
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12007-5
10.1016/j.heliyon.2024.e35976
e35976
Research Article
On the partial impact of uncertainties on the nexus between macroeconomic fundamentals in West Africa
Nkrumah-Boadu Bernice berenicenkrumahboadu@gmail.com
⁎
Tweneboah George
Frimpong Siaw
Department of Finance, School of Business, University of Cape Coast, Cape Coast, Ghana
⁎ Corresponding author. berenicenkrumahboadu@gmail.com
13 8 2024
30 8 2024
13 8 2024
10 16 e3597626 2 2023
3 8 2024
7 8 2024
© 2024 The Authors. Published by Elsevier Ltd.
2024

https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
We investigate the degree of interconnectedness between stock returns and exchange rate returns, and the influence of some selected global uncertainty indices on such a relationship within a time-frequency domain in West Africa through the bi and partial wavelet approaches. The analysis was based on monthly observations from February 2013 to June 2023. The results highlight a negative correlation between stock return and exchange rates. The partial wavelet analysis evidence a significant effect of the global economic policy uncertainty, the implied oil market volatility, and the United States volatility index in driving the co-movements observed in the currency and stock markets. We also find a significant impact of the stock market on the currency market, underscoring the need for robust stock market policies. It is recommended that policymakers prioritize strategies aimed at boosting stock market stability and depth which can positively affect the currency markets. The significant influence of global uncertainties or shocks should not be disregarded in the formulation of policies regarding exchange rates and stock return integration at various investment horizons.

Keywords

Flow-oriented model
Stock-oriented model
Heterogeneity
Implied volatilities
Interdependencies
Wavelets
West Africa
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pmc1 Introduction

The global economy has prominently observed the high ranking held by the foreign exchange (forex) and stock markets in the overall classification of financial markets [1]. The significant volumes of transactions conducted in the currencies and stock markets internationally are most often valued in the US dollar, which is the world's widely used currency [[2], [3], [4]]. The extant literature has established mechanisms through which the stock and foreign exchange markets are connected. These two markets are connected through common indicators such as GDP, commodities, interest rates, inflation, and investment, among others. These factors have a combined effect on the two markets, tying them together [1,5]. Nevertheless, the stock-oriented and the flow-oriented models offer insights into the extent to which stocks and exchange rates are related.

The flow-oriented theory operates under the premise that changes in exchange rates affect trade balance, global competitiveness, and ultimately the real production of a country, which affects enterprises' stock values and cash flows [6]. This suggests that the trade or current account balance of a country affects the exchange rate [7,8]. On the other hand, the stock-oriented approach argues that changes in stock prices affect currency rates through the capital account and the financial account [9,10]. Impliedly, a surge in stock markets causes a shift in the portfolio from foreign assets to assets dominated in the native currency. This triggers a shift in the demand for currency, which in turn results in the appreciation or depreciation of the currency [[9], [10], [11], [12], [13], [14]].

The literature is replete with many empirical studies that have investigated the relationship between the two markets with varied findings [[15], [16], [17], [18], [19], [20]] and others. Consequently, this study focuses on the integration of African stocks and currencies markets. A common observation is that the African stock markets exhibit lower levels of global integration, primarily influenced by the reliance of their economies on foreign exchange. In recent years, the stock markets in Africa have undergone significant growth and development [21]. This underscores the current imperative to investigate the connection between stock returns and exchange rates. Exchange rates are of special significance due to their frequent fluctuations, elevated levels of uncertainty, and their integral role in supply and demand dynamics.

Regarding African markets, the existing literature has identified mixed or contradictory findings concerning this relationship as well (see Refs. [[22], [23], [24], [25], [26], [27], [28]] and others). Notions from these studies are mostly restricted to the application of static models. Narrowing down to West Africa, similar outcomes can be found, especially in the case of Nigeria and possibly Ghana. There are, however, limited studies conducted on the nexus between the Bourse Régionale des Valeurs Mobilières (BRVM) and exchange rate [[29], [30], [31]], thereby arousing a myopic view of the entire West African economy.

Aside from that, the empirical literature on the effect of uncertainty indices on the nexus between exchange rate and stock returns simultaneously is entirely missing, across time and frequency, in the context of West Africa. In this regard, Pastor and Veronesi [32] as well as Gomes et al. [33] provide the earliest outcomes followed by the studies of Arouri et al. [34], Balli et al. [35], Gholipour [36], Naifar and Hammoudeh [37], Xunpeng et al. [38], among others. They observed that uncertainties influence financial market returns and volatility through the effects on investment, spending decisions of individuals, and labor supply. Without considering the impact of uncertainties, investors may not have accurate information about financial markets which may affect their investment decisions and plunge their confidence.

With this in mind, we employ three uncertainty indices namely the Chicago Board Options Exchange Volatility Index (VIX), Crude Oil Volatility Index (OVX), and the Global Economic Policy Uncertainty (GEPU). The CBOE volatility index is a benchmark for investors all over the world as it influences movements in price in markets for commodities and stocks [39] and has consequences for portfolio risk evaluations [40]. Also, the shocks from economic policy uncertainty and the crude oil volatility indices can impact financial market fluctuations across various investment horizons, either directly or indirectly [[41], [42], [43], [44]].

Also, it is equally important to investigate the nexus considering the adaptive [45] and heterogeneous [46] behaviors of markets regarding the irrationality of the participants [47], since financial time series experience rapid changes across time and frequency. For this reason, we employ the partial and bi-wavelet techniques. These approaches effectively reveal the adaptive and heterogeneous behavior of markets and their participants at investment horizons [2,44,48,49]. Hence, this study explores the relationship between stock returns and exchange rates in both time and frequency domains through bi-wavelet analysis. Additionally, we investigate the dynamic effects of global shocks or uncertainties on the relationship between stock returns and exchange rates across time and frequency using partial wavelets [50].

Although the existing literature provides valuable insights into the relationship between exchange rates and the stock market in the African context, there are still substantial gaps that need to be addressed. The incumbent study contributes to the body of literature in three respects. First, we look at the dynamic connectivity between stock return and exchange rate in West Africa using the bi-wavelet. Secondly, this study employs partial wavelet analysis to assess the impact of three uncertainty indices on the stock return-exchange rate relationship in West Africa. The methodological choice is superior since it takes into consideration both the time horizons of economic decisions as well as the strength of links between the stock market and the exchange rate and the impact of uncertainty indices on time and frequency variations [28,[51], [52], [53]]. Third, the BRVM stock market is included in the current research.

The findings reveal that, in West Africa, the connectedness that exists between stock return and currency rate are significant and inverse. The partial wavelet coherence results indicate that economic policy uncertainty, CBOE volatility index, and crude oil volatility index are key drivers of the comovement in the selected markets. The results reveal a significant stock market impact on the currency market, emphasizing the need for robust stock market policies. Authorities should prioritize strategies to enhance stock market stability and depth, which can positively affect the currency market. Also, the impact of uncertainties should be considered in the formulation of policies regarding exchange rates and stock return integration at various investment horizons.

The subsequent sections of this paper are organized as follows. Relevant literature is reviewed in Section 2. Section 3 explains the methods and materials employed in this study. The findings and conclusions of the study are correspondingly shown in Sections 4, 5.

2 Literature review

The West African region considered in this study exhibit some characteristics. For instance, the plan for economic integration in the West Africa region includes the capital market integration drive, which promotes integration in functional areas such as the monetary system [54]. The high integration level within West African economies facilitates trade and investment [55,56], and ensure their long-term sustainability [57]. Additionally, most West African nations have put policies and reforms into place to expand their financial markets, increase access to capital, and strengthen regulatory and supervisory frameworks.

2.1 Stock return and exchange rate

The literature is replete with studies on exchange rates and stock returns which have demonstrated inconclusive outcomes [22,[58], [59], [60], [61], [62]]. On the other hand, some studies found bidirectional causality [20,[63], [64], [65], [66], [67]], while others found no causal association between the two variables [68,69] and others. The inconsistency in the results could be related to possible differences in trade volume, equity, risk assessment, and economic relations among the countries [69]. The empirical evidence concerning the connection between stock prices and exchange rates is equally inconclusive, with some researchers discovering positive correlations between exchange rates and stock returns [26,61,62,70,71], while others found evidence of negative correlations [[72], [73], [74], [75], [76], [77], [78]].

Understanding how the two markets interact can help investors invest more effectively while assuming the least amount of risk. Prior studies produced distinct outcomes that varied depending on the macroeconomic factors considered, the research methodology used, and the nations studied. This lack of consensus on the direction and nature of the relationship supports further research into the dynamic connection between currency rates and stock markets in West Africa and arrive at novel insights that will be useful in currency and portfolio risk management.

2.2 Stock return, exchange rate, and uncertainties

Several studies in the literature relating currency, uncertainty, and stock markets are less focused on how uncertainty indicators such as economic policy uncertainty, CBOE volatility index, and crude oil volatility index affect the link between currency rate and different classes of assets, in this case, stock. Other empirical works have also looked at different connections between the stock market, exchange rates, economic policy uncertainty, CBOE volatility index, and crude oil volatility index [[79], [80], [81], [82]], etc.

Albulescu et al. [83] examined the potential causal impact of US policy uncertainty on the relationship between currency and crude oil markets and found significant relationships at low frequency. Conversely, Wen et al. [84] investigated the correlation of economic policy uncertainty, CBOE volatility index, and crude oil volatility index with some macroeconomic variables in a nonlinear cointegrating ARDL model in China. Their empirical findings indicated that, except for the crude oil volatility index, there is proof that the variables are connected. In the long run, the most important in fueling uncertainty in China’s macroeconomy seemed to be the CBOE volatility index. However, economic policy uncertainty and crude oil volatility index also triggered reactions in the inflation rate, outputs, and supply of cash and influenced uncertainty in the economy. Although investors and policymakers would benefit from this study, it is limited to China and the use of US-based economic policy uncertainty.

By applying the wavelet analysis technique, Das [85] addressed frequency and time connections between stock, crude oil, and exchange rates. Das [85] also discovered a short-term effect of lower-scale volatility. Furthermore, wavelet coherency at a large scale had a slower influence on the relationships over time. This study did not consider other factors such as economic policy uncertainty, CBOE volatility index, or crude oil volatility index that can influence how these markets operate. Asafo-Adjei et al. [86] indicated that investments in African stocks, in the short term, are less susceptible to global economic policy uncertainty.

Also, Fasanya et al. [1] concentrated on how the connectedness between oil and the most popular currency pairs was affected by the uncertainty in US economic policy. First, they found a substantial correlation between crude oil prices and currency exchange rates, with oil being the net shock receiver. Economic policy uncertainty also has an impact on how the markets for oil and exchange rates interact. Third, the causality test demonstrated that economic policy uncertainty near the median and lower quantiles is what causes the spillover for each asset. The study by Aímer [41] looked at the impact of economic policy uncertainty and CBOE volatility index on exchange rates for four nations that experienced the largest number of COVID-19-related fatalities. The results indicate that the co-integration experiments during the pre-pandemic period demonstrated a favorable long-term impact of the CBOE volatility index.

A plethora of studies have investigated the nexus between stock returns and factors such as exchange rate and uncertainties but mostly in developed markets. The closest study to ours is that of Albulescu et al. [83], Shaikh [87], and Fasanya et al. [1]. The earlier works have left some substantial gaps, nevertheless, that need to be filled. First off, insufficient attention is given to the dynamic interconnectedness between the stock returns-currency rates nexus in most studies, especially in West Africa, which tend to focus on causal influence. Second, the impact of various uncertainty indices on the stock market and foreign exchange rate markets are yet to be investigated. Third, the BRVM market is mostly ignored in most studies conducted in the region [88].

3 Methodology

3.1 Bi-wavelet analysis

3.1.1 Continuous wavelet transfer (CWT)

The wavelet transform algorithm breaks down time series into fundamental functions generated from a mother wavelet in general. The mother wavelet is defined by time (ί) and scale (s), which is described as shown in Equation (1) [89,90]:(1) ψί,s(t)=s−1ψ(t−ί)(s−1),ψ(⦁)∈L2(R)

where s−1 is the element of normalization, which guarantees that the wavelet’s variance unit ‖ψί,s(t)‖2=1;ί || is the time parameter, that specifies the specific location of the wavelet; and s is the scale dilation parameter, describing the wavelet stretch. Therefore, the Morlet wavelet is shown in Equation (2) as:(2) φM(t)=π−1/4eiωote−t2/2

where ωo, wavelet’s central frequency, is positioned at six [89,90].

A time series x (t) concerning a chosen mother wavelet can be decomposed [91] as shown by Equation (3) below:(3) wx(ί,s)=∫−∞∞x(t)s−1ψ(t−ίs)dt

By projecting the specific wavelet ψ (⦁) onto the chosen x (t), it is possible to quickly obtain ws(ί,s). Correspondingly, CWT’s main advantage is its capacity to break down and reassemble a function x(t) ∈ L2 (R) as represented in Equation (4) [89,90]:(4) x(t)=1Cφ∫0∞[∫0∞Wx(ί,s)ψί,s(t)dί]dss2,s>0

3.1.2 Wavelet coherence

To eliminate the wavelet power spectrum bias, we apply the cross-wavelet transform technique to examine the interconnectedness between the variables [92]. It explains the covariance in the time-frequency domain and defined as shown in Equation (5) below:(5) Nxy=Nx(ί,s)Ny*(ί,s)

where Nx(ί,s) and Ny*(ί,s) indicate the cross wavelet of series x(t) and y(t), in the order given [91,93]. * denotes a complex conjugate in Equation (5).

The squared absolute value obtained by normalizing a wavelet cross-spectrum to a single wavelet power spectrum is referred to as Wavelet Transform Coherence [91,93,94]. In view of that, the coefficient squared of wavelet between stock returns (x(t)) and exchange rate returns (y(t)) is indicated in Equation (6):(6) R2(x,y)=|ρ(s−1Nxy(i,s))|2ρ(s−1|Nx(i,s)|2)ρ(s−1|Ny(i,s)|2)

where ρ represents a smoothing element, which balances significance and resolution, and 0≤Rxy2(ί,s)≤1. A score approaching zero implies a weak connection, while a value nearing one signifies a strong association. The normalization aspect of wavelet coherence helps mitigate biases in the wavelet cross-spectrum and power [42,43,48,49].

3.1.3 Phase difference

We assume that ϕxy represents the phase difference in the financial markets. Therefore, in line with prior studies (see Refs. [91,95,96]), the phase difference between x(t) and y(t) is expressed as shown in Equation (7):(7) ∅xy(ί,s)=tan−1(J{S(s−1Nxy(ί,s))}R{S(s−1Nxy(ί,s))}),

where J and R represents the imaginary and the real operators respectively. The dimensional phase pattern on the wavelet coherence map delineates the impact of the wavelet coherence gap. Distinct phase patterns are distinguished by directional arrows. For example, when x(t) and y(t) are in-phase, the arrow points rightward (leftward). Likewise, an upward (downward) pointing arrow indicates that either x(t) or y(t) is leading.

3.2 Partial wavelet coherence (PWC)

The partial wavelet approach displays the conditional impact of a third variable on the co-movements of two other variables over frequency and time, rather than focusing on causal linkages. The PWC is used to analyze the correlations between stock returns and currency rates to uncertainty indicators such as the economic policy uncertainty, CBOE volatility index, or crude oil volatility index [90]. This is important to determine the extent to which these uncertainty metrics can influence or distort the relationship between stock returns and exchange rates. The PWC can be defined as represented by Equation (8) below:(8) Rp2(x,y,z)=|R(x,y)−R(x,y)⦁R(x,y)*|2[1−R(x,z)]2[1−R(y,z)]2

where Rp2(x,y,z) is between 0 and 1. In this research, x and y denote the stock return and exchange rate while z signifies the uncertainty measures. PWC is calculated using Monte Carlo techniques.

3.3 Empirical data

We analyzed monthly data encompassing stock prices, exchange rates, and uncertainty measures, spanning from February 2013 to June 2023, which provided a total of 125 observations. This sample duration offers a sufficient timeframe to explore the relationships among these variables across various time and frequency dimensions using wavelet methodologies [2]. The selected timeframe was chosen to encompass the onset of the coronavirus pandemic. Data about stock returns, exchange rates, and uncertainty indices were sourced from Refinitiv and www.investing.com.

The stock returns selected for this study are from the Ghana Stock Exchange (GSE), the Nigeria Stock Exchange (NGX), and the Bourse Régionale des Valeurs Mobilières (BRVM). These stock markets were selected based on consistent data availability and large market capitalization. This study used the West African currency exchange rates. These included the Ghana Cedi (GHS), Nigerian Naira (NGN), and the CFA franc (XOF) for the West African Communauté Financière Africaine. The uncertainty metrics include global economic policy uncertainty, CBOE volatility index, and the crude oil volatility index. All the variables are denominated in US dollars. This is because it is the invoicing currency for most international transactions. Moreover, the financial market of the US is well-established, which contributes, on a global scale, to the demand for its currency [27]. It has been demonstrated that using common currency returns in related studies is the most effective way to reduce noise [97]. The study relied on monthly returns computed as rt=ln(PtPt−1), where rt is the return, while PtandPt−1 are the current and previous indexes respectively.

4 Results and discussions

4.1 Descriptive statistics

Fig. 1 illustrates graphical representations of price and return series for stock returns, exchange rates, and uncertainty indicators. The analysis suggests that the observed fluctuations indicate instability or the heightened speculative nature of the financial market.Fig. 1 Prices (in red) and returns (in blue) series for Stocks (GSE, BRVM, NGX), Exchange rates (NGN, XOF, GHS), and Uncertainties (VIX, OVX, GEPU). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 1

Fig. 2 Correlation matrix.

Fig. 2

Table 1 shows the descriptive statistics for the observations full sample, before, during and after covid period. These are stock return (GSE, NGX, and BRVM), exchange rates (Ghana cedi, Nigerian naira, and CFA franc) and uncertainty indicators (economic policy uncertainty, CBOE volatility index, or crude oil volatility index). The results relate to stock return and exchange rates of West Africa specifically Ghana, Nigeria, and the market for the eight francophone countries (Benin, Côte d’Ivoire, Mali, Guinea Bissau, Senegal, Burkina Faso, Togo, and Niger). The results presented in the tables include mean, skewness, kurtosis, standard deviation, and normality test.Table 1 Descriptive statistics.

Table 1Variable	Mean	Std. Dev.	Skew.	Kurt.	Norm test.	ADF	KPSS	
GSE	0.0063	0.0508	0.8675	1.5248	0.9424***	−3.871***	0.14626	
BRVM	0.0011	0.0392	0.0193	−0.5026	0.9910	−3.8677***	0.30513	
NGX	0.0050	0.0632	0.1010	0.9109	0.9770**	−4.9669***	0.16985	
GHS	0.0143	0.0615	−0.8222	14.9017	0.7268***	−4.3869***	0.096935	
XOF	0.0021	0.0241	−0.1015	0.7287	0.9923	−4.3159***	0.086351	
NGN	0.0126	0.0583	6.4018	45.2748	0.2963***	−3.3607*	0.15587	
GEPU	0.0023	0.1872	0.4264	1.3202	0.9718***	−6.5836***	0.061486	
OVX	0.0031	0.2283	1.3149	5.6688	0.9132***	−7.1742***	0.04516	
VIX	−0.0004	0.2515	0.4288	0.6702	0.9831	−6.5192***	0.051008	
Levels of significance [***, **, *] suggest significance at 1 %, 5 %, and 10 % respectively.

According to Table 1, the average values, while not exhibiting significant differences, are positive. For the uncertainty indices, a negative return was recorded for the CBOE volatility index only. CBOE volatility index appears to be more volatile suggesting a stronger indicator of uncertainty in the markets. All the variables exhibit skewness to the right showing an asymmetric distribution except for Ghana cedi and CFA franc.

Ghana cedi, Nigerian naira, and crude oil volatility index analyzed are leptokurtic relative to the normal distribution. The normality test, conducted using the Shapiro-Wilk test, rejects the null hypothesis for all variables except BRVM and XOF, confirming that the distribution is indeed non-normal. Both the Augmented Dickey–Fuller (ADF) test and Kwiatkowski–Phillips–Schmidt–Shin (KPSS) in Table 1 reveals that the series is stationary at the first difference at the various levels of significance.

The study further shows the correlation matrix in Fig. 2. The correlation coefficients are presented for the BRVM, GSE, NGX, global economic policy uncertainty, CBOE volatility index, crude oil volatility index, Ghana cedi, Nigerian naira, and CFA franc. This is presented to illustrate the direction, as well as the magnitude of the relationship among the variables under consideration in this study. Through this, we can decipher the averaged level of portfolio diversification as well as volatility spillover with the uncertainty indices [42]. From Fig. 2, the relationship between the variables is mostly negative. It is also clear that all relationships except for XOF and NGN with the BRVM are negative and considered ideal to minimize portfolio risk.

4.2 Results

The bi-wavelet technique was employed to assess the correlation between stock return and exchange rate. We rely on the bi-wavelet package provided by Gouhier et al. [98] which contains the necessary codes and statistical interpretations for conducting the analyses. Negative correlations are denoted by left-pointing arrows, whereas positive correlations are shown by right-pointing arrows. The first variable leads with left and right arrows pointing downwards and upwards respectively, while the right-pointing arrows downwards and left-pointing arrows upwards show that the second variable is leading [2,43,49].

The colors show the intensity of the interdependence between the matched variables. Those with substantial interactions are shown in red, whereas parts with weaker series correlation are highlighted in blue [48]. The scales for the data frequency of the wavelet factors are interpreted as follows: Scale 0–8 represents the short-term (0–4 months), scale 8–16 is the medium-term (4–8 months) and scale 16–32 is the long-term (8–12 months) as similar notations indicated by Asafo-Adjei et al. [2] and Boateng et al. [48]. Fig. 3a, Fig. 3b, Fig. 3c(a–c) displays the co-movements between stock return and exchange rates using the bi-wavelet technique. As a result, the stock return of three stock markets and exchange rates of three currencies are employed–GSE, NGX, BRVM, and Ghana cedi, Nigerian naira, and CFA franc respectively. The analysis presents three combinations of stock-exchange interconnectedness.Fig. 3a Bi-wavelet analysis for GSE and GHS.

Fig. 3a

Fig. 3b Bi-wavelet analysis for NGX and NGN.

Fig. 3b

Fig. 3c Bi-wavelet analysis for BRVM and XOF.

Fig. 3c

A close examination of Fig. 3a, Fig. 3b, Fig. 3c reveal that the co-movements between stock returns and currency rates are of moderate intensity and are mostly negative as shown by the left-pointing arrows. In the short-term, GSE and NGX lead at most times as indicated by the right-pointing upward arrows (2015 and 2016, 2019–2020 respectively). We found no degree of interdependency between the stocks and currency rates in the long run. The strength of the interactions between stock returns and exchange rates decreases as follows: NGX-NGN (Fig. 3b), BRVM-XOF (Fig. 3c), and GSE-GHS (Fig. 3a). Specifically, in the short-run, at approximately 0–8 months, there is not much to say of the coherency between stock return exchange rates as the co-movements are weak specifically from 2016 to 2018, 2020–2023 for GSE-GHS, 2017 to 2018, 2020–2022 for NGX-NGN and 2016 to 2020 for BRVM-XOF. This makes diversification within these markets practically feasible since the correlation between them is low.

There was evidence of a strong correlation among the variables in the short run too. The periods are 2013–2014, 2016, 2019–2020 for NGX-NGN (Fig. 3b), 2013–2014, 2019–2023 for BRVM-XOF (Fig. 3c), 2015 and 2018–2019 for GSE-GHS (Fig. 3a). At 8–16 months, the results revealed that from 2015 to 2016 and 2018–2020, the correlation between NGX and NGN was strong. At the same time scale, it was noted that the relationship between BRVM and XOF and GSE and GHS were strong in 2019 and 2014 respectively. The high co-movements could be ascribed to the significant impact of COVID-19 [23]. This indicates that stock return and currency rates display high uncertainties across different periods, and this could imply that uncertainties lead to a decline in domestic stock returns. This means that investors will be steered away from domestic markets, causing capital outflows to increase, and putting upward pressure on exchange rates. In the long run, the association was weak for the relationship between stock and exchange rate throughout the period.

Fig. 4a, Fig. 4b, Fig. 4c, Fig. 5a, Fig. 5b, Fig. 5c and Fig. 6a, Fig. 6b, Fig. 6c show the partial wavelet coherence results. A significant drop in the strength of the coherence area between stock return and exchange rates in all instances of the partial wavelet coherence and this indicates that economic policy uncertainty (Fig. 4a, Fig. 4b, Fig. 4c, CBOE volatility index (Fig. 5a, Fig. 5b, Fig. 5c, and crude oil volatility index (Fig. 6a, Fig. 6b, Fig. 6c) are key drivers of the co-movement. This reveals the dominance of uncertainties in West Africa’s macroeconomic fundamentals. More precisely, the analysis revealed that the CBOE volatility index has the most pronounced impact on the interactions between stock returns and currency rates in Nigeria. This is primarily because the CBOE volatility index is directly linked to financial markets, and consequently, disturbances originating in one market can lead to a ripple effect on the other, thereby amplifying immediate volatility indices within the financial markets.Fig. 4a Partial wavelet coherence of GSE-GHS with GEPU.

Fig. 4a

Fig. 4b Partial wavelet coherence of NGX-NGN with GEPU.

Fig. 4b

Fig. 4c Partial wavelet coherence of BRVM-XOF with GEPU.

Fig. 4c

Fig. 5a Partial wavelet coherence of GSE-GHS with VIX.

Fig. 5a

Fig. 5b Partial wavelet coherence of NGX-NGN with VIX.

Fig. 5b

Fig. 5c Partial wavelet coherence of BRVM-XOF with VIX.

Fig. 5c

Fig. 6a Partial wavelet coherence of GSE-GHS with OVX.

Fig. 6a

Fig. 6b Partial wavelet coherence of NGX-NGN with OVX.

Fig. 6b

Fig. 6c Partial wavelet coherence of BRVM-XOF with OVX.

Fig. 6c

Consequently, the CBOE volatility index has the potential to transmit more shocks to the financial markets. Furthermore, in the long term, the crude oil volatility index exerts the most substantial influence on the correlations between BRVM and XOF (Fig. 6c). Also, the economic policy uncertainty has the most impact on the relationship between the NGX and NGN (Fig. 4b). Conversely, in the short to medium run, it can be observed that economic policy uncertainty has the greatest impact on the connection between stock returns and currency exchange for all countries. This means that external shocks influence short to medium-term investments in most West African economies.

4.3 Discussions

4.3.1 Relationship between stock returns and exchange rates

It was found that the connections that exist between stock returns and exchange rates in West African economies were generally moderate. These relationships were mostly found to be negative, suggesting the adverse nexus between stock and exchange rates for all economies. This agrees with the outcome of He et al. [60] that the stock-exchange rate nexus is negative. Additionally, this study includes the results of Rai and Garg [77]. Also, these findings match those of Lou and Luo [16], Cavusoglu et al. [15], Kallianiotis [17], Pole and Cavusoglu [76], Aftab et al. [72] to mention a few. The inverse relationship is particularly pronounced in nations heavily dependent on imports of which these West African countries are no exception. Consequently, when the local currency depreciates against the USD, it results in higher prices for imported goods and this affects the stock market. This implies that businesses may have to employ hedging procedures to protect their overseas trade in this situation. Domestic enterprises that do not trade worldwide may need to improve their efficiency to compete in local markets with their foreign counterparts. These connections are important to policymakers to maintain financial stability.

In addition, a unidirectional relationship was found between the two financial markets at various investment horizons highlighting the fact that each economic indicator drives the other at certainty time and frequency. Consequently, the study divulged that the nexus between stock and exchange rate markets is frequency dependent. This finding is in line with studies like Boako et al. [59], Abiola and Olusegun [22], He et al. [60], etc. Though in different settings and using different techniques, these works found a unidirectional relationship. The current study departs from the outcomes obtained by Do et al. [74], Afshan et al. [99], Emenike [64], Mattack [65], Živkov et al. [20], and others who found a bidirectional relationship.

The outcome of the causal role of stock is in line with the stock-oriented model. This means that rising stock prices may attract foreign investors to the West African market (GSE, NGN, BRVM), leading to a significant influx of capital. As foreign investors exchange their foreign currency for West African currencies (economic policy uncertainty, CBOE volatility index, crude oil volatility index), it creates substantial demand for West African currencies. The resulting increase in investor wealth drives greater demand for West African assets, causing interest rates to rise and ultimately leading to local currency appreciation. The strong and low correlations between stock returns and exchange rates in the time-frequency domain are illustrative of the adaptive market hypothesis (AMH) [45] and the heterogeneous market hypothesis (HMH) [46]. This is because the interdependencies between the two markets strongly influence each other across various time-frequency intervals.

The study also revealed the significant impact of crises, especially, the aftermath of the Eurozone crisis and the Covid-19 pandemic revealing contagion effects in West Africa. The discussion could also be advanced further. A study by Zoungrana et al. [100] reported a strong significant impact of the Covid-19 on the stock market operating in West African Economic and Monetary Union (WAEMU). Another research conducted by Shaik et al. [101] also documented that the connectedness between financial assets tend to exhibit higher intensity of volatility spillover during the COVID-19 pandemic. These researches underscore how pandemics strengthen financial asset linkages and provides insights to inform pandemic risk management strategies.

Policymakers and investors interested in protecting a diversified portfolio from the negative impacts of extreme market fluctuations in African stock and currency markets could consider guidance from these findings. The results show a significant effect of the stock market on the currency market, emphasizing the need for robust policies to boost stock market performance. Authorities should prioritize strategies to enhance stock market stability and depth, which can positively affect the currency market [102].

4.3.2 Effect of uncertainties on the relationship between stock returns and exchange rates

It was revealed that the CBOE volatility index has the most significant impact on the long term on the connection between stock returns and exchange rates in Nigeria. As a result, CBOE volatility index can effectively capture long-term fluctuations in the markets. Kang et al. [103] reported a similar result in a different setting. Speculative investors in the Nigerian economy are therefore advised to hedge against changes in the financial markets using the US CBOE volatility index. Similarly, in the long run, the linkages between BRVM and XOF are most significantly influenced by the crude oil volatility index. This implies that crude oil volatility has a pass-through effect on the stock return and exchange rate nexus for BRVM economies. Speculative investors of BRVM must monitor the dynamics of crude oil implied volatility to minimize their investment risk. They can do so by hedging against adverse fluctuations in the crude oil volatility index market.

Also, the economic policy uncertainty has the most impact on the relationship between stock and currency markets in Nigeria. Policymakers in Nigeria should deploy appropriate country-level policies to minimize external policy uncertainty shocks in the long term for speculative investors to enjoy potential diversification benefits [86]. On the other hand, in the short to medium period, economic policy uncertainty had the most significant impact on the integration of stock returns and exchange rates for the countries studied. This implies that external policy uncertainty or shocks influence short to medium-term investments in the West African economies. Policymakers and governments in these West African economies should arrange policies that mitigate the impact of economic policy uncertainty on economic activities within the short and medium frequencies.

The significant impact of economic policy uncertainty supports the outcome of Albulescu et al. [83]. Albulescu et al. revealed that the financial market is connected dynamically, and policy-induced uncertainties have a conditional impact. This suggests that GEPU is the pertinent driver of financial market interactions.

5 Conclusions

The study investigates the degree of interconnectedness between stock returns and exchange rate returns, and the influence of some selected uncertainty indices on such a relationship in West Africa. In this manner, the bi and partial wavelet approaches were employed in executing the purpose of this study. The study found negative relationships between stock returns and currency rates throughout the time-frequency domain. A unidirectional relationship was found between the stock returns and exchange rates. The outcome of the impact of the exchange rate on stock returns was in line with the stock-oriented model. The strong and weak interactions between stock returns and exchange rates in the time-frequency domain illustrate AMH and HMH. This is attributed to the fact that the patterns of interdependence between these markets are notably influenced by each other across time-frequency intervals.

In all instances of partial wavelet coherence, the strength of the coherence area diminishes between stock return and exchange rates. In the long term, it was found that the CBOE volatility index has the greatest effect on the relationship between stock returns and currency rates in Nigeria and the crude oil volatility index had the most significant impact in BRVM. Also, GEPU had the most impact on the connection between the stock returns and exchange rates in Nigeria. Conversely, it was clear that GEPU had the highest impact on the relationship between stock returns and currency rates for all countries in the short to medium term. Overall, the stock-currency relationship in Nigeria was most affected by the uncertainty indices. Nonetheless, investors who seek to hedge against fluctuations in stock returns can do so using exchange rates from these economies. In this regard, especially, during serious economic events, a portfolio with stock returns and exchange rate markets from these economies would induce safe haven potential.

The study also revealed the significant impact of crises, especially, the aftermath of the Eurozone crisis and the COVID-19 pandemic revealing contagion effects in West Africa. Policymakers and investors interested in protecting a diversified portfolio from the negative impacts of extreme market fluctuations in African stock and currency markets could consider guidance from these findings. The results also show a significant stock market impact on the currency market, emphasizing the need for robust stock market policies. Authorities should prioritize strategies to enhance stock market stability and depth, which can positively affect the currency market.

It will be fascinating to follow up on this research by looking into the role of uncertainty indexes on other instruments in the financial market such as bonds, cryptocurrency, commodities, etc., and for different regions or countries as well. Other uncertainties such as news-based uncertainty, currency volatilities, cryptocurrency volatility index, the volatility index for the energy sector, gold miners-based volatility index, developed market volatility index, volatility index based on the US treasury, etc. could be explored too. Future studies should focus on how to employ DCC-GARCH and ADCC-GARCH models to capture the hedging ratios for the markets.

Data availability statement

Data is available upon request.

Funding

No funding was provided.

CRediT authorship contribution statement

Bernice Nkrumah-Boadu: Writing – original draft, Software, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. George Tweneboah: Writing – review & editing, Validation, Supervision, Methodology, Formal analysis, Conceptualization. Siaw Frimpong: Writing – review & editing, Supervision.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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References

1 Fasanya I.O. Adekoya O.B. Adetokunbo A.M. On the connection between oil and global foreign exchange markets: the role of economic policy uncertainty Resour. Pol. 72 2021 102110 10.1016/j.resourpol.2021.102110
2 Asafo-Adjei E. Boateng E. Isshaq Z. Idun A.A.A. Owusu Junior P. Adam A.M. Financial sector and economic growth amid external uncertainty shocks: insights into emerging economies PLoS One 16 11 2021 e0259303 10.1371/journal.pone.0259303
3 Efremenko I.N. Panasenkova T.V. Artemenko D.A. Larionov V.A. The role of cryptocurrencies in the development of the global currency system European Research Studies Journal 21 1 2018 117 124 https://www.um.edu.mt/library/oar//handle/123456789/33910
4 Jain A. Biswal P.C. Dynamic linkages among oil price, gold price, exchange rate, and stock market in India Resour. Pol. 49 2016 179 185 10.1016/j.resourpol.2016.06.001
5 Roubaud D. Arouri M. Oil prices, exchange rates and stock markets under uncertainty and regime-switching Finance Res. Lett. 27 2018 28 33 10.1016/j.frl.2018.02.032
6 Dornbusch R. Fischer S. Exchange rates and the current account Am. Econ. Rev. 70 5 1980 960 971 https://www.jstor.org/stable/1805775
7 Aloui C. Price and volatility spillovers between exchange rates and stock indexes for the pre-and post-euro period Quant. Finance 7 6 2007 669 685 10.1080/14697680701302653
8 Stavarek D. Stock prices and exchange rates in the EU and the United States: evidence on their mutual interactions Czech Journal of Economics and Finance (Finance a uver) 55 3–4 2005 141 161 10.2139/ssrn.671681
9 Branson W.H. A Model of Exchange-Rate Determination with Policy Reaction: Evidence from Monthly Data 1983 NBER working paper series 10.3386/w1135 Working Paper No. 1135
10 Frankel J.A. Monetary and portfolio-balance models of exchange rate determination International Economic Policies and Their Theoretical Foundations 1992 793 832 10.1016/B978-0-12-444281-8.50038-6
11 Bahmani-Oskooee M. Saha S. On the relation between exchange rates and stock prices: a non-linear ARDL approach and asymmetry analysis J. Econ. Finance 42 2018 112 137 10.1007/s12197-017-9388-8
12 Nusair S.A. Al-Khasawneh J.A. On the relationship between Asian exchange rates and stock prices: a nonlinear analysis Econ. Change Restruct. 55 1 2022 361 400 10.1007/s10644-021-09318-8
13 Salisu A.A. Oloko T.F. Modelling spillovers between stock market and FX market: evidence for Nigeria J. Afr. Bus. 16 1–2 2015 84 108 10.1080/15228916.2015.1061285
14 Zhang M. Essays on Mispricing in the Chinese Stock Market vol. 302 2016 Publications issued by the Swedish School of Economics 1 146 https://helda.helsinki.fi/server/api/core/bitstreams/4523b91f-683f-411f-8db5-9b7ce2cd058e/content
15 Cavusoglu N. Goldberg M.D. Stillwagon J. New evidence on the portfolio balance approach to currency returns Institute for New Economic Thinking Working Paper Series 89 2019 10.2139/ssrn.3346757
16 Lou T. Luo W. Revisiting quantile Granger causality between the stock price indices and exchange rates for G7 countries Asian Econ. Financ. Rev. 8 1 2018 9 21 10.18488/journal.aefr.2018.81.8.21
17 Kallianiotis I.N. Exchange rate determination: the portfolio-balance approach J. Appl. Finance Bank 11 1 2021 19 40 10.47260/jafb/1112
18 Salisu A.A. Ndako U.B. Modelling stock price–exchange rate nexus in OECD Econ. Modell. 74 2018 105 123 10.1016/j.econmod.2018.05.010
19 Tabak B.M. The dynamic relationship between stock prices and exchange rates: evidence for Brazil Int. J. Theor. Appl. Finance 9 8 2006 1377 1396 10.1142/S0219024906003974
20 Živkov D. Manić S. Đurašković J. Short and long-term volatility transmission from oil to agricultural commodities–The robust quantile regression approach Borsa Istanbul Review 20 2020 S11 S25 10.1016/j.bir.2020.10.008
21 Owusu Junior P. Tweneboah G. Are there asymmetric linkages between African stocks and exchange rates? Res. Int. Bus. Finance 54 2020 101245 10.1016/j.ribaf.2020.101245
22 Abiola B. Olusegun A.J. Appraising the exchange rate volatility, stock market performance, and aggregate output nexus in Nigeria Bus. Econ. J. 8 1 2017 1000290 10.4172/2151-6219.1000290
23 Adjasi C.K. Biekpe N.B. Osei K.A. Stock prices and exchange rate dynamics in selected African countries: a bivariate analysis African J. Econ. Manag. Stud. 2 2 2011 143 164 10.1108/20400701111165623
24 Adjasi C. Harvey S.K. Agyapong D.A. Effect of exchange rate volatility on the Ghana stock exchange African Journal of Accounting, Economics, Finance and Banking Research 3 3 2008 28 47 https://ssrn.com/abstract=1534178
25 Amewu G. Junior P.O. Amenyitor E.A. Co-movement between equity index and exchange rate: fresh evidence from Covid-19 era Scientific African 16 2022 e01146 10.1016/j.sciaf.2022.e01146
26 Bala Sani A.R. Hassan A. Exchange rate and stock market interactions: evidence from Nigeria Arabian J. Bus. Manag. Rev. (AJBMR) 8 1 2018 1000334 https://www.researchgate.net/profile/Sani-Bala-2/publication/363121226_Arabian_Journal_of_Business_and_Management_Review/links/630eb2431ddd4470212139f9/Arabian-Journal-of-Business-and-Management-Review.pdf
27 Owusu Junior P. Kwaku Boafo B. Kwesi Awuye B. Bonsu K. Obeng-Tawiah H. Co-movement of stock exchange indices and exchange rates in Ghana: a wavelet coherence analysis Cogent Business & Management 5 1 2018 1481559 10.1080/23311975.2018.1481559
28 Tweneboah G. Owusu Junior P. Oseifuah E.K. Integration of major African stock markets: evidence from multi-scale wavelets correlation Acad. Account. Financ. Stud. J. 23 6 2019 1 15 https://www.researchgate.net/profile/Peterson-Owusu-Junior/publication/340647142_INTEGRATION_OF_MAJOR_AFRICAN_STOCK_MARKETS_EVIDENCE_FROM_MULTI-SCALE_WAVELETS_CORRELATION/links/5e96e3d74585150839de91fe/INTEGRATION-OF-MAJOR-AFRICAN-STOCK-MARKETS-EVIDENCE-FROM-MULTI-SCALE-WAVELETS-CORRELATION.pdf
29 Ahmed A.D. Huo R. Linkages among energy price, exchange rates, and stock markets: evidence from emerging African economies Appl. Econ. 52 18 2020 1921 1935 10.1080/00036846.2020.1726861
30 Mhlongo T.H. Olaomi J.O. Dynamic linkage amongst oil, gold, exchange rates and stock markets in Africa: evidence from volatility of major african economies Research Square 2021 10.21203/rs.3.rs-594726/v1
31 Ndong B. Réactions du Marché d'Actions aux Chocs sur les Taux d'Intérêt de Court Terme, le Cas d’un Marché Emergent: la Bourse Régionale des Valeurs Mobilières (BRVM) Afr. Dev. Rev. 23 3 2011 313 334 10.1111/j.1467-8268.2011.00289.x
32 Pastor L. Veronesi P. Uncertainty about government policy and stock prices J. Finance 67 4 2012 1219 1264 10.1111/j.1540-6261.2012.01746.x
33 Gomes F.J. Kotlikoff L.J. Viceira L.M. The excess burden of government indecision Tax Pol. Econ. 26 1 2012 125 164 10.1086/665505
34 Arouri M. Estay C. Rault C. Roubaud D. Economic policy uncertainty and stock markets: long-run evidence from the US Finance Res. Lett. 18 2016 136 141 10.1016/j.frl.2016.04.011
35 Balli F. de Bruin A. Chowdhury M.I.H. Naeem M.A. Connectedness of cryptocurrencies and prevailing uncertainties Appl. Econ. Lett. 27 16 2020 1316 1322 10.1080/13504851.2019.1678724
36 Gholipour H.F. The effects of economic policy and political uncertainties on economic activities Res. Int. Bus. Finance 48 2019 210 218 10.1016/j.ribaf.2019.01.004
37 Naifar N. Hammoudeh S. Do global financial distress and uncertainties impact GCC and global sukuk return dynamics? Pac. Basin Finance J. 39 2016 57 69 10.1016/j.pacfin.2016.05.016
38 Xunpeng S. Variam H.M.P. Tao J. Global impact of uncertainties in China's gas market Energy Pol. 104 2017 382 394 10.1016/j.enpol.2017.02.015
39 Chung K.H. Chuwonganant C. Market volatility and stock returns: the role of liquidity providers J. Financ. Mark. 37 2018 17 34 10.1016/j.finmar.2017.07.002
40 Al-Yahyaee K.H. Mensi W. Sensoy A. Kang S.H. Energy, precious metals, and GCC stock markets: is there any risk spillover? Pac. Basin Finance J. 56 2019 45 70 10.1016/j.pacfin.2019.05.006
41 Aímer N. Economic policy uncertainty and exchange rates before and during the COVID-19 pandemic Journal of Ekonomi 3 2 2021 119 127 https://dergipark.org.tr/en/pub/ekonomi/issue/59529/900517
42 Agyei S.K. Adam A.M. Bossman A. Asiamah O. Owusu Junior P. Asafo-Adjei R. Asafo-Adjei E. Does volatility in cryptocurrencies drive the interconnectedness between the cryptocurrencies market? Insights from wavelets Cogent Economics & Finance 10 1 2022 2061682 10.1080/23322039.2022.2061682
43 Amoako G.K. Asafo-Adjei E. Mintah Oware K. Adam A.M. Do volatilities matter in the interconnectedness between world energy commodities and stock markets of BRICS? Discrete Dynam Nat. Soc. 2022 2022 10.1155/2022/1030567
44 Owusu Junior P. Adam A.M. Asafo-Adjei E. Boateng E. Hamidu Z. Awotwe E. Time-frequency domain analysis of investor fear and expectations in stock markets of BRIC economies Heliyon 7 10 2021 e08211 10.1016/j.heliyon.2021.e08211
45 Lo A.W. The adaptive markets hypothesis J. Portfolio Manag. 30 5 2004 15 29 https://ssrn.com/abstract=602222
46 Müller U.A. Dacorogna M.M. Davé R.D. Pictet O.V. Olsen R.B. Ward J.R. Fractals and Intrinsic Time: A Challenge to Econometricians 1993 Unpublished manuscript, Olsen & Associates Zürich 130 http://finance.martinsewell.com/stylized-facts/scaling/Muller-et al.1993.pdf
47 Ijasan K. Junior P.O. Tweneboah G. Oyedokun T. Adam A.M. Analysing the relationship between global REITs and exchange rates: fresh evidence from frequency-based quantile regressions Advances in Decision Sciences 25 3 2021 58 91 10.47654/V25Y2021I3P58-91
48 Boateng E. Asafo-Adjei E. Addison A. Quaicoe S. Yusuf M.A. Adam A.M. Interconnectedness among commodities, the real sector of Ghana and external shocks Resour. Pol. 75 2022 102511 10.1016/j.resourpol.2021.102511
49 Nkrumah-Boadu B. Owusu Junior P. Adam A. Asafo-Adjei E. Safe haven, hedge and diversification for African stocks: cryptocurrencies versus gold in time-frequency perspective Cogent Economics & Finance 10 1 2022 2114171 10.1080/23322039.2022.2114171
50 Asafo-Adjei E. Adam A.M. Darkwa P. Can crude oil price returns drive stock returns of oil producing countries in Africa? Evidence from bivariate and multiple wavelet Macroeconomics and Finance in Emerging Market Economies 2021 1 19 10.1080/17520843.2021.1953864
51 Frimpong S. Gyamfi E.N. Ishaq Z. Kwaku Agyei S. Agyapong D. Adam A.M. Can global economic policy uncertainty drive the interdependence of agricultural commodity prices? Evidence from partial wavelet coherence analysis Complexity 2021 2021 10.1155/2021/8848424
52 Owusu Junior P. Frimpong S. Adam A.M. Agyei S.K. Gyamfi E.N. Agyapong D. Tweneboah G. COVID-19 as information transmitter to global equity markets: evidence from CEEMDAN-based transfer entropy approach Math. Probl Eng. 2021 10.1155/2021/8258778 2021
53 Tweneboah G. Dynamic interdependence of industrial metal price returns: evidence from wavelet multiple correlations Phys. Stat. Mech. Appl. 527 2019 121153 10.1016/j.physa.2019.121153
54 Obadiaru E.D. Omankhanlen A. Obasaju B.O. Inegbedion H. Correlation of stock market returns in the West African region from 2008 to 2016 Invest. Manag. Financ. Innovat. 16 3 2019 https://eprints.lmu.edu.ng/id/eprint/2516
55 Tyopev I. Trade Openness and Economic Growth in Selected West African Countries (Doctoral Dissertation) 2019 https://core.ac.uk/download/pdf/322634462.pdf
56 Umaru H. Aguda N.A. Davies N.O. The effects of exchange rate volatility on economic growth of West African English-speaking countries Int. J. Acad. Res. Account. Finance. Manag. Sci. 8 4 2018 131 143 10.6007/IJARAFMS/v8-i4/5470
57 Ehigiamusoe K.U. Lean H.H. The role of deficit and debt in financing growth in West Africa J. Pol. Model. 42 1 2020 216 234 10.1016/j.jpolmod.2019.08.001
58 Agrawal G. Srivastav A.K. Srivastava A. A study of exchange rates movement and stock market volatility Int. J. Bus. Manag. 5 12 2010 62
59 Boako G. Omane‐Adjepong M. Frimpong J.M. Stock returns and exchange rate nexus in Ghana: a bayesian quantile regression approach S. Afr. J. Econ. 84 1 2016 149 179 10.1111/saje.12096
60 He X. Gokmenoglu K.K. Kirikkaleli D. Rizvi S.K.A. Co‐movement of foreign exchange rate returns and stock market returns in an emerging market: evidence from the wavelet coherence approach Int. J. Finance Econ. 28 2 2021 1175 2225 10.1002/ijfe.2522
61 Kumar S. Asymmetric impact of oil prices on exchange rate and stock prices Q. Rev. Econ. Finance 72 2019 41 51 10.1016/j.qref.2018.12.009
62 Mikhaylov A.Y. Volatility spillover effect between stock and exchange rate in oil exporting countries Int. J. Energy Econ. Pol. 8 3 2018 321 326 https://www.proquest.com/docview/2056362574?pq-origsite=gscholar&fromopenview=true&sourcetype=Scholarly
63 Chkili W. The dynamic relationship between exchange rates and stock returns in emerging countries: volatility spillover and portfolio management Int. J. Manag. Sci. Eng. Manag. 7 4 2012 253 262 10.1080/17509653.2012.10671230
64 Emenike K.O. Exchange rate volatility in West African countries: is there a shred of Spillover? Int. J. Emerg. Mark. 13 6 2018 1457 1474 10.1108/IJoEM-08-2017-0312
65 Mattack T. The causal relationship between stock prices and exchange rate: a toda-yamamoto approach IUP Journal of Applied Finance 26 2 2020 49 59 https://eds.p.ebscohost.com/eds/pdfviewer/pdfviewer?vid=1&sid=b77d1e40-b3a4-46ea-91f5-97b7368fe038%40redis
66 Singh G. Relationship between exchange rate and stock price in India: an empirical study The IUP Journal of Financial Risk Management 12 2 2015 18 29 https://ssrn.com/abstract=2681958
67 Tiwari A.K. Bhanja N. Dar A.B. Islam F. Time–frequency relationship between share prices and exchange rates in India: evidence from continuous wavelets Empir. Econ. 48 2 2015 699 714 10.1007/s00181-014-0800-3
68 Ozair A. Causality between Stock Prices and Exchange Rates: a Case of the United States 2006 Florida Atlantic University https://www.proquest.com/openview/73eb55d85754ad0c8bdf04d65df10a81/1?pq-origsite=gscholar&cbl=18750&diss=y
69 Suriani S. Kumar M.D. Jamil F. Muneer S. Impact of exchange rate on stock market Int. J. Econ. Financ. Issues 5 1 2015 385 388
70 Ogbole F.O. Aladejare S.A. The Nigerian stock exchange: its impact on the performance of the Nigerian economy (1981-2012): an econometric approach Reiko International Journal of Social and Economic Research 8 3C 2015 1 18 https://www.researchgate.net/publication/308874058
71 Megaravalli A.V. Sampagnaro G. Macroeconomic indicators and their impact on stock markets in ASIAN 3: a pooled mean group approach Cogent Economics & Finance 6 1 2018 1432450 10.1080/23322039.2018.1432450
72 Aftab M. Ali A. Hegerty S.W. Foreign exchange market pressure and stock market dynamics in emerging Asia Int. Econ. Econ. Pol. 18 4 2021 699 719 10.1007/s10368-021-00501-w
73 Delgado N.A.B. Delgado E.B. Saucedo E. The relationship between oil prices, the stock market and the exchange rate: evidence from Mexico N. Am. J. Econ. Finance 45 2018 266 275 10.1016/j.najef.2018.03.006
74 Do H.X. Brooks R. Treepongkaruna S. Realized spill-over effects between stock and foreign exchange market: evidence from regional analysis Global Finance J. 28 2015 24 37 10.1016/j.gfj.2015.11.003
75 Elhendawy E.O. Stock prices and exchange rate dynamics: empirical evidence from Egypt International Journal of Economics, Commerce and Management 5 1 2017 29 43 https://ijecm.co.uk/wp-content/uploads/2017/01/513.pdf
76 Pole H. Cavusoglu B. The effect of macroeconomic variables on stock return volatility in the Nigerian stock exchange market Asian Journal of Economics, Finance and Management 3 1 2021 32 43 https://globalpresshub.com/index.php/AJEFM/article/view/1007
77 Rai K. Garg B. Dynamic correlations and volatility spillovers between stock price and exchange rate in BRIICS economies: evidence from the COVID-19 outbreak period Appl. Econ. Lett. 29 8 2021 1 8 10.1080/13504851.2021.1884835
78 Nkoro E. Uko A.K. Exchange rate and inflation volatility and stock prices volatility: evidence from Nigeria, 1986-2012 J. Appl. Finance Bank 6 6 2016 57 70 https://www.scienpress.com/Upload/JAFB/ Vol%206_6_4.pdf
79 Abid A. Economic policy uncertainty and exchange rates in emerging markets: short and long runs evidence Finance Res. Lett. 37 2020 101378 10.1016/j.frl.2019.101378
80 Bartsch Z. Economic policy uncertainty and dollar-pound exchange rate return volatility J. Int. Money Finance 98 2019 102067 10.1016/j.jimonfin.2019.102067
81 Hoque M.E. Zaidi M.A.S. The impacts of global economic policy uncertainty on stock market returns in regime switching environment: evidence from sectoral perspectives Int. J. Finance Econ. 24 2 2019 991 1016 10.1002/ijfe.1702
82 Juhro S.M. Phan D.H.B. Can economic policy uncertainty predict exchange rate and its volatility? Evidence from asean countries Buletin Ekonomi Moneter Dan Perbankan 21 2 2018 251 268 10.21098/bemp.v21i2.974
83 Albulescu C.T. Demirer R. Raheem I.D. Tiwari A.K. Does the US economic policy uncertainty connect financial markets? Evidence from oil and commodity currencies Energy Econ. 83 2019 375 388 10.1016/j.eneco.2019.07.024
84 Wen F. Xiao Y. Wu H. The effects of foreign uncertainty shocks on China's macro-economy: empirical evidence from a nonlinear ARDL model Phys. Stat. Mech. Appl. 532 2019 121879 10.1016/j.physa.2019.121879
85 Das S. The time–frequency relationship between oil price, stock returns and exchange rate Journal of Business Cycle Research 17 2 2021 129 149 10.1007/s41549-021-00057-3
86 Asafo-Adjei E. Agyapong D. Agyei S.K. Frimpong S. Djimatey R. Adam A.M. Economic policy uncertainty and stock returns of Africa: a wavelet coherence analysis Discrete Dynam Nat. Soc. 2020 2020 10.1155/2020/8846507
87 Shaikh I. Does policy uncertainty affect equity, commodity, interest rates, and currency markets? Evidence from CBOE's volatility index J. Bus. Econ. Manag. 21 5 2020 1350 1374 10.3846/jbem.2020.13164
88 Emenike K.O. Interdependence among West African stock markets: a dimension of regional financial integration Afr. Dev. Rev. 33 2 2021 288 299 10.1111/1467-8268.12575
89 Rua A. Nunes L.C. International comovement of stock market returns: a wavelet analysis J. Empir. Finance 16 4 2009 632 639 10.1016/j.jempfin.2009.02.002
90 Vacha L. Barunik J. Co-movement of energy commodities revisited: evidence from wavelet coherence analysis Energy Econ. 34 1 2012 241 247 10.1016/j.eneco.2011.10.007
91 Li R. Li S. Yuan D. Yu K. Does economic policy uncertainty in the US influence stock markets in China and India? Time-frequency evidence Appl. Econ. 52 39 2020 4300 4316 10.1080/00036846.2020.1734182
92 Ng E.K. Chan J.C. Geophysical applications of partial wavelet coherence and multiple wavelet coherence J. Atmos. Ocean. Technol. 29 12 2012 1845 1853 10.1175/JTECH-D-12-00056.1
93 Torrence C. Compo G.P. A practical guide to wavelet analysis Bull. Am. Meteorol. Soc. 79 1 1998 61 78 10.1175/1520-0477(1998)079%3C0061:APGTWA%3E2.0.CO;2
94 Grinsted A. Moore J.C. Jevrejeva S. Application of the cross wavelet transform and wavelet coherence to geophysical time series Nonlinear Process Geophys. 11 5/6 2004 561 566 10.5194/npg-11-561-2004
95 Bloomfield D.S. McAteer R.J. Lites B.W. Judge P.G. Mathioudakis M. Keenan F.P. Wavelet phase coherence analysis: application to a quiet-sun magnetic element Astrophys. J. 617 1 2004 623 632 10.1086/425300
96 Wu K. Zhu J. Xu M. Yang L. Can crude oil drive the co-movement in the international stock market? Evidence from partial wavelet coherence analysis N. Am. J. Econ. Finance 53 2020 101194 10.1016/j.najef.2020.101194
97 Pukthuanthong K. Roll R. Global market integration: an alternative measure and its application J. Financ. Econ. 94 2 2009 214 232 10.1016/j.jfineco.2008.12.004
98 Gouhier T.C. Grinsted A. Simko V. Gouhier M.T.C. Rcpp L. Package ‘biwavelet’ Spectrum 24 2013 2093 2102 https://rest.neptune-prod.its.unimelb.edu.au/server/api/core/bitstreams/746cabe0-edef-5a5d-98db-744394ec38bf/content
99 Afshan S. Sharif A. Loganathan N. Jammazi R. Time–frequency causality between stock prices and exchange rates: further evidences from cointegration and wavelet analysis Phys. Stat. Mech. Appl. 495 2018 225 244 10.1016/j.physa.2017.12.033
100 Zoungrana T.D. Toe D.L.T. Toé M. Covid‐19 outbreak and stocks return on the West African Economic and Monetary Union's stock market: an empirical analysis of the relationship through the event study approach Int. J. Finance Econ. 28 2 2021 1404 1422 10.1002/ijfe.2484
101 Shaik M. Varghese G. Madhavan V. The dynamic volatility connectedness of global financial assets during the Ebola & MERS epidemic and the COVID-19 pandemic Appl. Econ. 56 8 2024 880 900 10.1080/00036846.2023.2174499
102 Korley M. Giouvris E. The regime-switching behaviour of exchange rates and frontier stock market prices in Sub-Saharan Africa J. Risk Financ. Manag. 14 3 2021 122 10.3390/jrfm14030122
103 Kang S. Hernandez J.A. Sadorsky P. McIver R. Frequency spillovers, connectedness, and the hedging effectiveness of oil and gold for US sector ETFs Energy Econ. 99 2021 105278 10.1016/j.eneco.2021.105278
