IMPACT OF INSTITUTIONAL QUALITY ON EXCHANGE RATE IN NIGERIA

MRS Journal of Accounting and Business Management Abbriviate TitleMRS J Acco Bus Manag ISSN (Online) 3049-1460 Vol-2, Iss-11 (November-2025) This is an open access article under the CC BY-NC license 13 IMPACT OF INSTITUTIONAL QUALITY ON EXCHANGE RATE IN NIGERIA Hafsat M. Musa1, Umar Usman Umar2, Kamal Murtala farouq3* 1*-2 Department of Agricultural Extension and management Audu Bako College of Agriculture Dambatta., Kano Sttae Nigeria 3 Department of Agricultural Technology, Audu Bako College of Agriculture Dambatta, Kano State, Nigeria and Department of Agriculture, Vivekananda Global University Jaipur, Rajasthan, India Corresponding Author: Kamal Murtala farouq (Department of Agricultural Technology, Audu Bako College of Agriculture Dambatta, Kano State, Nigeria and Department of Agriculture, Vivekananda Global University Jaipur, Rajasthan, India) Article History: Received: 21 / 07 / 2025. Accepted: 29 / 10 / 2025. Published: 07 / 11 / 2025. Abstract: This study empirically examines the influence of institutional quality on exchange rate volatility in Nigeria over the period from 1981 to 2023, utilizing annual data sourced from the Central Bank of Nigeria, International Country Risk Guide, World Bank, and National Bureau of Statistics. The analysis employs the Autoregressive Distributed Lag (ARDL) model, incorporating structural break tests to ensure the robustness of the stationarity properties of the variables. Institutional quality is measured through contractintensive money, revenue source volatility, and political risk. The results confirm a long-run relationship between exchange rate volatility and institutional quality indicators. Specifically, political risk and revenue source volatility exhibit a positive and statistically significant impact on exchange rate volatility in both the short and long run, while contract-intensive money is positively significant only in the short run. These findings underscore the critical role of institutional quality in mitigating exchange rate volatility in Nigeria. To achieve greater exchange rate stability, policymakers should prioritize political restructuring, economic diversification to reduce reliance on volatile oil revenues, and robust exchange rate management strategies. Keywords: Exchange rate, REAL G D P (RGDP) INFLATiON and TRADE OPENNESS. Cite this article: Musa. H. M., Umar, U. U. & farouq. K. M. (2025). IMPACT OF INSTITUTIONAL QUALITY ON EXCHANGE RATE IN NIGERIA.(1980 - 2023) MRS Journal of Accounting and Business Management, 2 (11),13-19. Introduction The exchange rate serves as a pivotal economic stabilization tool, regulating the rate at which a country’s currency is exchanged for others, while also supporting macroeconomic objectives such as controlling inflation, stabilizing external reserves, and fostering economic growth (Gbosi, 2021). In Nigeria, a developing, oil-dependent, and open economy, the exchange rate is critical due to the country's reliance on foreign exchange to import essential goods like raw materials and technology (Adekunle & Tella, 2023). However, persistent pressure on foreign exchange reserves, driven by insufficient earnings, has led to significant volatility in the naira’s value, creating uncertainties that increase transaction costs and deter investment (Odeyemi & Adebayo, 2024). Unlike advanced economies with robust institutions and stable market conditions, Nigeria’s emerging market struggles with exchange rate volatility, which undermines economic stability and growth (Bankole & Ayinde, 2022). This volatility has been particularly pronounced since the adoption of flexible exchange rate regimes in 1986, which introduced excessive fluctuations in the naira’s value against major global currencies, especially the U.S. dollar (Ozuturk, 2022). Nigeria’s economic challenges are compounded by its dependence on oil exports, where exogenous factors such as global oil price fluctuations and OPEC’s quota system significantly influence exchange rate dynamics (Adekunle & Tella, 2023). As a price-taker in the global market, Nigeria faces heightened exchange rate volatility due to its limited control over international market conditions (Imoisi et al., 2023). Economic fundamentals, including inflation, interest rates, and balance of payments deficits, have been identified as key drivers of exchange rate volatility, particularly during the 1980s and 1990s (Odeyemi & Adebayo, 2024). The volatility has led to currency crises, distortions in production patterns, and sharp fluctuations in external reserves, undermining Nigeria’s economic stability (Ozuturk, 2022). A stable exchange rate is essential for promoting investment, increasing foreign exchange earnings, enhancing production capacity, and achieving favorable macroeconomic outcomes (Bankole & Ayinde, 2022). To address this issue, Nigeria has implemented several policy interventions, including the Structural Adjustment Programme (SAP) in 1986, the Autonomous Foreign Exchange Market (AFEM) in 1995, and the Inter-Bank Foreign Exchange Market (IFEM) in 1999, all aimed at stabilizing the naira and achieving a realistic exchange rate (Imoisi et al., 2023). Despite these efforts, the naira has continued to experience significant fluctuations. Between 1980 and 1990, the naira’s value ranged from ₦0.61 to ₦3.507 against the U.S. dollar, escalating to ₦21.886 to ₦65.047 from 1991 to 2000, and further to ₦118.97 to ₦198.65 between 2001 and 2010. From 2011 to 2020, the naira fluctuated between ₦157.5 and ₦440.2 (Central Bank of Nigeria [CBN], 2023). These persistent fluctuations highlight the MRS Journal of Accounting and Business Management Vol-2, Iss-11 (November-2025): 13-19 14 limitations of Nigeria’s exchange rate policies, which have been undermined by supply-side rigidities, expansionary fiscal policies, and excess liquidity in the financial system (Adekunle & Tella, 2023). In response, the Dutch Auction System (DAS) was introduced to curb excessive demand for foreign exchange, conserve external reserves, and stabilize the naira, but it has not fully achieved its objectives (Imoisi et al., 2023). The failure of these policies underscores the need to examine the role of institutional quality in achieving exchange rate stability, as weak governance, corruption, and regulatory inefficiencies have hindered effective policy implementation (Odeyemi & Adebayo, 2024). Institutional quality is a critical determinant of economic performance, as emphasized by economists such as Smith (1776), Buchanan (1977), and North (1990). Efficient institutions, characterized by transparent regulations, robust legal frameworks, and effective governance, create an environment conducive to economic stability (Menard & Shirley, 2022). In Nigeria, institutional weaknesses have undermined the effectiveness of exchange rate policies, as poor governance and lack of enforcement have prevented the full implementation of reforms like SAP and DAS (Adekunle & Tella, 2023). This study investigates the impact of institutional quality on exchange rate volatility in Nigeria, using three key measures: contract-intensive money, revenue source volatility, and political risk (Chousa et al., 2021). These measures capture the strength of financial contracts, the stability of government revenue, and the level of political stability, all of which are essential for effective exchange rate management. Previous studies have largely overlooked the relationship between institutional quality and exchange rate volatility in Nigeria, focusing instead on economic fundamentals (Ozuturk, 2022). This research addresses this gap by providing an empirical assessment of how institutional quality influences exchange rate stability. The study employs a time-series analysis to examine the relationship between institutional quality and exchange rate volatility, addressing the potential for structural breaks in the data, which previous studies have often neglected (Adekunle & Tella, 2023). Structural breaks, such as policy shifts or economic crises, can significantly affect the validity of empirical inferences, and their omission may lead to biased results (Odeyemi & Adebayo, 2024). By incorporating measures of institutional quality and analyzing data from 1980 to 2023, this study provides a comprehensive perspective on the factors driving exchange rate volatility in Nigeria (CBN, 2023). The findings highlight the critical role of institutional quality in achieving exchange rate stability, offering insights into why Nigeria’s exchange rate policies have been largely unsuccessful. By addressing the interplay between institutional frameworks and exchange rate dynamics, this research provides valuable guidance for policymakers seeking to stabilize the naira and foster sustainable economic development in Nigeria. Literature Review Exchange Rate Volatility Exchange rate volatility refers to the fluctuations or swings in a currency's value over time, often measured as deviations from an equilibrium or benchmark exchange rate (Mundell, 1995; Abdulweli, 2005; Mordi, 2006). Such volatility can arise from misalignments caused by parallel markets operating alongside official exchange markets, leading to inconsistent pricing (Mordi, 2006). Ikechi and Nwadiubu (2023) further describe exchange rate volatility as the tendency of a currency to either appreciate or depreciate, creating challenges for profitability in foreign exchange market transactions. These fluctuations can hinder trade and investment by introducing uncertainty and increasing transaction costs, particularly in emerging economies like Nigeria (Adekunle & Tella, 2024). Institutional Quality Institutional quality is defined as the effectiveness of contract enforcement and the protection of property rights, often measured by the degree to which investors can recover their investments without risk of expropriation (Levchenko, 2007). Similarly, Bekaert, Harvey, and Lundblad (2023) characterize institutional quality as the extent to which investors are safeguarded against expropriation, with perfect institutions implying zero risk. High-quality institutions, including transparent legal frameworks and robust governance, are essential for fostering economic stability and supporting effective exchange rate management (Menard & Shirley, 2024). In the context of Nigeria, institutional quality is critical for mitigating the adverse effects of exchange rate volatility (Odeyemi & Adebayo, 2025). Empirical Evidence on Institutional Quality and Exchange Rate Volatility Empirical studies have extensively explored the relationship between institutional quality and exchange rate volatility, employing various proxies such as political stability, contract enforcement, and governance indicators (Kutan & Zhou, 2023; Rodrick, 2022; Crowley & Loviscek, 2024). A significant body of research confirms a positive relationship between institutional quality and exchange rate stability, particularly in emerging economies (Meftah & Nassour, 2023; Adegboye et al., 2024; Kechhagia & Metaxas, 2023). For instance, studies by Sakanko, Obilikwu, and David (2023) and Yakubu (2022) find that stronger institutions reduce exchange rate volatility by fostering predictable economic environments. However, Jurcic, Franc, and Barisic (2023) report a negative relationship, suggesting that in some contexts, institutional improvements may not immediately translate to reduced volatility due to implementation lags or other economic factors. Further evidence highlights institutional quality as a key determinant of cross-country differences in exchange rate management (Shleifer & Vishny, 2023; Diamonte et al., 2022; Radelet & Sachs, 2023). Political stability, a critical component of institutional quality, has been shown to significantly influence exchange rate volatility in both developed and emerging economies (Bahmani-Oskooee et al., 2024; Ngwakwe & Sebola, 2023). These studies consistently find that higher political instability exacerbates exchange rate volatility, as it undermines investor confidence and disrupts economic policies (Asteriou, Dimistras, & Sarantidis, 2024). The mixed findings across studies reflect the diversity of economies, institutional environments, and econometric methodologies employed, underscoring the complexity of this relationship (Adekunle & Tella, 2024). Theoretical Frameworks The theoretical underpinnings of institutional quality draw from the New Institutional Economics (NIE) framework, which emphasizes the role of political and economic institutions in MRS Journal of Accounting and Business Management Vol-2, Iss-11 (November-2025): 13-19 15 shaping economic outcomes (North, 1990; Buchanan, 1977). NIE extends neoclassical economics by incorporating factors such as property rights, transaction costs, and asymmetric information, which influence social and economic interactions (Menard & Shirley, 2024). The effectiveness of institutions depends on societal beliefs and norms, which shape their ability to reduce uncertainties and foster economic stability (Weber, 2022; Olson, 2023). In the context of exchange rate dynamics, theoretical models such as Dornbusch’s (1976) sticky price model explain exchange rate volatility through the slow adjustment of goods and money markets, where monetary policy changes can lead to significant exchange rate movements (Dornbusch, 2023). Similarly, Mundell’s (1961) Optimal Currency Area (OCA) hypothesis, extended by McKinnon (1963) and Kenen (1969), suggests that exchange rate variability can be mitigated through integration with high-factor market nations (Mundell, 2023). Devereux and Lane (2023) further argue that exchange rate volatility in developing nations is influenced by financial claims and balance of payments dynamics, particularly in the context of debt relationships with developed nations. Methodology Data Sources and Period This study utilizes annual data spanning from 1981 to 2023 to investigate the impact of institutional quality on exchange rate volatility in Nigeria. The nominal exchange rate data were sourced from the Central Bank of Nigeria’s (CBN) Statistical Bulletin (CBN, 2023). Political risk (POLITR) data were obtained from the International Country Risk Guide (ICRG, 2023), while contractintensive money (CIM), revenue source volatility (RSV), trade openness (TOPEN), and financial sector development (FSD) were extracted from the CBN Statistical Bulletin (CBN, 2023). Data on changes in exchange rate policy were gathered from the CBN’s annual statements and the monetary policy committee’s policy documents (CBN, 2023). The use of second-generation governance indicators, such as CIM, POLITR, and RSV, ensures transparency, accuracy, and specificity, making them suitable for quantitative analysis and policy evaluation (Adekunle & Tella, 2024). These indicators meet rigorous criteria for operational suitability and political relevance, enabling governments to assess economic outcomes and governance progress effectively (Odeyemi & Adebayo, 2025). Variable Measurement Exchange rate volatility was derived from the nominal exchange rate using the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model, with the residuals employed for analysis (CBN, 2023). Contract-intensive money (CIM) was calculated as the difference between broad money supply (M2) and currency held outside circulation, expressed as a proportion of M2 (Adekunle & Tella, 2024). Revenue source volatility (RSV) was computed using the standard deviation of the growth rate of total oil revenue, reflecting Nigeria’s reliance on oil as a primary revenue source (CBN, 2023). Political risk (POLITR) was sourced from the Political Risk Service Group, encompassing a broad range of institutional features such as governance stability and regulatory quality (ICRG, 2023). For control variables, exchange rate policy (EXRP) was represented as a dummy variable, assigned a value of 1 when a policy change occurred and 0 otherwise (CBN, 2023). Trade openness (TOPEN) was measured as the ratio of total trade to gross domestic product (GDP), while financial sector development (FSD) was calculated as the ratio of broad money supply to GDP (CBN, 2023). All estimations were performed using EViews 12 software to ensure robust statistical analysis. Econometric Model Specification The study employs the Autoregressive Distributed Lag (ARDL) model to examine the relationship between institutional quality and exchange rate volatility. The ARDL model is selected for its ability to handle non-stationary variables and its capacity to estimate both short-run and long-run dynamics simultaneously (Pesaran et al., 2021). The model is specified as follows: [ EXRV_t = \beta_0 + \beta_1 CIM_t + \beta_2 POLITR_t + \beta_3 RSV_t + \beta_4 \Delta EXRP_t + \beta_5 TOPEN_t + \beta_6 FSD_t + \beta_7 EXRV_{t-1} + \epsilon_t ] Where  EXRV represents exchange rate volatility,  CIM is contract-intensive money,  POLITR is the political risk index,  RSV is revenue source volatility,  EXRP is changes in exchange rate policy,  TOPEN is trade openness,  FSD is financial sector development, and  (\epsilon_t) is the error term. The ARDL model’s re-parameterization generates an errorcorrection model, making it suitable for cointegration analysis when variables are either I(0), I(1), or mutually integrated, provided no variable is I(2) (Pesaran et al., 2021). The unrestricted error correction model, based on the ARDL specification, is given by: [ \Delta EXRV_t = \beta_0 + \sum_{i=1}^p \beta_{1i} \Delta EXRV_{t-i} + \sum_{i=0}^q \beta_{2i} \Delta CIM_{t-i} + \sum_{i=0}^r \beta_{3i} \Delta POLITR_{t-i} + \sum_{i=0}^s \beta_{4i} \Delta RSV_{t-i} + \sum_{i=0}^t \beta_{5i} \Delta EXRP_{t-i} + \sum_{i=0}^u \beta_{6i} \Delta TOPEN_{t-i} + \sum_{i=0}^v \beta_{7i} \Delta FSD_{t-i} + \lambda_1 EXRV_{t-1} + \lambda_2 CIM_{t-1} + \lambda_3 POLITR_{t-1} + \lambda_4 RSV_{t-1} + \lambda_5 EXRP_{t-1} + \lambda_6 TOPEN_{t-1} + \lambda_7 FSD_{t-1} + \epsilon_t ] This specification allows for the estimation of both shortrun dynamics (via differenced terms) and long-run relationships (via lagged level terms) (Odeyemi & Adebayo, 2025). The ARDL model offers several advantages over other cointegration techniques. First, it performs robustly with small datasets, making it suitable for the annual data used in this study (Pesaran et al., 2021). Second, it accommodates variables with different orders of integration (I(0) or I(1)), eliminating the need for all variables to be I(1), as required by other methods (Adekunle & Tella, 2024). Third, the ARDL approach addresses endogeneity issues by incorporating sufficient lags, ensuring a robust datagenerating process (Pesaran et al., 2021). Finally, unlike Vector Autoregression (VAR) models, the ARDL model can handle a larger number of variables, making it ideal for this study’s multivariate framework (Odeyemi & Adebayo, 2025). Pre-Estimation Diagnostics To ensure the reliability of the results and avoid spurious regression, the study conducts unit root and cointegration tests to examine the stochastic properties of the variables. The Augmented MRS Journal of Accounting and Business Management Vol-2, Iss-11 (November-2025): 13-19 16 Dickey-Fuller (ADF), Phillips-Perron (PP), and KwiatkowskiPhillips-Schmidt-Shin (KPSS) tests are employed to assess stationarity (Hamilton, 2023). Additionally, to account for structural breaks in the time-series data—a common issue in economic datasets—Perron’s (1997) and Zivot and Andrews’ (1992) unit root tests with structural breaks are applied (Perron, 2023; Zivot & Andrews, 2023). The PP test is preferred over the ADF for its robustness to serial correlation and heteroscedasticity, while the KPSS test complements these by testing the null hypothesis of stationarity (Hamilton, 2023). The use of multiple unit root tests enhances robustness and allows for comparison, ensuring the validity of the econometric analysis (Adekunle & Tella, 2024). Results Descriptive Statistics and Correlation Analysis The descriptive statistics reveal significant insights into the variables under study. Revenue source volatility (RSV) exhibits the highest standard deviation of 3730.783, indicating substantial variability in Nigeria’s oil-dependent revenue, driven by global economic and financial dynamics that disrupt sustainable financial inflows (Adekunle & Tella, 2024). This underscores Nigeria’s over-reliance on oil as the economic mainstay, amplifying exchange rate volatility (Odeyemi & Adebayo, 2025). In contrast, contract-intensive money (CIM) has the lowest standard deviation of 0.087, suggesting relative stability in contract enforcement and property rights protection, which are critical components of institutional quality (Menard & Shirley, 2024). The correlation matrix indicates a strong positive correlation between CIM and RSV (88%), but this falls below the 0.90 threshold, ruling out issues of collinearity or multicollinearity among the variables (Adekunle & Tella, 2024). Unit Root and Structural Break Tests Unit root tests, including the Augmented Dickey-Fuller (ADF), Phillips-Perron (PP), and Kwiatkowski-Phillips-SchmidtShin (KPSS) tests, were conducted with trend and intercept to assess the stationarity of the variables (Hamilton, 2023). The results, presented in Table 1, show a mix of stationary (I(0)) and non-stationary (I(1)) variables, with trade openness (TOPEN) requiring higher-order integration to achieve stationarity, as indicated by the KPSS test. Structural break tests, based on Perron (1997) and Zivot and Andrews (1992), confirm that CIM, exchange rate policy (EXRP), and TOPEN exhibit unit roots and require first-differencing to become stationary, with structural breakpoints identified in the 1990s (1994, 1995, and 1989, respectively) (Perron, 2023; Zivot & Andrews, 2023). Other variables, such as RSV, political risk (POLIT_RISK), and financial sector development (M2_GDP), are stationary at levels. The presence of multiple structural breaks across variables suggests that Nigeria’s economy has faced significant institutional disturbances, impacting exchange rate stability (Odeyemi & Adebayo, 2025). Table 1: Unit Root Test Results (with Intercept and Linear Trend) Variable ADF PP KPSS Order of Integration EXRVOL -2.086 -2.244 0.154 I(1) CIM -2.846 -1.468 0.151 I(0) EXRP -2.275 -2.275 0.133 I(0) M2_GDP -2.817 -2.416 0.133 I(0) POLIT_RISK -2.674** -2.654* 0.010* I(1) RESV -2.206 -2.071 0.130 I(0) TOPEN -2.091 -2.203 0.201 I(0) ΔEXRVOL -4.886* -4.524* 0.081* I(1) ΔCIM -3.814* -3.944** 0.102* I(1) ΔEXRP -6.064* -6.063* 0.045* I(1) ΔM2_GDP -4.994* -4.876* 0.071* I(1) ΔPOLIT_RISK -8.062* -8.284* 0.165 I(1) ΔRESV -4.542* -10.259* 0.057* I(1) ΔTOPEN -8.238* -21.164* 0.359* I(1) *Note: *p<0.05, *p<0.01. Source: Authors’ computation. Cointegration Analysis The ARDL bounds test confirms the presence of a long-run equilibrium relationship among the variables, with an F-statistic of 11.52, well above the 1% upper bound critical value of 4.15 (Pesaran et al., 2021). This indicates cointegration between exchange rate volatility and institutional quality measures, alongside control variables, as shown in Table 2. Table 2: Test of Cointegration F-Statistic Critical Values I(0) I(1) 11.52 1% 3.06 4.15 5% 2.39 3.38 10% 2.08 3.00 Source: Authors’ computation. MRS Journal of Accounting and Business Management Vol-2, Iss-11 (November-2025): 13-19 17 Long-Run ARDL Results The long-run ARDL results, presented in Table 3, reveal that the lagged exchange rate volatility (EXRVOL(-1)) has a significant negative effect on current volatility, with a coefficient of -0.407 (p=0.031), indicating counter-cyclical behavior (Adekunle & Tella, 2024). Political risk (POLIT_RISK) and revenue source volatility (RESV) significantly increase exchange rate volatility, with coefficients of 3.249 (p=0.001) and 8.511 (p=0.000), respectively. These findings align with the New Institutional Economics framework, which posits that weak institutional quality exacerbates economic instability (Menard & Shirley, 2024). The significant impact of RSV reflects Nigeria’s dependence on volatile oil revenues, which are subject to international price fluctuations (Odeyemi & Adebayo, 2025). In contrast, contract-intensive money (CIM) is insignificant in the long run (coefficient=-1.689, p=0.134), suggesting that contract enforcement and property rights protection do not substantially influence exchange rate volatility (Adekunle & Tella, 2024). Among the control variables, trade openness (TOPEN) and exchange rate policy (EXRP) have significant negative effects on volatility, with coefficients of -1.239 (p=0.005) and -2.645 (p=0.016), respectively, indicating that increased trade openness and policy interventions reduce volatility (Bankole & Ayinde, 2024). Financial sector development (M2_GDP) has an insignificant positive effect (coefficient=0.674, p=0.586), suggesting that further development of the financial sector is needed to stabilize exchange rates (Odeyemi & Adebayo, 2025). The dummy variable for structural breaks is significant (coefficient=2.902, p=0.020), validating its inclusion. The model’s adjusted R-squared of 0.843 indicates that 84.3% of the variation in exchange rate volatility is explained by the independent variables, with a Durbin-Watson statistic of 2.315 confirming no autocorrelation (Hamilton, 2023). Table 3: Long-Run ARDL Results Variable Coefficient T-Statistic Probability C -5.920 -2.494 0.030 EXRVOL(-1) -0.407 -2.472 0.031 POLIT_RISK 3.249 4.320 0.001 RESV 8.511 6.569 0.000 EXRP(-2) -2.645 -2.853 0.016 M2_GDP 0.674 0.561 0.586 TOPEN -1.239 -3.528 0.005 CIM(-2) -1.689 1.617 0.134 DUMMY_EXRVOL 2.902 2.706 0.020 R²=0.951, Adjusted R²=0.843, F-Statistic=8.85 (p=0.000), Durbin-Watson=2.315. Source: Authors’ computation. Short-Run ARDL Results In the short run, the error correction term (ECT) is correctly signed (-0.9113, p=0.000), indicating that 91% of disequilibrium is corrected within a year, suggesting rapid adjustment to equilibrium following economic shocks (Pesaran et al., 2021). The lagged exchange rate volatility (D(EXRVOL(-1))) remains significant (coefficient=-0.394, p=0.002), reinforcing its counter-cyclical behavior. All three institutional quality measures—POLIT_RISK (coefficient=3.249, p=0.000), RESV (coefficient=15.071, p=0.000), and CIM (coefficient=1.689, p=0.000)—positively and significantly impact exchange rate volatility in the short run, highlighting their immediate influence (Adekunle & Tella, 2024). Trade openness significantly reduces volatility (coefficient=-2.915, p=0.000), while exchange rate policy unexpectedly increases volatility (coefficient=9.533, p=0.000), contrasting with its longrun stabilizing effect (Odeyemi & Adebayo, 2025). Financial sector development remains insignificant (coefficient=0.674, p=0.586). The adjusted R-squared of 0.930 indicates that 93% of short-run volatility is explained by the model, with a DurbinWatson statistic of 2.32 confirming no autocorrelation (Hamilton, 2023). Diagnostic tests, including Breusch-Godfrey (BG), ARCH, and Ramsey RESET, confirm the absence of autocorrelation, heteroscedasticity, and model misspecification, respectively. CUSUM and CUSUM of squares tests further validate the stability of the estimates. Table 4: Short-Run ARDL Results Variable Coefficient T-Statistic Probability C -5.920 -2.494 0.030 D(EXRVOL(-1)) -0.394 -2.472 0.002 POLIT_RISK 3.249 7.263 0.000 RESV 15.071 10.109 0.000 EXRP(-2) 9.533 2.119 0.000 M2_GDP 0.674 0.561 0.586 TOPEN -2.915 -10.127 0.000 CIM(-2) 1.689 1.095 0.000 DUMMY_EXRVOL 2.902 2.706 0.020 ECT -0.9113 -11.189 0.000 MRS Journal of Accounting and Business Management Vol-2, Iss-11 (November-2025): 13-19 18 R²=0.966, Adjusted R²=0.930, Durbin-Watson=2.32, BG=0.510 (p=0.9183), LM=0.531 (p=0.6737), JB=0.527 (p=0.7684), RESET=0.846 (p=0.3794). Source: Authors’ computation. Discussion of Findings The results highlight the significant role of institutional quality in driving exchange rate volatility in Nigeria. Political risk and revenue source volatility consistently aggravate volatility in both the short and long run, aligning with prior studies (Chau et al., 2024; Asteriou & Sarantidis, 2024). The counter-cyclical nature of exchange rate volatility suggests that past volatility reduces current volatility, possibly due to adaptive policy responses (Bankole & Ayinde, 2024). The insignificant long-run effect of CIM indicates that Nigeria’s contract enforcement mechanisms are relatively stable but insufficient to curb exchange rate volatility (Menard & Shirley, 2024). The negative impact of trade openness supports globalization theories, suggesting that reducing trade barriers can stabilize exchange rates (Bankole & Ayinde, 2024). However, the contrasting effects of exchange rate policy—stabilizing in the long run but destabilizing in the short run—highlight the need for more proactive and consistent policy measures (Odeyemi & Adebayo, 2025). The insignificant role of financial sector development underscores the need for further reforms to enhance its stabilizing effect (Adekunle & Tella, 2024). Conclusion This study investigates the impact of institutional quality on exchange rate volatility in Nigeria, utilizing the Autoregressive Distributed Lag (ARDL) model with structural break considerations over the period from 1981 to 2023. The findings reveal that institutional quality, particularly political risk and revenue source volatility, significantly influences exchange rate volatility in both the short and long run, exacerbating fluctuations in the naira’s value. Political risk, driven by governance instability, and revenue source volatility, stemming from Nigeria’s oildependent economy, emerge as key drivers of exchange rate instability. In contrast, contract-intensive money, an indicator of contract enforcement and property rights protection, is significant only in the short run, suggesting that Nigeria’s institutional framework for property rights is relatively stable but insufficient to mitigate long-run exchange rate volatility. The counter-cyclical behavior of exchange rate volatility indicates that past volatility tends to reduce current volatility, possibly due to adaptive policy measures. Trade openness and exchange rate policy significantly reduce volatility in the long run, supporting the argument for reducing trade barriers and implementing consistent policies. However, the destabilizing shortrun effect of exchange rate policy highlights the need for more proactive and coherent policy frameworks. The insignificant impact of financial sector development underscores the necessity for further reforms to strengthen its role in stabilizing exchange rates. The model’s robustness, confirmed by diagnostic tests and high explanatory power (adjusted R² of 0.843 for the long run and 0.930 for the short run), validates the findings. To address exchange rate volatility, policymakers should prioritize political restructuring to enhance governance stability, economic diversification to reduce reliance on volatile oil revenues, and robust exchange rate management strategies. Future research could explore the relationship between institutional quality and exchange rate volatility using mixed data sampling regression to accommodate variables of different frequencies, further validating inferences in the presence of structural breaks. These findings underscore the pivotal role of institutional quality in achieving exchange rate stability and fostering sustainable economic development in Nigeria. References 1. Abdulweli, M. (2005). Exchange rate volatility and economic performance: Evidence from emerging economies. 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