9416 Impact Factor: 5.57 Journal Homepage: https:// https://americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com Official Journal of America Serial Publication American Research Journal of Economics, Finance and Management americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com 24 | P a g e 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 mailto:contact@americaserial.com mailto:contact@americaserial.com American Research Journal of Economics, Finance and Management Volume 12 Issue 1, January-February 2024 ISSN: 2836-9416 Impact Factor: 5.57 Journal Homepage: https:// https://americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com Official Journal of America Serial Publication American Research Journal of Economics, Finance and Management americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com 25 | P a g e 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 mailto:contact@americaserial.com mailto:contact@americaserial.com American Research Journal of Economics, Finance and Management Volume 12 Issue 1, January-February 2024 ISSN: 2836-9416 Impact Factor: 5.57 Journal Homepage: https:// https://americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com Official Journal of America Serial Publication American Research Journal of Economics, Finance and Management americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com 26 | P a g e 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 mailto:contact@americaserial.com mailto:contact@americaserial.com American Research Journal of Economics, Finance and Management Volume 12 Issue 1, January-February 2024 ISSN: 2836-9416 Impact Factor: 5.57 Journal Homepage: https:// https://americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com Official Journal of America Serial Publication American Research Journal of Economics, Finance and Management americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com 27 | P a g e 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. mailto:contact@americaserial.com mailto:contact@americaserial.com American Research Journal of Economics, Finance and Management Volume 12 Issue 1, January-February 2024 ISSN: 2836-9416 Impact Factor: 5.57 Journal Homepage: https:// https://americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com Official Journal of America Serial Publication American Research Journal of Economics, Finance and Management americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com 28 | P a g e (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 mailto:contact@americaserial.com mailto:contact@americaserial.com American Research Journal of Economics, Finance and Management Volume 12 Issue 1, January-February 2024 ISSN: 2836-9416 Impact Factor: 5.57 Journal Homepage: https:// https://americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com Official Journal of America Serial Publication American Research Journal of Economics, Finance and Management americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com 29 | P a g e 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) mailto:contact@americaserial.com mailto:contact@americaserial.com American Research Journal of Economics, Finance and Management Volume 12 Issue 1, January-February 2024 ISSN: 2836-9416 Impact Factor: 5.57 Journal Homepage: https:// https://americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com Official Journal of America Serial Publication American Research Journal of Economics, Finance and Management americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com 30 | P a g e 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 mailto:contact@americaserial.com mailto:contact@americaserial.com American Research Journal of Economics, Finance and Management Volume 12 Issue 1, January-February 2024 ISSN: 2836-9416 Impact Factor: 5.57 Journal Homepage: https:// https://americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com Official Journal of America Serial Publication American Research Journal of Economics, Finance and Management americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com 31 | P a g e 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 mailto:contact@americaserial.com mailto:contact@americaserial.com http://www.investing.com/ http://www.investing.com/ American Research Journal of Economics, Finance and Management Volume 12 Issue 1, January-February 2024 ISSN: 2836-9416 Impact Factor: 5.57 Journal Homepage: https:// https://americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com Official Journal of America Serial Publication American Research Journal of Economics, Finance and Management americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com 32 | P a g e 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, mailto:contact@americaserial.com mailto:contact@americaserial.com American Research Journal of Economics, Finance and Management Volume 12 Issue 1, January-February 2024 ISSN: 2836-9416 Impact Factor: 5.57 Journal Homepage: https:// https://americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com Official Journal of America Serial Publication American Research Journal of Economics, Finance and Management americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com 33 | P a g e 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 mailto:contact@americaserial.com mailto:contact@americaserial.com American Research Journal of Economics, Finance and Management Volume 12 Issue 1, January-February 2024 ISSN: 2836-9416 Impact Factor: 5.57 Journal Homepage: https:// https://americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com Official Journal of America Serial Publication American Research Journal of Economics, Finance and Management americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com 34 | P a g e 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. mailto:contact@americaserial.com mailto:contact@americaserial.com http://www.investing.com/ http://www.investing.com/ http://www.policyuncertainty.com/ http://www.policyuncertainty.com/ http://www.policyuncertainty.com/ American Research Journal of Economics, Finance and Management Volume 12 Issue 1, January-February 2024 ISSN: 2836-9416 Impact Factor: 5.57 Journal Homepage: https:// https://americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com Official Journal of America Serial Publication American Research Journal of Economics, Finance and Management americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com 35 | P a g e 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 mailto:contact@americaserial.com mailto:contact@americaserial.com American Research Journal of Economics, Finance and Management Volume 12 Issue 1, January-February 2024 ISSN: 2836-9416 Impact Factor: 5.57 Journal Homepage: https:// https://americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com Official Journal of America Serial Publication American Research Journal of Economics, Finance and Management americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com 36 | P a g e 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 mailto:contact@americaserial.com mailto:contact@americaserial.com American Research Journal of Economics, Finance and Management Volume 12 Issue 1, January-February 2024 ISSN: 2836-9416 Impact Factor: 5.57 Journal Homepage: https:// https://americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com Official Journal of America Serial Publication American Research Journal of Economics, Finance and Management americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com 37 | P a g e 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# mailto:contact@americaserial.com mailto:contact@americaserial.com American Research Journal of Economics, Finance and Management Volume 12 Issue 1, January-February 2024 ISSN: 2836-9416 Impact Factor: 5.57 Journal Homepage: https:// https://americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com Official Journal of America Serial Publication American Research Journal of Economics, Finance and Management americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com 38 | P a g e 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. mailto:contact@americaserial.com mailto:contact@americaserial.com American Research Journal of Economics, Finance and Management Volume 12 Issue 1, January-February 2024 ISSN: 2836-9416 Impact Factor: 5.57 Journal Homepage: https:// https://americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com Official Journal of America Serial Publication American Research Journal of Economics, Finance and Management americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com 39 | P a g e 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. 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. mailto:contact@americaserial.com mailto:contact@americaserial.com American Research Journal of Economics, Finance and Management Volume 12 Issue 1, January-February 2024 ISSN: 2836-9416 Impact Factor: 5.57 Journal Homepage: https:// https://americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com Official Journal of America Serial Publication American Research Journal of Economics, Finance and Management americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com 40 | P a g e 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. 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. mailto:contact@americaserial.com mailto:contact@americaserial.com American Research Journal of Economics, Finance and Management Volume 12 Issue 1, January-February 2024 ISSN: 2836-9416 Impact Factor: 5.57 Journal Homepage: https:// https://americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com Official Journal of America Serial Publication American Research Journal of Economics, Finance and Management americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com 41 | P a g e 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. 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 mailto:contact@americaserial.com mailto:contact@americaserial.com American Research Journal of Economics, Finance and Management Volume 12 Issue 1, January-February 2024 ISSN: 2836-9416 Impact Factor: 5.57 Journal Homepage: https:// https://americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com Official Journal of America Serial Publication American Research Journal of Economics, Finance and Management americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com 42 | P a g e 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 7(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. 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. mailto:contact@americaserial.com mailto:contact@americaserial.com American Research Journal of Economics, Finance and Management Volume 12 Issue 1, January-February 2024 ISSN: 2836-9416 Impact Factor: 5.57 Journal Homepage: https:// https://americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com Official Journal of America Serial Publication American Research Journal of Economics, Finance and Management americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com 43 | P a g e 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. 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. mailto:contact@americaserial.com mailto:contact@americaserial.com 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://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- https://www.southsouthgalaxy.org/news/article-the- https://www.southsouthgalaxy.org/news/article-the- https://www.southsouthgalaxy.org/news/article-the- https://www.southsouthgalaxy.org/news/article-the- American Research Journal of Economics, Finance and Management Volume 12 Issue 1, January-February 2024 ISSN: 2836-9416 Impact Factor: 5.57 Journal Homepage: https:// https://americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com Official Journal of America Serial Publication American Research Journal of Economics, Finance and Management americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com 44 | P a g e 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. mailto:contact@americaserial.com mailto:contact@americaserial.com American Research Journal of Economics, Finance and Management Volume 12 Issue 1, January-February 2024 ISSN: 2836-9416 Impact Factor: 5.57 Journal Homepage: https:// https://americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com Official Journal of America Serial Publication American Research Journal of Economics, Finance and Management americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com 45 | P a g e 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. 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. mailto:contact@americaserial.com mailto:contact@americaserial.com