Examining the currency-equity nexus in frontier African markets: a wavelet-based approach

Gyasi, Genevieve; Frimpong, Joseph Magnus; Mireku, Kwame Article Examining the currency-equity nexus in frontier African markets: a wavelet-based approach Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Gyasi, Genevieve; Frimpong, Joseph Magnus; Mireku, Kwame (2024) : Examining the currency-equity nexus in frontier African markets: a wavelet-based approach, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 12, Iss. 1, pp. 1-19, https://doi.org/10.1080/23322039.2024.2399947 This Version is available at: https://hdl.handle.net/10419/321591 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Economics & Finance ISSN: 2332-2039 (Online) Journal homepage: www.tandfonline.com/journals/oaef20 Examining the currency-equity nexus in frontier African markets: a wavelet-based approach Genevieve Gyasi, Joseph Magnus Frimpong & Kwame Mireku To cite this article: Genevieve Gyasi, Joseph Magnus Frimpong & Kwame Mireku (2024) Examining the currency-equity nexus in frontier African markets: a wavelet-based approach, Cogent Economics & Finance, 12:1, 2399947, DOI: 10.1080/23322039.2024.2399947 To link to this article: https://doi.org/10.1080/23322039.2024.2399947 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group Published online: 06 Sep 2024. Submit your article to this journal Article views: 645 View related articles View Crossmark data Citing articles: 2 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 FINANCIAL ECONOMICS | REVIEW ARTICLE Examining the currency-equity nexus in frontier African markets: a wavelet-based approach Genevieve Gyasi a , Joseph Magnus Frimpong b and Kwame Mireku b a Department of Entrepreneurship and Business Science, University of Energy and Natural Resources, Fiapre, Ghana; b Department of Finance, School of Business, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana ABSTRACT This research examines the co-movement between exchange rates and equity prices in a selection of frontier African markets (Ghana, Mauritius, and Tunisia). The analysis encompasses data from 4 January 2010 to 31 March 2023. Employing advanced econometric techniques, the study investigates the interconnectedness of frontier markets and the direction of volatility spillovers between currency and equity markets. Our findings revel that Ghana, Mauritius, and Tunisia are characterized by strong sensitivity to price variations and high volatility. Moreover, significant volatility transmission and spillover effects are observed across the selected markets. Finally, the analysis finds the presence of non-linear dynamics in both the time and frequency domains. In light of these findings, policymakers and investors should incorporate the potential for abrupt and persistent changes, as well as volatility spillovers, into their decision-making processes when considering investments in the Ghana, Mauritius, and Tunisia markets. This research is anticipated to contribute to the development of more robust investment strategies for managing risk exposure within diversified portfolios. IMPACT STATEMENT This study investigates the relationship between currency and equity markets in frontier African economies using a wavelet-based approach. Focusing on Ghana, Mauritius, and Tunisia, it uncovers significant volatility spillovers and complex, time-varying dynamics. The findings reveal non-linear market behaviors, emphasizing the need for adaptive investment strategies in these volatile regions. By offering deeper insights into the currency-equity nexus, the research provides valuable guidance for investors and policymakers, enabling more informed decision-making and the development of resilient strategies for managing risks in frontier African markets. ARTICLE HISTORY Received 22 May 2024 Revised 23 August 2024 Accepted 29 August 2024 KEYWORDS Exchange rate; frontier markets; Africa; stock markets; wavelet transformation JEL CLASSIFICATION G10; G11; G15; O16; O11 SUBJECTS Statistics for Business, Finance & Economics; Macroeconomics; International Economics; Finance; Financial Mathematics; Mathematical Finance; Quantitative Finance; Economics 1. Introduction This study explores the co-movement between exchange rates and stock market performance in frontier African economies. We focus on three markets with significant capitalization which are Ghana, Mauritius, and Tunisia. We address the dearth of research on the impact of the currency-equity effect on the financial systems of selected countries. These countries were selected because they have the largest capitalization among frontier African economies Tunisia ($7.2BN), Mauritius ($7.8BN), and Ghana ($7.5BN) (African Securities Exchanges Association (ASEA) and Oxford Business Group (OBG), 2022). An examination of the selected frontier economies (Mauritius, Tunisia, and Ghana) reveals recent depreciation episodes in their respective currencies (Mauritian rupee, Tunisian dinar, and Ghanaian cedi). Notably, these depreciations coincided with the most significant negative impact observed on their equity capital performance, particularly during the pandemic. The equity capital market fell about 21% for Mauritius, 9.1% for Tunisia, and 6.5% for Ghana according to the Oxford Business Group report (ASEA, 2022). Frontier African markets account for 60% of Sub-Saharan gross domestic product (GDP) and population (International Monetary CONTACT Gyasi Genevieve [email protected] Department of Entrepreneurship and Business Science, University of Energy and Natural Resources, Fiapre, Ghana. ß2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. COGENT ECONOMICS & FINANCE 2024, VOL. 12, NO. 1, 2399947 https://doi.org/10.1080/23322039.2024.2399947 Fund (IMF), 2023). A significant share (approximately two-thirds) of small, open economies in Sub-Saharan Africa and Northern Africa exhibit heightened vulnerability to exchange rate volatility. This vulnerability stems from different factors, including free-floating exchange rate regimes and dependence on external economic conditions, current account deficits, economic slowdowns, inflationary pressures, and rising interest rates. As a consequence, African economies face potential disruptions to their overall economic performance and equity stock market stability (Njindan Iyke, 2017). These variations have intensified global risk aversion, prompting a flight to safe-haven assets and a decline in investment inflows toward frontier African markets. Consequently, despite their abundant natural resources, in frontier African countries, they continue to display a limited presence in global trade, foreign direct investment, and portfolio investment (Klagge & Zademach, 2018). The currencies of Sub-Saharan Africa’s frontier economies have displayed a declining trend since the 2008/2009 global financial crisis. This decline has been expanded by recent events, including the COVID-19 pandemic and the Russia–Ukraine war, which have significantly affected critical sectors such as financial, supply chains, and energy markets (Agyei et al., 2022; Amewu et al., 2022). The increased vulnerability of frontier markets in Sub-Saharan and North Africa, represents an important section of the region’s economic scope, highlighting the need for more investigation to bring understanding to the internal dynamics of African markets. The multifaceted nature of the relationship between equity stock markets and currency markets is, as seen in existing empirical literature with a significant return of co-movement and volatility spillover, found in some emerging and frontier markets while others have examined the intricate dynamics between currency fluctuations and equity market performance in these volatile regions (Bouri et al., 2018,2019,2020; Tang & Yao, 2018). Declines in currency prices have often resulted in the fall of stock prices as capital outflows increase while inflows fall alongside a decline in investor confidence (Ahmad et al., 2016; Alagidede & Panagiotidis, 2009; Patro et al., 2014). However, the effect and nature of the relationship vary from trade balances, foreign exchange reserves, and macroeconomic policies exerting their influence in some cases (Aloui et al., 2018; Aloui & Hkiri, 2014; Chkili & Nguyen, 2014). In the shortand long-term countries with strong fundamental policies have displayed strong comovements patterns (Balcilar et al., 2021). Also, stock prices are a contributing element in predicting changes in exchange rate prices with weak unidirectional asymmetric causal effect from exchange rate to stock prices (Xie et al., 2020). Coronado et al. (2021) found instantaneous spillovers across the returns for currency and stock markets with underlying portfolio diversification for investors to take advantage in the US market. Usman et al. (2022) found the local stock index and SP500 index contributed to the extreme shocks in currency returns. Furthermore, Chkili (2012) examined the time-varying relationship between exchange rates and stock returns in emerging markets, highlighting the impact of economic and financial crises. In a systematic review Obuya et al. (2024) found exchange rate volatility to have a positive effect on stock market returns in developed countries. Moreover, other studies on BRICS, Asian, and some African economies have found the following; Aydin et al. (2023) found a relationship between exchange rates and stock prices for ASEAN and BRICS countries before COVID-19 confirming the results from Narayan et al. (2020) study on Japan. Using the wavelet analysis Mohamed Dahir et al. (2018) found that for BRICS countries a positive relationship exists for Brazil and Russia, a negative relationship for South Africa, and no relationship for China. In the work of Hussain et al. (2023) exchange rate volatility was found to be connected to stock return volatilities during pandemicinduced crises for Russia, India, Brazil, and South Africa with weak volatility connected from China to any of the other countries in the BRICS. Using nonlinear dynamic analysis on Asian countries found volatility effects were present between different asset markets, showing a bi-directional relationship between stock prices and exchange rates (Sakemoto, 2017). Again, in Turkey, a negative unidirectional relationship was found between the stock market and exchange rates however at different frequencies using the wavelet coherence approach (He et al., 2021). Hung (2019) examined central and eastern European countries’temporal significant financial contagion between stock markets and exchange rate markets using the GARCHBEKK framework alongside the constant and dynamic conditional correlation (CCC and DCC) models. Kumar (2013) found a bi-directional volatility spillover between stock markets and exchange rates among IBSA countries. In Tanzania, stock prices have a unidirectional effect on exchange rates (John & Kisava, 2017). Mkhombo and Phiri (2023) that comovements between exchange rates and stock returns occur during periods of high inflation and lower interest rate movements. Xu et al. (2022) used advanced 2 G. GYASI, J. MAGNUS FRIMPONG AND K. MIREKU econometric models to analyze the dynamic interactions between exchange rates, oil prices, and stock markets in emerging economies, revealing complex interdependencies and the impact of global risk factors. Nit ,oi et al. (2018) explored the time-frequency co-movement between stock markets and exchange rates in frontier markets, emphasizing the significance of global financial cycles and regional economic policies. Salisu et al. (2022) emerging markets volatility responds positively to geopolitical risk. Sui and Sun (2016) studied spillover effects between exchange rates and stock markets in emerging economies, emphasizing the impact of global economic uncertainty. Furthermore, Basher et al. (2012) investigated the dynamic interactions between exchange rates, oil prices, and stock markets in frontier markets, uncovering complex interdependencies and the effects of external shocks. From empirical literature, comprehensive studies have explored currency and stock market volatility in developed, and emerging economies, and frontier markets in other regions whereas the focus on African Frontier markets remains limited (Jamil & Mobeen, 2021; Lee, 2009; Sayed & Charteris, 2022).  Zivkov et al. (2021) commendably identified the most liquid African stock markets and short-term volatility spillovers within select markets. However, their work does not delineate the optimal investment horizon for frontier African markets. This knowledge gap necessitates further investigation to identify the timeframe that maximizes investor returns within this asset class and how these markets respond to exchange rate fluctuations and global events. Our paper addresses the gap by investigating the nexus between currency volatilities and selected individual frontier African equity markets (Ghana, Mauritius, Tunisia). We employ the continuous Morlet wavelet transform (CMWT) to examine the co-movements across different time scales (Omane-Adjepong & Dramani, 2018). This approach seeks to capture both time (variant and frequency) features which in turn provides an understanding of the linkages that exist within the selected individual economies. In using high-frequency data to examine very short-term to long-term horizons, the CMWT provides understating aiding in investment decision-making in the selected frontier African currency-equity markets. The findings from this study will equip investors with a profound thought of the dynamic relationship between exchange rates and stock market performance in frontier African economies. This knowledge will empower them to make informed investment decisions and potentially identify optimal entry or exit points within these markets. The article is sectioned as follows: Section 2 presents the empirical methodology; Section 3 presents the empirical results. Section 4 presents a discussion of concluding remarks and gives implications of the key findings. 2. Empirical methodology In applying the wavelet analysis, we first consider the linear and non-linear causal relationship between the currency and equity markets by using the Granger test (Granger, 1969) as a conventional method for testing parametric linear, time series model by estimating the conditional mean, and the Diks and Panchenko (2005,2006), hereafter the DP test, to detect Granger non-causality which is more consistent and robust against other mean and non-linear variance techniques (see Akosah et al., 2020). We proceed further to employ the continuous wavelet transform to examine co-movement in the currency-equity market. 2.1. Linear granger causality framework The Granger technique (Granger, 1969) is used to explore informational linkages between stock and currency markets. Given any two stationary data pairs, say EStand CUt, say variable EStGranger causes CUt linearly provided lags of EStoffer useful information for explaining the current values of Bt, and vice versa. Where EStis equity markets and CUtrepresent currency markets. The bivariate Linear Granger causality is specified in a VAR system as follows: ESt¼u1þX k i¼1 a1iESt−iþX k j¼1 b1iCUt−jþe1t(1) COGENT ECONOMICS & FINANCE 3 CUt¼u2þX k i¼1 a2iESt−iþX k j¼1 b2iCUt−jþe2t(2) where u1and u2are the constant terms of the system of the equation; aand bdenote estimated coefficients; kis the optimal lag order based on the Akaike Information Criterion (AIC) and e1tand e2trepresent residuals from the VAR model. Again, the null hypothesis for Eq. (1) states that changes in the price of equity stock markets do not Granger cause currency volatilities. In Eq. (2), currency volatilities do not Granger cause changes in the price of equity stock markets. Additionally, we employ the Wald test to examine the joint hypothesis for a1i¼0 and b2j¼0: However, due to the low power limitation that characterizes linear models when detecting non-linear informational linkages between variables, higher non-linear predictive power is often not captured by the linear model, thus we employ the non-linear model (Omane-Adjepong & Alagidede, 2019). 2.2. Non-linear granger causality Among the various non-linear models, we employ the non-linear test by Diks and Panchenko (2005, 2006), hereafter the DP test, due to its consistency, flexibility, and robustness. This test is built on the HJ and initial nonparametric tests (Baek and Brock, 1992; Hiemstra and Jones, 1994). The DP test diminishes the issue of over-rejection rates that characterize the HJ test under the null hypothesis. Our adoption of the D&P test is, thus, not misplaced as we denote the information set for the lags of EStand CUtrespectively as CX,tand CY,t, before time t¼1; and ‘῀’the equivalent distribution (Diks & Panchenko, 2005,2006). Consequently, we the modeling section of Bekiros and Diks (2008) with the replacement of X, Y, and Q for variables that represent our selected variables of interest we assume that Atis said to Granger cause Btprovided that, ðCUtþ1...,CUtþkÞjðCX,1...,CY,kÞðCUtþ1...,CUtþkÞjCX,1(3) where kis an integer k 1, representing the forecasting horizon. For the lag vectors Alr t¼ðESt−lrþ1,EStÞ and CUls t¼ðCUt−lsþ1,CUtÞ, given (lrþls1) we test the conditional independence using a determinate number of lags lrand lsUnder a null hypothesis of: H0:CUtþ1jðESlr t:CUls tÞCUtþ1jCUls tÞ(4) We further reduce the two variable representations and use A, B, and W due to ease in modeling adopting the Bekiros and Diks (2008) model. Considering that the null hypothesis of Granger non-causality is an assertion about the invariant distribution of ðlrþlsþ1Þdimensional vector Qt¼ðAlr t,Bls t,WtÞ where the lead vector Wt¼Btþ1, the time index is dropped and written as Q ¼(A, B, W) (see Bekiros & Diks, 2008). We set lrþls¼1, and k ¼1 and assume the trivariate in Q follows a continuous random variable. The null hypothesis of the non-causality in (4) may be redefined as a joint probability density function fA,B,Wða, b, wÞwith its marginal satisfying the following condition: fA,B,Wða, b, wÞ fBðbÞ¼fA,Bða, bÞ fBðbÞfB,Wðb, wÞ fBðbÞ(5) For every determinate value of bfrom our test, the continuous random A and W are independent conditioned on Y ¼y. Therefore, under the revised null hypothesis H0, q¼EfA,B,WA, B, W ðÞ fBB ðÞ −fA,BA, B ðÞ fB,WB, W ðÞ  (6) The estimate of q is expressed based on the indicator function as: Tnԑ ðÞ¼ð2ԑÞ−mA−2mB−mW nðn−1Þðn−2ÞX iX k,k6¼iX jj6¼iðIABW ik IB ij −IAB ik IBW ij Þ (7) Wherever, IQ ij ¼IðjjQi−Qjjj <ԑ), where K¼j, is not excluded explicitly as they each contribute zero to the t-statistic (Broock et al., 1996). 4 G. GYASI, J. MAGNUS FRIMPONG AND K. MIREKU We represent the local density estimators of a mQ, variate as a random vector of Q at Qias ^ fQQi ðÞ ¼ð2ԑÞmQ n−1XIQ ij jj6¼i(8) The test statistic reduces to: Tnԑ ðÞ¼n−1 ðÞ nn−2 ðÞ X ið^ fA,B,WAi,Bi,Wi ðÞ ^ fBBi ðÞ − ^ fA,BAi,Bi ðÞ ^ fB,WBi,Wi ðÞ Þ(9) The appropriate sequence ԑnfor bandwidth, values should have estimators and test statistics that are consistent and match the weighted average of the local contributions ^ fA,B,WðA,B,WÞ^ fBðBÞ− ^ fA,BðA,BÞ^ fB,WðB,WÞwhich tend to be zero in for the probability under the proposed null hypothesis. Given that mA¼mB¼mW¼1, to achieve a consistent test, the bandwidth is selected based on the sample size (see Powell & Stoker, 1996). when, en¼Xn −bfor a constant X to be positive (X>0) and, (be(0.25, 0.67), This will help to achieve an asymptotic and normally distributed t-statistic when the dependence between is absent for Qi:We extend this to our time series following the Denker and Keller (1983) mixing conditions on the assumption that the covariances between the local density estimators are accounted for. From the linear and non-linear Granger causality, the use of the VAR system allows for the extraction of linear predictive power leaving incremental predictive power that could account for the non-linear causality or predictive power in the series. On the other hand, the non-linear Diks and Panchenko (DP) test and Granger causality test face significant limitations with non-stationary time series data, prevalent in financial markets. The DP test, designed for detecting non-linear dependencies, struggles with structural breaks and varying volatility, often yielding false positives or inconclusive results. Similarly, the Granger causality test assumes stationarity, leading to misleading inferences when applied to non-stationary data, as it violates underlying statistical assumptions. These constraints underscore the necessity for more robust methods like wavelet-based strategies, which effectively handle non-stationarity by capturing localized time-frequency patterns, offering a more accurate analysis of financial time series. We, therefore, employ the wavelet approach as it captures the time and frequency features of our series and offers a robust technique that is useful in examining the non-stationary nature of time series. In comparing the wavelet approach to vector autoregression (VAR) models and dynamic conditional correlation (DCC) models respectively, we find that the VAR examines linear interdependencies over multiple time series while the DCC examines time-varying correlations underscoring the dynamics prevalent in the long-term linear relationships and dynamic correlations. Again, the wavelet technique considers abrupt changes and varying frequencies outperforming the VAR and DCC models which could lead to misleading conclusions due to the presence of trends, volatility, and structural breaks underpinning the data if not correctly examined. Ensuring that the true relationships are not obscured the wavelet approach thoroughly examines the data by identifying any underlying issues such as economic cycles, and market shocks among others, and accurately models, captures, and ultimately yields robust and reliable results. 2.3. Continuous wavelet transformation (CWT) and coherence We employ the Morlet wavelet to examine the frequency and time-space behavior of the series due to the presence of high properties in the localized frequency and time (Aloui et al., 2018; Wu et al., 2020). We use the CWT by examining the variations in non-stationary variables across time and space. Firstly, we decompose the time series to ensure that localized time-frequency space and zero means are a function of wavelet transformation. We further extract the needed information from the local neighborhood using the series that has been decomposed. The wavelet coherence (WC) is specified below as: uu,st ðÞ¼1 ffiffis pwt−u s  w∙ ðÞ2L2R ðÞ (1) We denote 1 ffiffis pas the stabilization element validating unit for the wavelet disparity. jjwu,st ðÞ jj2¼1;uis the location boundary, which presents the exact location of the wavelet. COGENT ECONOMICS & FINANCE 5 Where sis the expansion level located in the boundary, which determines the spread of the wavelet. 2.4. Morlet wavelet uMt ðÞ¼1 p1=4eix0te−t2=2(2) We denote the significant incidence of the wavelet by x0as 6. The convolution on the discrete sequence, scaled, and translated wavelet used in the CWT is: Wsu,s ðÞ ¼ð1 -1 xt ðÞ1 ffiffis pwt−u s  dt (3) Extracting the wavelet wð∙Þon the time series, Wsðu,sÞis obtained. Also, the main advantage lies in the CWT’s ability to decompose the series and rebuild it under the function: xt ðÞ2L2ðÞ :xt ðÞ¼1 cu Ð1 0Ð1 0Wsu,s ðÞ wu,st ðÞ du  ds s2,s>0 (4) From Eq. (4), the power spectrum is examined using the adjusted measurement jjvjj2¼1 cuð1 0ð1 -1jWsu,s ðÞ jj2du  ds s2,s>0 (5) In modeling out the red noise from the spectrum series background AR (1) provides the allowance. Based on the null hypothesis, the pockets found in the wavelet power spectrum (WPS) allow for the peaks to be examined to establish their significant levels. The local WPS established from the distribution using the Monte Carlo simulation presents the individual time (n) and scale (s) (see Torrence and Webster, 1999). DWx nsÞ2  d2 x <p 2 43 5)1 2pfv2 v(6) fis represented as pf, the mean spectrum at Fourier frequency from the series. The wavelet scale is aligned with the Fourier frequently. (s 1/f), v ¼1 is the real wavelet, and v ¼2 denotes the complex wavelet where d2 xis the modified variable. The behavior of time-frequency in the currency-equity market is investigated using the CWT to locate the common power between the series (Aloui et al., 2018). The region’s location is found using the power of CWT to locate the co-movements in the time-frequency dynamics. Using a specific variable series (v) against another (Y), the wavelet spectra of the individual series Wx nðsÞand Wx nðsÞare used in determining the CWT in the series as: Wxy ns ðÞ¼Wx nðsÞWx nðsÞ(7) The highest common power from the CWT shows the area, time, and space with WY nðsÞas the intricate conjugate of WY nðsÞ: 2.5. Cross-wavelet power The cross-wavelet power jWxy nðsÞj from the CWT indicates the covariance is shared on all individual levels. In this case, a two-time series from the W.C plot represented by v¼vn fg and y ¼yn fg , which is the frequency and time gaps for the series covary. This allows for the detection of co-movements among the variables showing the absolute square values from the WC plots for the normalized WPS. 6 G. GYASI, J. MAGNUS FRIMPONG AND K. MIREKU The coefficient of the square wavelet is: R2x,y ðÞ ¼jSðs−1Wxy u,s ðÞ Þj2 Sðs−1jWxu,s ðÞ Þj2ÞSðs−1jWyu,s ðÞ Þj2Þ(8) The smoothing parameter (S) balances the resolution and significance level removing all issues related to WPS and the wavelet cross-spectrum (WCS). 2.6. Wavelet coherence The inequality equation 0 R2ðx,yÞ1 represents the coherence with values from 0 to 1. 0 represents a low correlation and 1 is a robust correlation value. The lag oscillation is positioned in the phase form for the variables as the frequency with ;xy, explaining the phase difference between v(t) and y(t) as: ;xy ¼tan −1IWxy n  RWxy n  ! ,;xy 2−p,p ½ (9) The smoothened CWT is in two parts when Iand R, representing the imaginary and real parts. The directional arrows are also indicated on the WC plot, with unique different phase forms of the variables. The phase forms of a(t) and b(t) where arrows point to the right (in-phase) and the left (antiphase), downwards the second variable is in lead, and upwards the first variable in lead (see b(t)/a(t)). 3. Empirical results We consider historical time series data for leading stock frontier markets with the highest market capitalization (as of 4 January 2010) and a relatively lengthier data span. In no particular order of arrangement, stock markets; Ghana Stock Exchange Composite Index (GSE-CI), Tunisia stock market (TUNINDEX), the stock exchange of Mauritius (SEMDEX); exchange rate market; the United States Dollar against the Ghanaian Cedi (US/GHS); Tunisian dinar (US/TND), Mauritian rupee (US/MUR), are examined. The daily stock returns and local currency to the United States dollar rate dataset spans from 4 January 2010 to 31 March 2023. African frontier markets have experienced significant developments from 2010 to 2023, reflecting both opportunities and challenges. These markets, characterized by rapid economic growth and increasing investor interest, have seen substantial changes in infrastructure, regulatory frameworks, and market performance. Frontier markets in Africa such as Tunisia, Ghana, and Mauritius since 2010 have experienced robust economic growth driven by the telecommunications industry, banking, and natural resource sector. Since 2012 many African economies have taken financial, transparency, and efficiency, to improve growth and development. The Securities and Exchange Commission (SEC) of Africa has introduced new regulations, and improved corporate governance, and investor protection. These reforms implemented in capital markets have increased foreign investment and improved market liquidity. Through technological advancement, African frontier markets stand a chance to influence the transformational agenda of Africa’s capital market. Fintech and banking solutions have also increased financial inclusion, improved participation, and revolutionized financial transactions leading to market development. Since the introduction of these reforms market performance has been mixed with significant gains and downturns occurring with influence from external and global factors such as currency, and interest rate fluctuations, political instability, corruption, inadequate infrastructure, global financial crises health pandemic (COVID-19), wars, and commodity price variations, among others. Volatility due to oil price and exchange rate shocks has riddled some frontier markets while others have shown a high level of resilience coupled with steady growth due to the diversification efforts of their respective governments. The inflow of foreign investment an important element of the development of frontier markets is hinged on investor interest in higher returns in low-yield markets which African markets often find themselves. The low influx of capital through private equity and venture capital investments needs to grow to support the expansion of businesses and encourage start-ups in African frontier markets. Global disruptions, and a growing need for sustainable, environmental, social, and governance (ESG) in investment portfolios and COGENT ECONOMICS & FINANCE 7 4. Discussion This investigation employs wavelet analysis to comprehensively examine the time-frequency dynamics of co-movement between equity and currency markets in Ghana, Mauritius, and Tunisia. Wavelet analysis offers a distinct advantage by enabling the exploration of how these variables interact at various temporal scales and how their interconnectedness evolves. Notably, wavelets excel at capturing non-stationary features embedded within the data series, a limitation that plagues alternative methodologies. Prior research has yielded conflicting results (Aloui & Hkiri, 2014; Rua, 2010), highlighting the superiority of wavelets in effectively capturing the erratic behavior of the series in both the time and frequency domains. The wavelet analysis reveals that co-movements beyond a four-year horizon fall outside the Cone of Influence (COI) in the long term. This signifies a lack of statistically significant relationships at these time scales, contrasting with prior studies that suggested persistent co-movements beyond 2048 days (Owusu Junior et al., 2018). On the other hand, we find significant co-movements are only noticeable within the 1024 days. Furthermore, the observed co-movement patterns exhibit heterogeneity, encompassing both in-phase (positive correlation) and anti-phase (negative correlation) relationships. The direction of causality, as indicated by the arrows pointing upwards (stock market leading) or downwards (exchange rate leading), varies across the examined countries and timeframes. Frontier markets in Ghana, Mauritius, and Tunisia exhibit a dynamic interplay between stock market fluctuations and exchange rate movements. Our research suggests that recurrent events can exert short-term pressures on exchange rates, potentially impacting stock market performance. Furthermore, disruptions that cast doubt on the sustainability of future dividend payments can trigger investor risk aversion behavior. In response to such events, investors may strategically withdraw capital from the stock market and redeploy it into longerterm instruments within the frontier market itself. This portfolio reallocation serves as a risk mitigation strategy, aiming to shield investor wealth from the potential volatility associated with these periodic events. Our findings further reveal that investors in the selected African frontier markets (Ghana, Mauritius, and Tunisia) often construct diversified investment portfolios. This diversification strategy is motivated, in part, by a desire to minimize exposure to systemic risks, including the potential for exchange rate fluctuations to negatively impact overall portfolio returns. African frontier markets examined in this study display the Arbitrage Pricing theory dynamics. This is evident in the propensity of the short-term markets to be sensitive to price volatility. However, the stock prices in these markets are often in equilibrium in the long-term. These disparities spur investors to engage in the diversification of their investment portfolios and implement hedging strategies that would mitigate their risk exposure and increase returns (Hammami & Boujelbene, 2022;Zaiane&Jrad,2020). The results also draw attention to the dynamic relationship between stock volatility and investor behavior as risk-averse investors on the African frontier reduce their transaction volumes during heightened volatility periods. Unlike risk-tolerant investors, who tend to increase their trading activity during these periods. Moreover, the type of diversification strategy may exert quantifiable transaction costs on market liquidity. 5. Conclusion A multitude of African frontier markets have been beset by a convergence of negative economic forces. These include export disruptions, a decline in the terms of trade, depreciation of local currencies relative to the US dollar, and a subsequent contraction in investment inflows and reduced competition. The cumulative impact of these challenges has been a pronounced scarcity of foreign exchange reserves, severely impeding both trade finance activities and foreign direct investment. In the past decade, a noteworthy policy shift has been observed within the monetary frameworks of many African frontier economies. A transition has occurred from fixed exchange rate regimes to more flexible exchange rate systems while initially beneficial and transitory, has not provided sustained advantages due to ongoing market volatility and spillover effects. Frontier equity markets in Africa are particularly prone to fluctuations, deterring both domestic and foreign investors and limiting capital mobilization. The interplay of demand and supply forces, originating from both domestic and international markets, continues to exert a significant influence on exchange rate dynamics. This dynamic can lead to the transmission of volatility and spillover effects across these selected African frontier markets. Frontier equity stock markets in Ghana, Mauritius, and Tunisia are characterized by a susceptibility to pronounced fluctuations and heightened volatility. These market 14 G. GYASI, J. MAGNUS FRIMPONG AND K. MIREKU dynamics act as a significant disincentive to investor participation, both domestic and foreign, thereby constraining the market’s capacity to mobilize capital for investment purposes. To mitigate this challenge, frontier economies should prioritize the implementation of a multifaceted policy framework designed to cultivate a more robust investor appetite for their stock markets. Potential policy interventions could encompass; the adoption of stricter regulatory frameworks and the promotion of enhanced corporate governance practices to foster a more stable and predictable market environment; the cultivation of a diversified range of value-added products within the domestic economy, creating attractive investment propositions for both local and international market participants; in the short term, a focus on essential imports, such as medicine, food, and fuel, in sectors where domestic production capacity remains limited; over the medium to long term, the prioritization of policies that foster self-sufficiency within key industries, with the ultimate goal of reducing reliance on exports and cultivating a more diversified and resilient economic structure; the introduction and subsequent regulation of innovative payment system technologies to facilitate streamlined foreign currency inflows into the stock market. Again, regulators and investors need to account for volatility spillovers and manage risk in the market. Strong and sound strategies from diversification, and portfolio rebalancing are important mechanisms to prevent adverse price movements due to extreme changes in macroeconomic factors in one market (Aloui et al., 2018; Cao et al., 2020; Du et al., 2020; Kolm & Ritter, 2019), all found that the various strategies above among others help in balancing the returns during high volatility periods on the market (Acerbi & Tasche, 2002; Pflug, 2000; Rockafellar & Uryasev, 2002). Furthermore monitoring, and implementing transparency measures to reduce third-party risk amount others can reduce cross-border spillovers they navigating the complex relationship between stock market prices and exchange rate changes (Chen et al., 2024; OECD, 2013). Our study finds that flexible exchange rate regimes enjoy short-lived advantages but does not examine the limited long-term benefits to enable a generalization due to the uniqueness of each market and country. There is a need for researchers to consider exploring the specific conditions under which advantages can be sustained under flexible exchange rate regimes. By addressing the shortcomings and implementing recommendations African markets can take advantage of opportunities for effective policy implementation to attract investors. Acknowledgment I acknowledge the Kwame Nkrumah University of Science and Technology, Kumasi, and the University of Energy and Natural Resources, Sunyani Ghana, for their support throughout this study. Authors’Contribution Genevieve Gyasi was involved in the conception and design, or analysis and interpretation of the data; the drafting of the paper, revising it critically for intellectual content; and the final approval of the version to be published. Joseph Magnus Frimpong and Kwame Mireku were involved in the conception and design, revising it critically for intellectual content; and the final approval of the version to be published. All authors agreed to be accountable for all aspects of the work. Disclosure statement No potential conflict of interest was reported by the author(s). Funding No funding was received. About the authors Genevieve Gyasi, PhD Finance, Lecturer/Researcher at the University of Energy and Natural Resources (UENR) in Ghana. My current research interest covers International Economics, Finance, and Econometrics topics in Developmental and Financial Economics in Africa. COGENT ECONOMICS & FINANCE 15 Joseph Magnus Frimpong, Professor of Finance at Kwame Nkrumah University of Science and Technology, Kumasi, Ghana. My research interest cover, financial econometrics, stock market finance, private sector development, and macro economic variables. Kwame Mireku, PhD finance, Lecturer/Researcher at Kwame Nkrumah University of Science and Technology, Kumasi, Ghana. My interest cover Financial literacy, macroeconomic variable and development. ORCID Genevieve Gyasi http://orcid.org/0000-0002-5144-116X Data availability statement The data that support the findings of this study are openly available at investing.com and the Ghana Stock Exchange. These data were derived from the following resources available in the public domain: https://www.investing.com/indices/ for all countries except Ghana’s equity data which was sourced from https://gse.com.gh/tradingand-data/ data available on reasonable request from the corresponding author Genevieve Gyasi. The data was obtained from investing.com and the Ghana Stock Exchange database. Daily data was obtained for all three countries and is easily accessible. All rights and permissions to the data used are under investing.com and the Ghana Stock Exchange. References Acerbi, C., & Tasche, D. 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EXC 0.876 0.19058 −0.049 0.51956 −1.486 0.93135 EXC6!GSE-CI   0.377 0.35308 0.629 0.26466 MAURITIUS SEMDEX !EXC       EXC!SEMDEX       TUNISIA TUNINDEX 6! EXC 0.767 0.2215     EXC6!TUNINDEX       COGENT ECONOMICS & FINANCE 19