Cogent Economics & Finance ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/oaef20 Relationship between Exchange Rate Volatility and Interest Rates Evidence from Ghana Sarpong Mohammed, Abubakari Mohammed & Edward Nketiah-Amponsah | To cite this article: Sarpong Mohammed, Abubakari Mohammed & Edward Nketiah-Amponsah | (2021) Relationship between Exchange Rate Volatility and Interest Rates Evidence from Ghana, Cogent Economics & Finance, 9:1, 1893258, DOI: 10.1080/23322039.2021.1893258 To link to this article: https://doi.org/10.1080/23322039.2021.1893258 © 2021 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Published online: 24 Mar 2021. Submit your article to this journal Article views: 2267 View related articles View Crossmark data Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 Mohammed et al., Cogent Economics & Finance (2021), 9: 1893258 https://doi.org/10.1080/23322039.2021.1893258 GENERAL & APPLIED ECONOMICS | RESEARCH ARTICLE Relationship between Exchange Rate Volatility and Interest Rates Evidence from Ghana Sarpong Mohammed1, Abubakari Mohammed2* and Edward Nketiah-Amponsah3 Received: 14 July 2020 Abstract: This paper examines the effect of interest rates on exchange rate Accepted: 16 February 2021 volatilities in Ghana. It utilizes the Quarterly Time Series dataset spanning 2000 *Corresponding author: Abubakari Quarter 1 to 2017 Quarter 2 and the Autoregressive Distributed Lag model as well Mohammed, Council for Scientific as the Vector Error Correction Model to investigate the long-run and short-run and Industrial Research, Accra, Ghana relationships between the variables. The results showed that in the long-run model, E-mail: abubakarim62@gmail.com exchange rate volatility was seen to be influenced by money supply, inflation, Reviewing editor: Central Bank’s policy rate, and the Ghana Stock Exchange composite index. Aviral Tiwari, Finance and Economics, Rajagiri Business School, However, in the short-run model, exchange rate volatility was found to be signifi- INDIA cantly influenced by its past values and the Central Bank’s policy rate. Additional information is available at the end of the article Subjects: Economics; finance; business, management and accounting Keywords: Exchange rate volatility; interest rate; autoregressive-distributed lag 1. Introduction There has been growing interest in assessing the relationship between interest rates and exchange rates in both advanced and developing economies in recent years. This is attributable to the important role these variables play in determining developments in the nominal and real sides of the economy, including the behavior of domestic inflation, real output, exports and imports (Sánchez, ABOUT THE AUTHOR PUBLIC INTEREST STATEMENT Sarpong Mohammed is a finance and internal Ghana, being a net importer of goods and ser- control expert with over 9 years’ experience in vices, is saddled with exchange rate volatility Accounting and Internal audit. He is a Chartered issues as more foreign currency is required to Accountant and a member in good standing with meet the needs of importers. Since indepen- the Institute of Chartered Accountants, Ghana dence in 1957, the country has gone through (ICAG). He holds a Bachelor’s Degree in com- various exchange rate regimes in an attempt to merce (B. Com) from the University of Cape Coast ensure economic stability but to no avail as the and Master of Philosophy in Finance and local currency continues to perform abysmally Investment from the Kwame Nkrumah University with reference to major currencies. of Science and Technology, Kumasi. Volatility increases exchange rate risk as the He has worked with Ghana Broadcasting profit in trade or the rate of return on interna- Corporation as an Internal Auditor and later as tional investments are eroded due to changes in an Accountant. Currently, he is an Internal the exchange rate. Available data show Sarpong Mohammed Auditor with the West African Examinations a seeming relationship between Interest Rates Council (WAEC). He is the National Auditor for the and Exchange Rate Volatility. Youth Wing of the Ahmadiyya Muslim Mission, Therefore, my team set out to explore how Ghana. such a relationship, if any, could help stabilize His research interest is in the financial markets exchange rates. The findings, among others, and macroeconomics. showed that interest rates could influence exchange rate volatility. In the end, appropriate recommendations have been tabled for consideration. © 2021 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Page 1 of 19 Mohammed et al., Cogent Economics & Finance (2021), 9: 1893258 https://doi.org/10.1080/23322039.2021.1893258 2005). The subject of exchange rate volatility and interest rate is even more critical due to globaliza- tion; countries intermingle with each other through trade and investment (Suranovic, 2012). Volatile exchange rates are associated with random movements in relative prices in an econ- omy. For this reason, stable exchange rate is a very significant factor to stimulate total investment, price stability and stable economic growth (AL Samara, 2009). The Ghanaian cedi has witnessed long periods of depreciation against major foreign currencies, especially the US dollar (US$) since the adoption of the floating exchange rate regime (Kwakye, 2015). While this regime offers the country some level of monetary independence, it is associated with exchange rate volatilities. For instance, at the beginning of January 2014, US$1 was exchanged for GH¢2.3975, but by the end of September 2014, the cedi/dollar exchange rate stood at GH¢3.2 to US$1, denoting about 33.48% decrease in value. Over the same period, the policy rate was revised from 18% to 19% by the Bank of Ghana (BoG, 2014). Most researchers have considered the role of interest rates in stabilizing exchange rates by asking whether or not interest rates have the potential to influence domestic currency. A study by Kwakye (2015) examined the relationship between exchange rates and key macroeconomic vari- ables in Ghana. The study employed Autoregressive Distributed Lag (ARDL) technique for co- integration and found that there is cointegration relationship between the variables, indicating the existence of a long-run equilibrium relationship between them. Further, the study established a significant effect of exchange rate lag (past exchange rate) on current exchange rate. Another study by Nchor and Darkwah (2015) investigated the impact of exchange rate movement and the nominal interest rate on inflation in Ghana. The study examined the presence of Fisher Effect and International Fisher Effect scenarios. They made use of an autoregressive distributed lag model and an unrestricted error correction model (UECM). Ordinary Least-Squares regression methods were also employed to determine the presence of the Fisher Effect and the International Fisher Effect. The results showed that, in the short run, there exists a positive relationship between exchange rate and inflation while the relationship between interest rate and inflation in the short run is negative. The study further established the presence of both the partial Fisher Effect and the full International Fisher Effect. It can be seen from the literature reviewed that much had not been done to examine the influence of interest rates on exchange rate volatility in Ghana. In this light, this study seeks to investigate the effect of a change in the Central Bank policy rate on exchange rate volatility in Ghana. The motivation to focus on volatility stems from the fact that, according to Chen (2006), empirical evidence in developing countries suggests that exchange rate volatility may discourage foreign trade and investment and hence reduce national income. The rest of the paper is organized as follows: Section 2 reviews the existing theoretical and empirical literatures on interest rates and exchange rate volatility, while section 3 presents the methodology used in the study. The fourth section presents and discusses the empirical findings of the study. The last section presents the summary and concluding remarks. 2. Literature review The major exchange rate theories used by the researchers include Purchasing Power Parity, Balance of Payments, Interest Rate Parity, and International Fisher Effect Theories. These theories deal with parity conditions, which is an economic explanation of the price at which two currencies are exchanged based on factors including inflation and interest rates (Otuori, 2013; Madhura, 2008; Isard, 1995 & Fisher, 1930). These economic theories posit that, in situations where parity conditions do not hold, they give rise to arbitrage opportunities for market participants. Exchange rate theories are funda- mentally based on the law of one price states that, in the absence of restrictions such as shipping costs and tariffs, the price of a product when converted into a common currency such as the US dollar, using the spot exchange rate, is the same in every country (Levi, 2005). Page 2 of 19 Mohammed et al., Cogent Economics & Finance (2021), 9: 1893258 https://doi.org/10.1080/23322039.2021.1893258 Volatility represents the degree to which a variable changes over time (Suranovic, 2012). Therefore, exchange rate volatility can be referred to as the rise or fall in the value of foreign currency in relation to the local currency. A study by the Research Department of the Bank of Ghana on financial and monetary policies in Ghana, cited by Quartey and Afful-Mensah (2014), attributed the dynamics of exchange rates to policy directions and interventions in the exchange rate market. In December 2006, the Foreign Exchange Act was enacted to replace the Exchange Control Act as part of measures to deepen the country's financial system. Under the old exchange rate law, Ghana operated a controlled exchange rate policy where restrictions were placed on foreign transactions, including external loans contracted by residents and non- residents. The introduction of the Foreign Exchange Act was to ensure a shift from these restrictions to a more liberalized foreign exchange regime (Bank of Ghana Working Paper, Bank of Ghana, 2007). Notwithstanding these interventions, however, maintenance of a stable value for the cedi vis-à-vis major international currencies, such as the US dollar, British pound, and the euro, has continued to pose a challenge to policymakers (Bank of Ghana Working Paper, Bank of Ghana, 2007). Interestingly, few researchers have attempted to examine the relationship between exchange rate volatility and interest rates. According to Saraç and Karagöz (2015), the issue of exchange rate volatility is one of the leading impediments to the progress of developing economies as it adversely affects macro- economic management. As a result, policymakers continue to use monetary policy instruments, key among which is interest rate to contain the rate of exchange rate fluctuations. Although some researchers disagree on the empirical findings of the role of tight monetary policies, such as high interest rates in stabilizing exchange rates, there is considerable level of agreement among most of them (Chen, 2006). Perhaps, the support for this policy is premised on the traditional wisdom that, during periods of exchange rate fluctuations, rising interest rates make speculations against the domestic currency unattractive because, when domestic interest rates are raised, it has the potential to attract foreign investment. Additionally, it affects the decision of domestic investors to invest abroad which will lead to inflow of foreign currency which can stabilize the exchange rate (Verbeek, 2004). Furman et al. (1998) employed simple regression analysis to establish the simultaneous relation- ship between interest rate and exchange rate in nine emerging markets which had temporarily high interest rates. They concluded that the quantum and length of the high interest rates were associated with exchange rate depreciation. Their interpretation of the results therefore questions the rationale for raising interest rates to defend exchange rates. This conclusion was however contested by Baig and Goldfajn (1999) who investigated the relationship between monetary policy and exchange rates in five Asian countries affected by the financial crisis using simple correlations. The study specifically focused on the role of monetary policy in stabilizing exchange rates after a large collapse. The results of the study showed no evidence to suggest that high interest rates impacted unstable exchange rates, which is in direct contradiction to the conclusions drawn by Furman et al. (1998). Another study by Chen (2006) analysed weekly data totaling 296 data points to explain the relation- ship between exchange rate volatility and interest rates in six developing countries, namely: Indonesia, South Korea, Philippines, Thailand, Mexico, and Turkey using the Markov regime switching approach. This study employs a model anchored in the microstructural theory of exchange rates in Jeane and Rose (2002) which combines the theory of exchange rate determination with the noise trading approach to asset price volatility. The researchers found that when the nominal interest rate increases, it increases the probability of switching to a regime which allows for volatile exchange rates. This supports the traditional argument that one significant merit of the floating exchange rate regime stabilizes interest rates. This is because it saves monetary authorities the problem of having to intervene to allow the exchange rate to remain fixed (Reinhart & Reinhart, 2001). Equally, when the exchange rate is fixed, it induces inter-sectoral or intertemporal shifts in volatilities to other variables (Frenkel & Mussa, 1980). Page 3 of 19 Mohammed et al., Cogent Economics & Finance (2021), 9: 1893258 https://doi.org/10.1080/23322039.2021.1893258 Ali et al. (2015) used VECM and co-integration models to investigate the impact of inflation, interest rates, and money supply on volatility of exchange rates in Pakistan. They noted that monetary policies are crucial in stabilizing prices and reducing unemployment. They analyzed monthly data from July 2000 to June 2009 and concluded that exchange rate volatility is influ- enced by high money supply and an increase in interest rates which raises the price levels. Interestingly, another study by Asari et al. (2011) used VECM approach to explain the relationship between interest rates and inflation towards exchange rate volatility in Malaysia produced differ- ent results. The results showed a positive relationship between interest rate and inflation but an inverse relationship between interest rate and exchange rate volatility. The study therefore sug- gested that interest rates can be efficient in containing exchange rate volatility. The Ghana Stock exchange (GSE) is an avenue for foreign investment. When macroeconomic factors such as inflation and interest rates are favourable, it attracts foreign investment. This has the potential to boost the performance of the GSE and hence improve the foreign exchange situation of the country. In view of this, some researchers have suggested that the stock market can affect the currency market through the rate of foreign cash invested in domestic companies. For instance, researchers, namely Bala Sani and Hassan (2018), and Farooq et al. (2005) among others have suggested the existence of causal relationships and positive and statistically signifi- cant relationships between the stock market general index and the exchange rate. Yet, other scholars sought to ascertain whether inflation targeting as a monetary policy frame- work has the potential to influence exchange rate volatility (Fosu, 2015). For instance, Chow and Kim (2004) employed bivariate VAR-GARCH model to examine the empirical relationship between exchange rates and interest rates in Indonesia, Korea, Philippines, and Thailand and probed how the dynamics had changed following the Asian financial crisis. The results suggest that interest rates play no significant role in stabilizing exchange rates. Interest rates did not stabilize due to increased exchange rate flexibility. On the other hand, Minella et al. (2003) also examined the difficulties brought about by the inflation targeting regime in Brazil after it has been in operation for three and a half years. They employed a VAR to model inflation targeting and also ran an OLS regression of the inflation target, the interest rate, and the 12-month inflation rate. The results suggested that the inflation target- ing framework had helped to stabilize the macroeconomy. Osei-Assibey (2018) investigated “Inflation Targeting under Weak Macroeconomic Fundamentals” and sought to find out if there was a need for monetary policy redirection in Ghana. He suggested a rethinking of Ghana’s inflation targeting (IT) regime, in order to accelerate the nation’s socio- economic development. The study found that, although IT had been successful in keeping inflation levels low, if the rule was implemented very strictly, an inflation target could severely limit the central bank’s flexibility in responding to changing economic conditions. Therefore, the study concluded that further interventions seem necessary to augment its effectiveness. Numerous studies conducted in this field confirm the significance of the subject matter. It can be observed that extensive study has been done in other countries but Ghana. Studies in this area in the Ghanaian context have focused on long-run relationships. Studies that looked at volatilities did so in relation to other economic variables apart from interest rates. It is worth noting that exchange rate fluctuations do not necessarily pose a risk. However, the rate of fluctuations (volatility) poses a risk to both traders and investors (Suranovic, 2012). This occurs when the expected profit in trade or rate of return on international investment is eroded due to the rate and extent of fluctuations in the exchange rate as it makes it more expensive to import. Therefore, this study is different from previous studies conducted in Ghana in that the current study shifts from the relationship based on fluctuations which has been the area of focus of Page 4 of 19 Mohammed et al., Cogent Economics & Finance (2021), 9: 1893258 https://doi.org/10.1080/23322039.2021.1893258 previous studies conducted in Ghana and focuses on the volatilities (rate and extent of fluctua- tions). Further, the study looks at the relationship in the context of inflation targeting which uses interest rates as the key monetary policy operating instrument in macroeconomic stabilization (Chow and Kim, 2004). Studies have shown that, in the face of financial crisis, inflation targeting could stabilize exchange rates (see Minella et al. (2003), Mishkin (2004), Osei-Assibey (2018), and Roger (2010)). 3. Methodology 3.1. Volatility estimate In this study, real exchange rate volatilities are estimated using the Generalized Autoregressive Conditional heteroskedasticity GARCH (1,1) model which is the Generalized ARCH introduced by Engle and Granger (1982) and Bollerslev (1986). The GARCH (1,1) is given as: (i) The jointly estimated GARCH (1,1) model is given as: at ¼ σtε ~t; εtiidð0;1Þ; a0>0 (1) m s σ2 ¼ α þ ∑ α ε20 þ ∑ β σ2 ; εtN~t i t i j t j ð0:1Þ; α0>0; αi � 0; βj � 0 (2) i¼1 j¼1 The variance equation σ2t is composed of three terms: α0 = the mean (long-term average) α2t i = News about volatility from the previous period (the ARCH term) σ2t j. = GARCH term 3.2. Regression model Following the precedence of earlier scholars (see Ali et al., 2015), we present a theoretical model for the determinants of the exchange rate as shown in Equation (3). The Ghana Stock Exchange index has been included owing to its relationship with the exchange rate (see Bala Sani and Hassan (2018) & Farooq et al. (2005)) Ert ¼ fðIRt; It;MSt;GSEtÞ (3) Where Ert ¼ exchange rate volatility; IRt ¼ Central Bank policy rate; I ¼ inflation rate; MSt ¼ Money supply; Ert ¼ β0 þ β1IRt þ β2It þ β3MSt þ β4GSEt þ εt Page 5 of 19 Mohammed et al., Cogent Economics & Finance (2021), 9: 1893258 https://doi.org/10.1080/23322039.2021.1893258 Equation (3) indicates that the exchange rate volatility is a function of the Central Bank policy rate, the inflation rate, the money supply, and the Ghana Stock Exchange composite index. For the purpose of econometric estimation, the theoretical model for the determinants of exchange rate volatility in Ghana is expressed empirically as: Ert ¼ β0 þ β1IRt þ β2It þ β3MSt þ β4GSEt þ εt (4) Where the subscript “t” represents a time period, that is, a quarter in this case, since the data set is quarterly data spanning the period of the first quarter of 2000 to the second quarter of 2017. εt = stochastic error term assumed to be white noise, while βi, for i =0, 1, 2, 3, 4, are regression parameters to be estimated. 3.3. Estimation procedure To achieve the study’s objectives, we estimate the long-run relationship between Central Bank policy rate (a proxy for interest rate) and exchange rate volatility. This study will then apply the ARDL approach introduced by Pesaran et al. (2001) to investigate the long-run relationship between Central Bank policy rate and exchange rate volatility. The reason for using the ARDL Bound Testing approach is that it allows for a combination of variables integrated of different orders, i.e., I(0) and I(1). This makes the ARDL model superior to conventional approaches to cointegration such as the Johansen cointegration approach and the Engle-Granger two-step residual base test for cointegration (Johansen, 1991 & Engle, 1987). 4. Estimation results and discussion 4.1. Descriptive statistics Table 1 shows the descriptive statistics for these elected variables in the form of means, standard deviations, as well as their maximum and minimum values. For instance, the table shows that the average policy rate within the period of study was 19.3% while inflation was 16.8% within the same period. It can also be seen that inflation was as high as 41.9% at a point and went as low as 8.4% while policy rate ranged from 12.5% to 27.5% within the same period. 4.2. Preliminary data analysis Preliminary data analysis as presented in Tables 1, 2, and 3 (see appendix) involves summary statistics of the data, normality tests, correlation analysis, and stationarity tests on the variables. It is interesting to note that the pairwise correlations are statistically significant at the conventional level in most cases, especially with regard to correlations between the dependent variable (exchange rate volatility) and independent variables. Gujarati (2004), however, argues that high zero order correlations are a sufficient but not a necessary condition for the presence of multicollinearity. Also, Figures 1 and 2 (see appendix) depict the evolution of exchange rate volatility and the Central Bank’s policy rate in Ghana, respectively. In Figure 1, in particular, we observe that the cedi/US Dollar exchange rate has been extremely volatile with the rate of volatility rising from just 0.06 to as high as Table 1. Descriptive statistics of variables Variable Obs Mean Std. Dev. Min Max FX Volatility 70 0.0479782 0.044781 0.00398 0.19339 CB Policy Rate 70 19.33286 5.166551 12.5 27.5 Inflation 70 16.81043 7.622356 8.4 41.9 Money Supply 70 14,763.19 16,561.54 514.7 59,903.8 GSE Composite 70 3,582.037 2,726.189 763.1 10,890.8 Index Source: Authors’ own Computation Page 6 of 19 Mohammed et al., Cogent Economics & Finance (2021), 9: 1893258 https://doi.org/10.1080/23322039.2021.1893258 Table 2. Pairwise correlation matrix Er GSE I IR MS Er 1 GSE −0.3467 1 I 0.4657 −0.1744 1 IR 0.5077 −0.4669 0.7373 1 MS 0.3154 −0.3123 −0.2687 0.1313 1 Source: Author’s computation Table 3. Results of ADF and PP unit root tests AUGMENTED DICKEY FULLER PHILLIPS–PERRON UNIT ROOT (ADF) UNIT ROOT TEST TEST ADJUSTED TEST STATISTIC ADJUSTED TEST STATISTIC VARIABLE Trend and Constant Trend and Constant Er −5.054791[1] −2.43749 (0.0005) *** −0.3576 Der −6.094780[1] −8.682622 (0.0000) *** (0.0000) *** GSE −1.799165[0] −1.895996 −0.6945 −0.6459 DGSE −7.158695[0] −7.153575 (0.0000) *** (0.0000) *** I −2.635796[0] −3.234673 −0.2663 (0.0863) * DI −7.523462[3] −5.902178 (0.0000) *** (0.0000) *** IR −1.538793[1] −1.233165 −0.8063 −0.8954 DIR −5.448886[0] −5.445427 (0.0001) *** (0.0001) *** MS −1.994393[4] −3.042747 −0.5933 −0.1285 DMS −3.727496[5] −19.81065 (0.0276) * * (0.0001) *** Notes: (1) *, **, and *** denote stationarity at 10%, 5%, and 1% levels of statistical significance, respectively; (2) Values in parentheses are the p-values, while values in square brackets are the lag length of the ADF unit root test regression; (3) the logarithm of money supply (MS) is used for the unit root test; (4) A maximum lag order of 10 was set for the ADF test according to the Schwert formula for determining the maximum lag order. The number of lags selected for the ADF test was automatically selected based on the Schwartz Information Criterion. However, in applying the Phillips–Perron test (Phillips & Perron, 1988), the bandwidth was selected based on the Newey–West Bandwidth. 0.19. It is noteworthy, however, that the rate has been extremely oscillatory during the post- 2007 period, i.e. the years after the redenomination of the Ghana cedi which converted GHc10,000 old Ghana cedis to GHS 1 new Ghana cedi. In contrast, the policy rate trended upward between the first quarter of 2012 and the first quarter of 2016. The study utilised the Augmented Dickey Fuller (ADF) test and Phillips-Perron (PP) test to examine the stationarity properties of the variables. This is done to ensure that the dependent variable is I(1) Page 7 of 19 Mohammed et al., Cogent Economics & Finance (2021), 9: 1893258 https://doi.org/10.1080/23322039.2021.1893258 Figure 1. Plot of cumulative 30 sum of recursive residuals. Note: The straight lines repre- 20 sent critical bounds at 5% sig- nificance level 10 0 -10 -20 -30 05 06 07 08 09 10 11 12 13 14 15 16 17 CUSUM 5% Significance and none of the explanatory variables is I(2) or higher. This requirement is necessary since the use of variables which are integrated of order I(2) or higher invalidates the F-statistics and all critical values established by Pesaran. The unit root test results from ADF test and PP test presented in Table 4 shows that most of the regression variables are stationary in the first difference. On the basis of this, we present the order of integration of the regression variables in Table 4 as follows: From Table 4, it is observed that none of the regressors is integrated of order two or higher and the dependent variable, exchange rate volatility (Er), is integrated of order I(1) as depicted by the PP unit root test. This implies that the ARDL cointegration procedure can be employed in the analysis of the long-run cointegration relationship between the dependent variable (Er) and the vector of regressors. 4.3. Testing for the existence of a long-run relationship In testing for the presence of a long-run relationship between the level variables in a multivariate framework under the ARDL or Bounds Testing cointegration procedure, we estimate an UECM and the error correction version of the ARDL model using Ordinary Least Squares (OLS). The Bounds Testing cointegration approach is simply an F Test for the joint significance of the lagged variables on the right-hand side of the UECM. Table 1A (appendix) shows the results of the estimated UECM. From this, we determine the maximum number of lags using the lag length selection criteria as shown in Table 1B (appendix). Given that most of the lag order selection criteria, such as the Likelihood Ratio (LR), Schwartz information criterion (SC), and Hannan–Quinn information criterion (HQ) choose a maximum lag of two, we set the maximum lag order of the ARDL model to two. The estimated ARDL model is presented in Table 1C (appendix). Table 4. The order of integration of the regression variables VARIABLE ADF UNIT ROOT TEST PP UNIT ROOT TEST Er I(0) I(1) GSE I(1) I(1) I I(1) I(0) IR I(1) I(1) MS I(1) I(1) Source: Author’s computation Page 8 of 19 Mohammed et al., Cogent Economics & Finance (2021), 9: 1893258 https://doi.org/10.1080/23322039.2021.1893258 The null and alternative hypotheses of the ARDL Bounds Test F statistic is given by: H0: No cointegration H1: Cointegration The decision rule is given as: If the ARDL F-statistic value is greater than the upper bound (U) critical value at a chosen level of significance, we reject the null hypothesis of no cointegration. On the other hand, if the ARDL F-statistic value is less than the lower bound (L) critical value at a chosen level of significance, we accept the null hypothesis of no cointegration. The test is, however, inconclusive if the ARDL F-statistic value lies between the upper and lower bounds critical values at the chosen level of significance. The estimated results of the long-run cointegration test using the Wald coefficient diagnostic test are shown in Table 5. Based on the Wald Test result (Table 5), the null hypothesis of no cointegration is rejected at the 1% level of significance, indicating the presence of a long-run level relationship between exchange rate volatility and the regressors in the model. Given that a long-run relationship exists between the exchange rate volatility and the regressors in the model, the next step in the ARDL cointegration procedure is to estimate the long-run and short-run coefficients of the model. 4.4. Estimation and discussion of long-run coefficients The existence of cointegration between the exchange rate volatility and the regressors makes it possible to estimate the long-run results of the regression equation. The results of the long-run model are presented in Table 6. Based on the long run results in Table 6, we find that the exchange rate volatility in Ghana is significantly influenced by the Central Bank’s policy rate (IR), money supply (MS), inflation (I), and the GSE composite index in the long run (see Table 6). Specifically, the results in Table 6 show that a percentage increase in the rate of inflation is associated with about 0.004 units increase in exchange rate volatility in the long run, whereas a percentage increase in the money supply raises the volatility of the exchange rate by about 0.015 units in the long run. Also, the empirical results suggest that an upward revision of the Central Bank’s policy rate is associated with an increase in the volatility of the exchange rate in the long run. In particular, revising the Central Bank’s policy rate by just a basis point increases the volatility in the exchange rate by about 0.8% in the long run. Furthermore, we observe that an increase in the GSE composite index reduces exchange rate volatility in the long run. The finding in this study corroborates that of Furman et al. (1998) who concluded that the quantum and length of the high interest rates were associated with exchange rate depreciation. Table 5. Results of the cointegration test Test Statistic Value df Probability F-statistic 3.882929 (5, 50) 0.0047 Chi-square 19.41465 5 0.0016 Source: Author’s computation Page 9 of 19 Mohammed et al., Cogent Economics & Finance (2021), 9: 1893258 https://doi.org/10.1080/23322039.2021.1893258 Table 6. Long-run regression estimates of the effect of Central Bank’s policy rate on exchange rate volatility in Ghana [dependent variable: exchange rate volatility (Er)] Variable Coefficient Std. Error t-Statistic Prob. IR 0.008238** 0.003407 2.417981 0.0185 MS 0.014952*** 0.003662 4.082992 0.0001 I 0.004033*** 0.000644 6.258682 0.0000 GSE −2.98E-06* 1.50E-06 −1.992008 0.0506 Constant −0.141153 0.041711 −3.384067 0.0012 R-squared 0.51559 Adjusted R-squared 0.485314 Log likelihood 141.4746 F-statistic 17.02985 Prob(F-statistic) 0.0000 Notes: ***, **, and * denote statistical significance at 1%, 5%, and 10% levels of significance, respectively; IR represents a quarterly adjustment in the Central Bank’s policy rate; MS represents the money supply; I represent inflation rate; and GSE represents the Ghana Stock Exchange composite index. Source: Author’s computation However, this finding is in contrast to that of Baig and Goldfajn (1999) who found no evidence to suggest that high interest rate impacted the unstable exchange rate. The interpretation of the results of this study alongside that of Furman et al. (1998) therefore questions the rationale for raising interest rates to defend exchange rates as supported by the traditional view. It is important to note that monetary policy works best where financial markets are efficient and well developed, and market participants are committed to the achievement of overall national economic goals. Therefore, in less developed economies, high interest rates may actually not only discourage investments and slow economic growth but could precipitate finan- cial-sector crisis thereby depreciate the local currency. 4.5. Estimation and discussion of short-run coefficients The existence of a long-run level relationship between the regression variables implies that there is an error correction representation which gives information on the long-run relationship, short-run relation- ship, and the speed of adjustment. Thus, after establishing the existence of a long-run relationship between the variables and estimating the long-run coefficients, the final step in the ARDL bound testing approach involves the determination of the short-run dynamics associated with the long-run estimates of the variables in the model. This is achieved by estimating the ECM as shown in Table 7. We observe from Table 7 that exchange rate volatility is significantly influenced by its past values and the Central Bank’s policy rate in the short run. In particular, whereas the first lag of the Central Bank’s policy rate positively influences exchange rate volatility in the short run, the second lag of the Central Bank’s policy rate does not significantly influence the behavior of the exchange rate in the short run (see Table 7). However, by performing a joint significance test of the two coefficients (i.e., D(IR(−1)) and D(IR(−2))), we find that the null hypothesis of jointly non-significant coefficients is rejected at the 10% level of statistical significance (see Table 1D, appendix). The lag error correction term (ECT(−1)) represents the extent of disequilibrium or departure from the equilibrium in the previous period. The coefficients of ECT tell us the speed of adjustment towards the equilibrium. The coefficient of the lagged error correction term is negative (−0. 365,399) and is significant at the 1% level of statistical significance, indicating convergence towards the long-run equilibrium. According to Afzal et al. (2010), the negative value of the error correction term implies that in each period, approximately 37% of the shocks can be justified as a long-run trend. The Page 10 of 19 Mohammed et al., Cogent Economics & Finance (2021), 9: 1893258 https://doi.org/10.1080/23322039.2021.1893258 Table 7. Short-run estimates of the determinants of exchange rate volatility in Ghana Variable Coefficient Std. Error t-Statistic Prob. C −0.007766 0.004954 −1.567721 0.1228 D(Er(−1)) 0.474323 0.113045 4.195892 0.0001 D(Er(−2)) 0.009292 0.123675 0.07513 0.9404 D(IR(−1)) 0.005913 0.002372 2.492908 0.0158 D(IR(−2)) −0.00098 0.002344 −0.417986 0.6776 D(MS(−1)) 0.036719 0.038896 0.944022 0.3494 D(MS(−2)) 0.053989 0.039238 1.375934 0.1745 D(I(−1)) −0.000555 0.000852 −0.651793 0.5173 D(I(−2)) −0.0007 0.000702 −0.996428 0.3235 D(GSE(−1)) 1.42E-06 2.18E-06 0.64966 0.5187 D(GSE(−2)) 5.02E-07 2.15E-06 0.233799 0.816 ECT(−1) −0.365399 0.11271 −3.241932 0.002 R-squared 0.405427 Adjusted R-squared 0.28431 Log likelihood 177.7255 F-statistic 3.347406 Prob(F-statistic) 0.001437 Notes: ***, **, and * denote statistical significance at 1%, 5%, and 10% levels of significance, respectively. Source: Author’s computation implication of this is that deviations in the exchange rate volatility away from the equilibrium are corrected by 37% within a quarter. Intuitively, it could be explained that if a review of the Central Bank’s monetary policy rate is a response to certain macroeconomic conditions prevailing at a particular time including fluctua- tions in exchange rates, then it is expected that the revision of the policy rate will have both short- run and long-run influence. That is, the Central Bank has as its primary objective to achieve macroeconomic stability and one of the instruments at its disposal is the policy rate. Such a policy rate could potentially be effective both in the short run and long run to stabilize exchange rates. To ensure that the ARDL model is well specified, we conducted two major post-estimation diagnostic tests. The diagnostic test includes tests for serial correlation and the test of the stability of the ARDL model. The serial correlation test is conducted using the Breusch–Godfrey Serial Correlation LM test. A summary of the test results is shown in Table 8. From Table 8, it is seen that the F-statistic value = 0.310378 and Prob. F(2,48) = 0.7346. This indicates that the null hypothesis of no serial correlation cannot be rejected. Hence, there is no serial correlation in the model and so it can be inferred that the model is correctly specified. Table 8. Breusch–Godfrey serial correlation LM test F-statistic 0.310378 Prob. F(2,48) 0.7346 Obs*R-squared 0.842642 Prob. Chi-Square(2) 0.6562 Source: Author’s computation Page 11 of 19 Mohammed et al., Cogent Economics & Finance (2021), 9: 1893258 https://doi.org/10.1080/23322039.2021.1893258 The second diagnostic test is a test for the stability of the ARDL model. The stability of coefficients of regressors in a regression model is important for long-run policy analysis. Indeed, effective policy analysis requires that the model is stable over the long run. To assess the stability of the exchange rate volatility model, the CUSUM (Cumulative Sum) of recursive residuals tests is utilized. The CUSUM of recursive residuals test is depicted graphically in Figure 1 (see appendices). From Figure 3 it is clear that the CUSUM test does not exceed the bounds of the 5% significance level (depicted by the two straight lines). This means that the model is stable as well as being correctly specified. Thus, there exists a significant and stable relationship among the variables in the model. Also, from the correlation matrix in Table 2 in the appendices it can be seen that there is a high correlation between inflation and policy rate (0.73), which raises concerns about potential multi- collinearity. Therefore, the study went further to check for the existence of multicollinearity in the estimation equations by performing a VIF test. The test results are shown in the appendices. Appendix (Table 1E) shows a mean VIF of 2.4 which is far below 10. As a rule of thumb, if the mean VIF is below 10 it is interpreted as the absence of multicollinearity, which proves the robustness of the estimation equations in the study (Long & Freese, 2006). 4.6. Robustness check In order to check for the robustness of the estimated model, the authors used a simple OLS to estimate the same model with the same variables which can be seen in appendix (Table 1F). The results clearly show that the ARDL estimation technique is superior to the OLS because the F-statistic associated with the OLS is insignificant and is associated with a low Adjusted R-squared, meaning the regression equation explains less than 50% of the variability in the dependent variable. 5. Summary and concluding remarks The study employed the ARDL Bounds Testing approach to investigate the relationship between exchange rate volatility and Central Bank’s policy rate in Ghana. The results revealed that, in the long run, exchange rate volatility is influenced by Central Bank’s PR, MS, I, and GSE composite index. Unlike the long-run estimates, results from the short-run estimation show that the exchange rate volatility is significantly determined by its past values and Central Bank’s PR. This suggests that changes in the Central Bank’s PR in Ghana will impact on exchange rate volatility both in the short run and long runs. Finally, the coefficient of the error correction term indicates convergence towards the long-run equilibrium. The results from the study have several implications for policy action. First, the positive effects of Bank of Ghana policy rate on exchange rate volatility in the short run and long run add to the call for interest rate control as has been suggested by several studies on interest rates in Ghana. That is, the need to keep interest on borrowing down is no longer a mere call but a substantive one which is necessary to slow exchange rate volatility. Second, given that inflation and money supply influence exchange rate volatility in the long run, it is important for the Central Bank to adopt steps aimed at weighing down inflationary pressures. This is because an increase in money supply will likely cause higher inflation that eventually aggravates depreciation of the value of the local currency. The conclusions of this research corroborate conclusions drawn in Furman et al. (1998), Ali et al. (2015), and Asari et al. (2011) among others who suggested a positive relation between interest rates and exchange rate volatility. However, this is in sharp contrast with the conclusion reached by Baig and Goldfajn (1999) who found no evidence to suggest that high interest rates impacted unstable exchange rates and therefore question the rationale for raising interest rates to defend exchange rates as supported by the traditional view. The difference in outcome may largely be attributable to differences in jurisdictions. It is important to note that monetary policy works best where financial markets are efficient and well developed, and market participants are committed to the achievement of overall national Page 12 of 19 Mohammed et al., Cogent Economics & Finance (2021), 9: 1893258 https://doi.org/10.1080/23322039.2021.1893258 economic goals. Therefore, in imperfect market economies, high interest rates may actually not only discourage investments and slow economic growth but could precipitate financial-sector crisis thereby depreciate the local currency. Funding Engle, R. E. and Granger, C. W. J. (1982). Cointegration The authors received no direct funding for this research. and error-correction: representestimation, and test- ing. Econometrica, 55(2), 251–276 Author details Engle, R. E., & Granger, C. W. J. (1987). Cointegration and Sarpong Mohammed1 error-correction: Representation, estimation, and E-mail: sarmedgh@gmail.com testing. Econometrica, 55(2), 251. https://doi.org/10. Abubakari Mohammed2 2307/1913236 E-mail: abubakarim62@gmail.com Farooq, T. M., Keung, W. W., & Kazmi, A. A. (2005). Linkage Edward Nketiah-Amponsah3 between stock market prices and exchange rate: E-mail: enamponsah@ug.edu.gh A causality analysis for Pakistan. The Pakistan Society 1 Internal Audit Department, West Africa Examination of Development Economists Islamabad, 43(4), 639– Council, Accra, Ghana. 649. 2 Science and Technology Policy Research Institute Fosu, N. K. (2015). Inflation Targeting: The Ghanaian (STEPRI), Council for Scientific and Industrial Research, Experience [Doctoral dissertation, University of Accra, Ghana. Ghana]. 3 Department of Economics, University of Ghana, Accra, Frenkel, J. A., & Mussa, M. L. (1980). Efficiency of foreign Ghana. exchange markets and measures of turbulence. (No. w0476), National Bureau of Economic Research. Citation information Furman, J., Stiglitz, J. E., Bosworth, B. P., & Radelet, S. Cite this article as: Relationship between Exchange Rate (1998). Economic crises: Evidence and insights from Volatility and Interest Rates Evidence from Ghana, East Asia. Brookings Papers on Economic Activity, Sarpong Mohammed, Abubakari Mohammed & Edward 1998(2), 1–135. https://doi.org/10.2307/2534693 Nketiah-Amponsah, Cogent Economics & Finance (2021), Gujarati, D. N. (2004). Basic econometrics (4th ed.). 9: 1893258. McGraw-Hill Inc. Hameed, A. S., & Rose, A. (2017). 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Page 14 of 19 Mohammed et al., Cogent Economics & Finance (2021), 9: 1893258 https://doi.org/10.1080/23322039.2021.1893258 APPENDICES Figure 2 Evolution of exchange 0.25 rate volatility in Ghana, 2000 (first quarter) to 2017 (second quarter). 0.2 0.193385424 Source: Author’s illustration based on data from the Bank 0.15 0.150374977 of Ghana 0.1 0.102551686 0.05 0.063733453 0.004118393 0.035783806 0 Year_Quarter Figure 3. Evolution of Central 30 27 27.5 Bank policy rate in Ghana, 2000 26 (first quarter) to 2017 (second 25 quarter). 24.5 18.5 Source: Author’s illustration 20 22.5 based on data from the Bank of Ghana 15 10 12.5 12.5 5 0 Year_Quarter Page 15 of 19 rate rate 2000Q1 2000Q3 2000Q1 2001Q1 2001Q3 2000Q4 2002Q1 2001Q3 2002Q3 2003Q1 2002Q2 2003Q3 2003Q1 2004Q1 2004Q3 2003Q4 2005Q1 2004Q3 2005Q3 2006Q1 2005Q2 2006Q3 2006Q1 2007Q1 2006Q4 2007Q3 2008Q1 2007Q3 2008Q3 2008Q2 2009Q1 2009Q3 2009Q1 2010Q1 2009Q4 2010Q3 2011Q1 2010Q3 2011Q3 2011Q2 2012Q1 2012Q1 2012Q3 2013Q1 2012Q4 2013Q3 2013Q3 2014Q1 2014Q3 2014Q2 2015Q1 2015Q1 2015Q3 2016Q1 2015Q4 2016Q3 2016Q3 2017Q1 2017Q2 Mohammed et al., Cogent Economics & Finance (2021), 9: 1893258 https://doi.org/10.1080/23322039.2021.1893258 Table 1A. Unrestricted Error Correction Model (UECM) Included observations: 66 after Adjustments Standard errors in () & t-statistics in [] D(Er) D(Er(−1)) 0.393641 (0.11635) [3.38311] D(Er(−2)) −0.263077 (0.09346) [−2.81497] C −0.004573 (0.00351) [−1.30149] D(IR) 0.002886 (0.00219) [1.31891] D(MS) 0.056630 (0.03919) [1.44495] D(I) 0.000418 −0.0008 [0.52072] D(GSE) −2.78E-06 (2.2E-06) [−1.26491] R-squared 0.269885 Adj. R-squared 0.195637 F-statistic 3.634873 Log likelihood 170.9486 Source: Author’s computation Page 16 of 19 Mohammed et al., Cogent Economics & Finance (2021), 9: 1893258 https://doi.org/10.1080/23322039.2021.1893258 Table 1B. VAR lag order selection criteria Endogenous variables: D(Er) Exogenous variables: C D(IR) D(MS) D(I) D(GSE) Sample: 2000Q1 2017Q2 Included observations: 62 Lag LogL LR FPE AIC SC HQ 0 155.5014 NA 0.000456 −4.854884 −4.683341 −4.787532 1 157.4479 3.516149 0.000443 −4.885415 −4.679563 −4.804592 2 162.4211 8.823489* 0.000390 −5.013584 −4.773423* −4.919291* 3 163.5960 2.046632 0.000388* −5.019226* −4.744757 −4.911463 4 163.5996 0.006181 0.000400 −4.987085 −4.678307 −4.865851 5 163.6536 0.090531 0.000413 −4.956568 −4.613482 −4.821863 6 163.9506 0.488669 0.000423 −4.933891 −4.556497 −4.785717 * indicates lag order selected by the criterion LR: sequential modified LR test statistic (each test at 5% level) FPE: Final prediction error AIC: Akaike information criterion SC: Schwarz information criterion HQ: Hannan-Quinn information criterion Table 1C. Autoregressive distributed lag model Dependent Variable: D(EXCHANGE_RATE_VOLATILITY) Method: Least Squares Sample (adjusted): 2001Q1 2017Q2 Included observations: 66 after adjustments Variable Coefficient Std. Error t-Statistic Prob. C −0.078767 0.035164 −2.239975 0.0296 D(Er(−1)) 0.376696 0.115956 3.248613 0.0021 D(Er(−2)) 0.019483 0.119789 0.162643 0.8715 D(IR(−1)) 0.006211 0.002405 2.582708 0.0128 D(IR(−2)) −3.96E-05 0.002443 −0.016200 0.9871 D(MS(−1)) 0.047374 0.037875 1.250789 0.2168 D(MS(−2)) 0.070587 0.038317 1.842186 0.0714 D(I(−1)) −0.000887 0.000884 −1.002944 0.3207 D(I(−2)) −0.000639 0.000785 −0.814146 0.4194 D(GSE(−1)) 1.47E-06 2.15E-06 0.681672 0.4986 D(GSE(−2)) 8.21E-07 2.16E-06 0.379743 0.7057 Er(−1) −0.392991 0.109908 −3.575645 0.0008 IR(−1) −0.000913 0.000918 −0.994062 0.3250 MS(−1) 0.008767 0.003255 2.693163 0.0096 I(−1) 0.001702 0.000872 1.951999 0.0566 GSE(−1) −4.49E-07 1.03E-06 −0.435971 0.6647 R-squared 0.488367 Mean dependent var −0.001978 Adjusted R-squared 0.334878 S.D. dependent var 0.021404 (Continued) Page 17 of 19 Mohammed et al., Cogent Economics & Finance (2021), 9: 1893258 https://doi.org/10.1080/23322039.2021.1893258 Table 1C. (Continued) Dependent Variable: D(EXCHANGE_RATE_VOLATILITY) S.E. of regression 0.017456 Akaike info criterion −5.051008 Sum squared resid 0.015236 Schwarz criterion −4.520183 Log likelihood 182.6833 Hannan-Quinn criter. −4.841254 F-statistic 3.181758 Durbin-Watson stat 2.011294 Prob(F-statistic) 0.001065 Source: Author’s computation Table 1D. Joint significance test of the short run coefficients of policy rate in the exchange rate volatility model for Ghana Wald Test: Equation: Untitled Test Statistic Value Df Probability F-statistic 3.108249 (2, 54) 0.0528 Chi-square 6.216497 2 0.0447 Null Hypothesis: C(4)=C(5)=0 Table 1E. VIF test for multicollinearity Variable VIF 1/VIF CB Policy Rate 3.5 0.285994 Inflation 3.2 0.312361 Money Supply 1.49 0.671686 GSE Composite Index 1.43 0.699653 Mean VIF 2.4 Table 1F. OLS regression Dependent Variable: Exchange rate volatility Variables Coefficients Standard Deviation P-value Intercept −0.0152 0.0231 0.5138 PR 0.0003 0.0015 0.8292 INF 0.0035 0.0010 0.0007 MS 0.0000 0.0000 0.0001 GSE-CI −0.0002 0.0001 0.2692 Multiple R 0.6723 R Square 0.4520 Adjusted R Square 0.4177 Standard Error 0.0344 Observations 69.0000 Page 18 of 19 Mohammed et al., Cogent Economics & Finance (2021), 9: 1893258 https://doi.org/10.1080/23322039.2021.1893258 © 2021 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. You are free to: Share — copy and redistribute the material in any medium or format. Adapt — remix, transform, and build upon the material for any purpose, even commercially. The licensor cannot revoke these freedoms as long as you follow the license terms. Under the following terms: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. No additional restrictions You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits. Cogent Economics & Finance (ISSN: 2332-2039) is published by Cogent OA, part of Taylor & Francis Group. Publishing with Cogent OA ensures: • Immediate, universal access to your article on publication • High visibility and discoverability via the Cogent OA website as well as Taylor & Francis Online • Download and citation statistics for your article • Rapid online publication • Input from, and dialog with, expert editors and editorial boards • Retention of full copyright of your article • Guaranteed legacy preservation of your article • Discounts and waivers for authors in developing regions Submit your manuscript to a Cogent OA journal at www.CogentOA.com Page 19 of 19