Exchange rate behaviour in ASEAN countries: A sensitivity analysis

Umoru, David; Igbinovia, Beauty; Aliyu, Mohammed Farid Article Exchange rate behaviour in ASEAN countries: A sensitivity analysis The Central European Review of Economics and Management (CEREM) Provided in Cooperation with: WSB Merito University in Wrocław Suggested Citation: Umoru, David; Igbinovia, Beauty; Aliyu, Mohammed Farid (2024) : Exchange rate behaviour in ASEAN countries: A sensitivity analysis, The Central European Review of Economics and Management (CEREM), ISSN 2544-0365, WSB Merito University in Wrocław, Wrocław, Vol. 8, Iss. 4, pp. 37-73, https://doi.org/10.29015/cerem.1008 This Version is available at: https://hdl.handle.net/10419/312543 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc-nd/4.0/ CENTRAL EUROPEAN REVIEW OF ECONOMICS AND MANAGEMENT ISSN 2543-9472; eISSN 2544-0365 www.cerem-review.eu www.ojs.wsb.wroclaw.pl Vol. 8, No.4, December 2024, 37-73 Correspondence address: David UMORU, Dertment of Economics, Edo State University Uzairue, Iyamho, Nigeria, Email: [email protected]. Beauty IGBINOVIA, Department of Economics, Edo State University Uzairue, Iyamho, Nigeria, E-mail: [email protected] Mohammed Farid ALIYU, Department of Economics, Edo State University Uzairue, E-mail:[email protected] © 2024 WSB MERITO UNIVERSITY WROCŁAW Exchange rate behaviour in ASEAN countries – a sensitivity analysis David UMORU, Beauty IGBINOVIA, Mohammed Farid ALIYU Edo State University Uzairue, Iyamho, Nigeria Received: 27.06.2024, Revised: 06.10.2024, Accepted: 19.10.2024 doi: http://10.29015/cerem.1008 Aim: The study examined the behavior of exchange rate in ASEAN countries. This was highly necessitated in order to account for the structural break in the data set occasioned by global financial crisis. Research method: The quantile regression sensitivity analysis was performed on daily series of exchange rate volatility for 8 ASEAN countries having divided our sample into two, before and after the financial crisis eras. Periods of low market volatility (2001–2006 plus 2010–2017) and high market volatility (1990–2000, 2007–2009, plus 2018–2023) correlate to the periods before and after the financial crisis, respectively. Findings: The empirical finding going forward is that since the global financial crisis took effect, exchange rate volatility has not been effectively curtailed by the governments and monetary authorizes of ASEAN countries especially in Thailand, Malaysia, Indonesia and Vietnam respectively. There is therefore the need for a policy fight in favour of stability of the currency exchange rates. Originality: The originality of the research resides with the sensitivity analysis which validates the presence of high persistence in the volatility of the Thai Baht exchange rate throughout the quantiles. This was followed on by the high persistence in the exchange rate of the Malaysian ringgit which began at the 70th quantile in the pre-financial crisis period with a persistence value of 1.0097 as against the 30th quantile in the post-financial crisis estimations with a persistence value of 1.0387. The Indonesian Rupiah and Vietnamese dong took turns as regards volatility persistence. We also found significant ARCH effect which instigated further estimations of the GARCH and FIGARCH models as robustness checks. Contributions: With the GARCH results, the study contributed to establishing persistence of volatility in the exchange rates of all ASEAN countries in our sample, with varying degrees and this could be attributed instabilities in the economies. Explicitly, the significance of the FIGARCH coefficient confirms the persistence of volatility over time with considerable long-term memory effect. This implies that once the exchange rate becomes volatile, such volatility last long, influencing future volatility levels noticeably in all the countries. Exchange rate volatility persistence of the Singapore Dollar was very low. David UMORU, Beauty IGBINOVIA, Mohammed Farid ALIYU 38 Keywords: Exchange rate behavior, FIGARCH-DCC, volatility persistence, RER, long-term memory, volatility JEL: A20, B34, C50 1. Introduction Exchange rate, being the value of one currency for the conversion to another, is influenced by numerous factors such as inflation, interest rates, oil price variation, growth of money in circulation, and income growth rate etc. (Umoru, Abugewa-Ejegi, Effiong 2023; Umoru, Akpoviroro, Effiong 2023). The study aimed at evaluating the behaviour of exchange rate in ASEAN countries. The members of the Association of Southeast Asian Nations (ASEAN) covered in this research include Indonesia, Singapore, Philippines, Thailand, Vietnam, Cambodia, Myanmar, and Malaysia. Price and exchange rate stability is the core objective of the monetary authorities of these countries. Indonesia central bank operates a free floating exchange rate regime. Hence, Indonesia rupiah exchange rate is strongly swayed by capital flows and exports earnings. The monetary policy framework of Singapore is exchange rate-centered. The Singapore dollar is regulated against a basket of currencies whose composition is reviewed occasionally in order to accommodate changes in trade patterns. The tradeweighted exchange rate fluctuates within a policy band that is fixed nearby a targeted level and a given appreciation rate. The policy adjustments to the parameters of the exchange rate band are announced every three months. The Philippine government implements the floating exchange rate system for the Philippine Peso. Accordingly, anytime foreign shocks disturb the domestic economy, the flexible peso exchange rate serves as an automatic stabilizer that regulates and provides a restoration to macroeconomic balance. The Bank of Thailand operates a managed float exchange rate system whereby market mechanism fixes the value of the Thai baht while the Bank of Thailand intervenes when the Thai baht exchange rate is extremely volatile. The government of Vietnam operates two exchange rate policies, namely, “following” the market and “unifying” the market. In Cambodia, volatility in the exchange rate, the National Bank of Cambodia (NBC) committee meets to strategize to bring the exchange rate back within range. Currently, the Central Bank of Myanmar Exchange rate behaviour in ASEAN countries – a sensitivity analysis 39 (CBM) issues daily official parallel FX rates having aggregated transactions reported by commercial banks to the online forex trading platform. The exchange rates are accessible and the CBM upholds control over the rates used in these transactions. In Malaysia, the Bank Negara Malaysia (BNM) operates a floating exchange rate system since 2016. The BNM permitted exporters to exchange 75% of their proceeds into the ringgit, while 25% of the proceeds are retained in foreign currency. This practice relaxed forex hedging restrictions and created some liberalization measures which has amplified the volatility of the Malaysian ringgit because huge flexibility is allowed for exporters. The behavior of the exchange rates of ASEAN currencies is of policy significance because financial traders and marketers can rightly predict the performance of the foreign exchange market in ASEAN countries and factor in the volatility of the exchange rate when making investment decisions. As a result, these investors are guided in their decision-making. Overall, the research findings are relevant in that they provide policy guidelines to asset portfolio decision-makers and forex traders in the ASEAN financial markets. In summary, the stability of the exchange rates of all ASEAN currencies relative to foreign currencies is essential in order to achieve and maintain price stability and stability in the financial market. The study is of immense value to policymakers who stand to benefit profoundly from policy findings as regards the formulation of effective regulatory policies; ideas into how digital currencies interact with traditional economic indicators can guide the development of regulatory frameworks that foster innovation while mitigating potential risks. Policymakers can use the findings to design measures that promote financial stability and ensure the responsible integration of forex markets within the broader economic landscape of all ASEAN nations. The next section reviews related and relevant literature. Section three discusses the research methodology, while section four discusses the results and policy implications. The study concludes with section five. David UMORU, Beauty IGBINOVIA, Mohammed Farid ALIYU 40 2. Literature review Kipkorir & Mutai (2024) focused on the influence of commodity price fluctuations on the behavior of the Kenyan Shilling against major currencies from 2015 to 2023. The study sourced its exchange rate and commodity price data from the Central Bank of Kenya and global commodity exchanges. Employing VECM, the research aimed to uncover how global changes in commodity prices, especially tea and coffee, impact the exchange rate. The findings indicated a significant and lasting impact of commodity price volatility on the exchange rate, with a significance level less than 0.05. Long-term analysis showed a slow adjustment to equilibrium, with an annual speed of 3.7%, reflecting the protracted effect of commodity price changes on the currency. The study suggest that Kenya’s central bank should enhance its monitoring of commodity markets and potentially engage in futures contracts to hedge against predictable fluctuations in currency values. Okonkwo & Mbekeani (2023) investigated the behavioral patterns of the Rand against the US Dollar during periods of political instability from 2010 to 2022. Using exchange rate data from the South African Reserve Bank, the study utilized a combination of unit root tests to ensure data stationarity and co-integration analysis to establish relationships, followed by a VECM to estimate the dynamics. The research found that political instability leads to significant volatility in the Rand, with exchange rate movements showing heightened sensitivity during election cycles and major political announcements. The significance level for these fluctuations was found to be below 0.05, indicating a strong relationship. The study also revealed that this relationship persists in the long run, with a speed of adjustment to equilibrium of 5.4% annually. In the short run, each 1% increase in political instability could lead to approximately a 0.12% increase in exchange rate volatility. The study recommended that policymakers and investors consider the timing of political events when assessing currency risk, suggesting that strategic currency management could mitigate the adverse effects on the Rand. Djouakaa et al. (2023) examined real effective exchange rate causes. Their results demonstrated that money supply; direct investment, inflation, imports and interest rate are the main determinants of the real effective exchange rate in the franc zone after using the Driscol-Kraay method on panel data. Also, the use of the Panel Corrected Exchange rate behaviour in ASEAN countries – a sensitivity analysis 41 Standard Error method also revealed the same results. In addition, the analysis of the specificities of the 15 Franc Zone countries made it achievable to take into account the heterogeneity of the panel. As a result, the central banks of each of the 15 Franc Zone countries must incorporate the consequences of the rate of exchange when formulating their monetary policies. Moreover, each government, when formulating its macroeconomic policy, must take into account the repercussions of the exchange market. Aizenman et al. (2023) investigated the link between RER behaviour and international reserve in the era of financial integration. They utilized nonlinear regressions and panel threshold regressions techniques to determine whether international reserve is a determinant of real exchange rate. Their study covered over 110 countries making use of panel data from 2001-2020. Some of the countries include Algeria, Botswana, Brazil, Guinea, Honduras, Hungary, Guinea, Honduras, Hungary and others. The findings show that term of trade shocks had significant on real exchange rate. The results also indicate that countries with intermediate levels of financial development will have a more powerful buffer. The study conducted in SSA nations with a focus on the oil-importing and exporting nations by Korley & Giouvris (2022) evaluated the influence of oil price and oil volatility index on the exchange rate. Their study employed quantile regression and Markov switching models to evaluate their joint effect and showed that oil volatility considerably influenced the exchange rate of all countries. They established that the rising and falling oil prices leads the local currency to devalue or appreciate. They submitted that exchange rates respond to oil price and oil volatility mostly at lower quantiles for all countries which indicates the sensitivity of investors to risks and returns. Bangura et al. (2021) aimed to investigate the behavior of the Leone/US dollar exchange rate based on the influence of global oil price shocks in Sierra Leone throughout the post-war period from June 2002 to May 2020. Among the three estimated models, the EGARCH (1, 1) model emerged as the most suitable fit, with all mean and variance coefficients deemed significant. The empirical findings indicated that a rise in oil prices corresponded to a depreciation of the exchange rate in Sierra Leone amongst others. Raksong & Sombatthira (2021) reported a positive impact on the REER of the ASEAN countries by some variables which include the ratio of FDI to GDP and David UMORU, Beauty IGBINOVIA, Mohammed Farid ALIYU 42 government spending. Trade opening also had a substantial positive impact on REER in all the ASEAN countries studied except Vietnam while terms of trade had significant impact on REER in Malaysia, Philippines and Indonesia. Foreign direct investment also had significant impact on REER, but only in Vietnam. International reserve was shown to have long-run impact on REER in Malaysia, Thailand and Vietnam. Ani & Mashood (2021) undertook a comprehensive analysis to evaluate the behaviour of real exchange rate (RER) in Nigeria over a sample size of 60 years from the period of 1960-2020. The study utilized a multivariate co-integration test, he ADF and KPSS stationarity test as well as the VECM to analyze the data set. The result of the stationarity test showed that the macroeconomic variable under study had no stochastic trends and were stationery at all levels while the result of the Granger causality test showed that real GDP growth rate, inflation rate, money supply growth rate and government expenditure exerted significant influence on real exchange rate behavior. Damayanthi & Gunawardhana (2021) analyzed the behavior of the real effective exchange rate in Sri Lanka with a direct linkage to external sector stability, sampling data from the period from 2010 to 2019. The research employed the vector auto regression (VAR) model as an analytical tool. The results of the study revealed that exchange rate behaved as theoretically expected with changes in policy rates of United States. Although, behavior of inflation, domestic interest rates and net exports were contrary to theoretical expectations. The findings show that even though domestic currency depreciation may lead to increased net exports, Sri Lanka, being a net importing economy, had suffered further depreciation of its domestic currency. Kalaj & Golemi (2020) focused their study on the bases for real exchange rate behaviour in Albania; sing the VAR model to assess data for the period of 1995-2015. The study aims to find the long run relationship among variables. The findings suggest that real exchange rate behavior can be decreased by increasing fiscal policies and decreasing monetary policies. Findings from the study also indicated a long-run relationship between real exchange rate behavior and trade. Romo & Gallardo (2020) explored the bases for the behavior of RER behavior, particularly analyzing the effect of share of wages in output on RER. The study modeled the behavior RER of the domestic currencies of three countries: Mexico, Exchange rate behaviour in ASEAN countries – a sensitivity analysis 43 Korea and France against the US dollar. The econometric technique, VAR model, was specified to find the long-run associations of the RER for each country. The results show that, for each country, there was a negative relationship between the RER and wage share. Findings also showed that Real Exchange Rate is positively related with labor productivity in France and Korea, but inversely related in Mexico. An explanation as to why RER tended to return to the long run normal value was also given. To Kahsay & Patena (2020), estimates from vector error correction models (VECMs) and vector autoregression models (VARs) indicate that the REER responds minimally to changes in fundamentals, with small and delayed responses. Three factors are proposed to have contributed to this minimal response: labor market conditions, price management, and remittance outflows. The policy implications suggest that labor market reforms, price liberalization, and policies encouraging domestic investment could improve the REER’s response to economic fundamentals. Hien et al. (2020) undertook a comprehensive analysis on how huge amounts of remittance can have an effect on a country’s Real Exchange Rate (RER) behavior, thereby causing the Dutch disease. The study focused on countries which receive a quite high value of remittances, specifically on 32 Asian developing countries. Using data covering the period from 2006 to 2016, the S-GMM for the linear dynamic panel data (DPD) was used to analyze the relationship between REER and remittances. The findings show that as per capita remittances increases by 1 percent, REER increases by 0.103 percent, signifying the existence of the Dutch disease. The results further indicated that in countries with low remittance to Gross Domestic Product, remittances results in REER appreciation while for countries having a ratio higher than 1 percent, higher remittances results in real effective exchange rate appreciation. Moreover, the study corroborates other findings why postulate that a flexible exchange rate regime leads to the dampening of the appreciation of the REER caused by increased remittances. Hassan et al. (2020) did a study for 22 OECD countries. The fixed effect model was the estimated technique used to analyze the panel data covering the period 19802015. Their research amongst other results, reveals that countries with a large share of a working population tend to have an appreciating effect on the RER because increase in the working age population leads to an increase in the marginal product of David UMORU, Beauty IGBINOVIA, Mohammed Farid ALIYU 44 capital which increases foreign direct investment thereby causing an increase in capital inflow. The terms of trade also had a significant effect on RER. The implications of the findings suggest that for developing countries experiencing a rapid aging population, there will be a negative effect on their international competitiveness due to RER appreciation. To counter this, the government of these countries will have to adopt a policy of saving more for the future by increasing the effective retirement age or through an increase in budget surplus. After reviewing the literature, a gap is found: many scholars have not felt the need to conduct an empirical investigation into the behavior of ASEAN currency exchange rates within the context of a sensitivity analysis that considers financial crisis periods. Sensitivity analysis receives empirical focus in this study. Thus, trustworthy conclusions that direct the decision-making process regarding firms, investments, and the entire economy are reached by assessing the sensitivity of our data on the currency exchange rates of ASEEAN nations with respect to times of high and low volatility. Sensitivity analysis of this kind offers factual proof of the validity of study conclusions about the behavior of exchange rates in ASEAN nations. The current study focused on the ASEAN countries of Malaysia, Indonesia, Singapore, the Philippines, Thailand, Vietnam, Cambodia, Myanmar, and Vietnam. 3. Methodology To analyze the behavior of exchange rates in ASEAN countries, we estimated quantile regression analysis on the daily series of exchange rate volatility for 8 ASEAN countries using daily data. This was highly necessitated in order to control for the structural break in the data set caused by global financial crisis. Within the scope of this research, 1990 to 2023, the following phases of structural breaks are discernible. The period from 1990 to 1991 witnessed low market volatility due to political stability that existed across countries, fiscal discipline on the part of the governments, absence of terrorism, etc. The period of 1990 to 2000 was a period of high market volatility due to Harshad Mehta Scam of 1992 in Indian that crashed the stock market and Asian tiger Financial crisis of 1997-1998 which led to the collapse Exchange rate behaviour in ASEAN countries – a sensitivity analysis 51 level of volatility that is significant given a z-statistic of 25.52022. The coefficient for RESID(-1)^2 is -0.015426, which is statistically significant (z=-2.509593, p=0.0121), indicating a negative relationship between past squared residuals and current volatility. This negative value suggests a mean-reverting volatility behavior of exchange rate in Singapore; periods of high volatility tend to be followed by lower volatility; a typical characteristic observed in financial markets known as the volatility clustering. Table 5. ARCH results for Philippines Mean Equation Variable Coefficient Std. Error z-Statistic Prob. C 0.171417 0.187246 0.915467 0.3599 NEXC(-1) 0.979605 0.017294 56.64583 0.0000 Variance Equation C 0.083953 0.001614 52.01721 0.0000 RESID(-1)^2 -0.005885 0.000477 -12.32860 0.0000 F=1347.2 (0.0000) Source: Authors’ estimation results with Eviews 10. Table 5 shows that the coefficient for real effective exchange rate is 0.979605, which is significant at the 0.05 level. Hence, real effective exchange rate in the previous period is a strong predictor of the real effective exchange rate in the current period, with a 1 unit increase in real effective exchange rate associated with approximately a 0.98 percent increase in the current NEXC, holding all other variables constant. The ARCH model results suggest that the real exchange rate in Philippines exhibits time-varying volatility that can be partially captured by its own past values. This could have implications for the demand for in Philippines. From Table 6, the coefficient for nominal exchange rate lagged one-period is extremely significant (z-statistic of 59.96873) and near unity (0.976800), pointing towards a strong autoregressive behavior where current real effective exchange rate values closely follow past values. The variance equation, which models the volatility of the real effective exchange rate, features a small but highly significant constant (C=0.030006; z-statistic of 51.68566), indicating a base level of volatility that is consistent yet relatively low, given the scale of the coefficient. This suggests that external shocks or inherent economic volatility in Thailand’s market is modest but David UMORU, Beauty IGBINOVIA, Mohammed Farid ALIYU 52 persistent over time. The ARCH model’s findings shows that real effective exchange rate in Thailand exhibits predictable behavior based on its historical values, its volatility is not overly influenced by its immediate past, except in the form of meanreversion in response to shocks. This could imply that the Thailand exchange rate is relatively stable, with inherent mechanisms that dampen the impact of large fluctuations over time, potentially reflecting effective monetary policy interventions or a stable macroeconomic environment. Table 6. ARCH results for Thailand Mean Equation Variable Coefficient Std. Error z-Statistic Prob. C 0.121671 0.101827 1.194875 0.2321 NEXC(-1) 0.976800 0.016288 59.96873 0.0000 Variance Equation C 0.030006 0.000581 51.68566 0.0000 RESD(-1)^2 -0.006750 0.000206 -32.74471 0.0000 F=486.17(0.0000) Source: Authors’ estimation results with Eviews 10. Table 7. ARCH results for Vietnam Mean Equation Variable Coefficient Std. Error z-Statistic Prob. C 0.373365 0.209037 1.786120 0.0741 NEXC(-1) 0.967368 0.014555 66.46428 0.0000 Variance Equation C 0.608470 0.023843 25.52022 0.0000 RESID(-1)^2 -0.015426 0.006147 -2.509593 0.0121 F=3002.3(0.0000) Source: Authors’ estimation results with Eviews 10. The ARCH model results for Vietnam on Table 7 shows 0.967368 coefficient of the real effective exchange rate Lagged one period. The coefficient is highly significant (z-statistic of 66.46428) and very close to one. This indicates that the current nexc value is almost perfectly predictive by its previous value, reflecting strong persistence or inertia in the exchange rate. This finding implies that once the Exchange rate behaviour in ASEAN countries – a sensitivity analysis 53 exchange rate reaches a certain level, it is likely to remain near that level in the subsequent period unless significant economic events occur. The variance equation is critical for understanding the volatility of real effective exchange rate. The constant term (C) is 0.608470, highly significant (z-statistic of 25.52022), indicating a substantive inherent volatility in the nexc. This reflects ongoing economic volatility in Vietnam potentially due to fluctuating commodity prices, political uncertainty, or external economic shocks. Table 8. ARCH results for Cambodia Mean Equation Variable Coefficient Std. Error z-Statistic Prob. C 0.095810 0.152964 0.626354 0.5311 NEXC(-1) 0.995107 0.008166 121.8662 0.0000 Variance Equation C 0.215612 0.005022 42.92938 0.0000 RESD(-1)^2 -0.013266 0.000340 -39.06671 0.0000 F-statistic 267.1(0.000) Source: Authors’ estimation results with Eviews 10. In Table 8, the ARCH results for Nigeria show Cambodia’s one-period lag of NEXC has the coefficient of 0.995107, which is significantly high with a z-statistic of 121.8662. This suggests that the current value of real effective exchange rate is almost entirely determined by its value in the previous period, highlighting strong continuity and minimal variation from historical levels in the short term. The intercept (C) in the mean equation is 0.095810, which is not statistically significant (p=0.5311). This suggests that there are no significant mean changes in the real effective exchange rate independent of its past values, reinforcing the idea that the exchange rate’s movements are predominantly influenced by its own inertia. In the variance equation, the constant term (C) is 0.215612, demonstrating a significant level of baseline volatility (zstatistic of 42.92938). This indicates a relatively high inherent volatility in the exchange rate, potentially reflecting the economic fluctuations, policy changes, or market uncertainties prevalent within Cambodia. David UMORU, Beauty IGBINOVIA, Mohammed Farid ALIYU 54 Table 9. ARCH results for Myanmar Mean Equation Variable Coefficient Std. Error z-Statistic Prob. C 0.433092 0.072750 5.953181 0.0000 NEXC(-1) 0.965299 0.005570 173.3036 0.0000 Variance Equation C 0.066289 0.003058 21.67919 0.0000 RESD(-1)^2 2.600196 0.909884 2.857724 0.0043 F=25461.3(0.000) Source: Authors’ estimation results with Eviews 10. Table 9 shows nominal exchange rate coefficient in Myanmar is 0.965299. The coefficient is highly significant with a z-statistic of 173.3036, showing that the nominal exchange rate is strongly influenced by its previous value. This coefficient nearly reaching one suggests that the exchange rate in Myanmar demonstrates substantial persistence, meaning that current nominal exchange rate values are almost a direct reflection of the previous period’s values. This level of persistence can indicate stability in the currency but might also reflect a rigidity that could impede rapid adjustment to new economic conditions. The variance equation reveals the model's approach to handling volatility. The constant term (C) is 0.066289, with a very high significance level (z-statistic of 21.67919), indicating a relatively moderate baseline volatility in the exchange rate. This finding suggests that while there are fluctuations, they are not excessively volatile under normal conditions, which is favorable for economic planning and foreign trade negotiations. Table 10. ARCH results for Malaysia Mean Equation Variable Coefficient Std. Error z-Statistic Prob. C 0.065025 0.025872 2.513322 0.0120 NEXC(-1) 0.986980 0.004727 208.7983 0.0000 Variance Equation C 0.010352 0.000206 50.13430 0.0000 RESD(-1)^2 -0.010740 0.000218 -49.29567 0.0000 F-statistic=1468.92(0.000) Source: Authors’ estimation results with Eviews 10. Exchange rate behaviour in ASEAN countries – a sensitivity analysis 55 From the ARCH in Table 10 above, the coefficient for one-period lag of real effective exchange rate is remarkably high at 0.986980, with an exceptionally significant z-statistic of 208.7983. This result indicates a very strong persistence in the exchange rate, suggesting that the current nexc is almost entirely predictable by its immediate past value. Such a high level of persistence reflects a stable exchange rate environment, where changes from one period to the next are minimal and largely anticipated. The constant term (C) in the mean equation is 0.065025, which is statistically significant (p=0.0120). This signifies that there is a small but consistent adjustment to the real effective exchange rate independent of its previous value, possibly reflecting systematic influences such as policy adjustments or long-term economic trends. In the variance equation, the constant term (C) is 0.010352, indicating a baseline level of volatility that is significant and consistent (z-statistic of 50.13430). This suggests that the underlying volatility of Malaysia’s nominal exchange rate is moderate but persistent, providing a foundational level of exchange rate fluctuation that might be attributed to regular market dynamics or external economic influences. Table 11. GARCH results for Indonesia Mean Equation Variable Coefficient Std. Error z-Statistic Prob. C 0.745908 0.567637 1.314058 0.1888 NEXC(-1) 0.982474 0.012734 77.15495 0.0000 Variance Equation C 1.661573 0.066105 25.13538 0.0000 RESD(-1)^2 -0.027529 0.002027 -13.57907 0.0000 GARCH(-1) 0.564221 0.003347 168.5564 0.0000 Source: Authors’ estimation results with Eviews 10. The results of the GARCH in Table 11 shows that Indonesia nominal exchange rate with a coefficient of 0.982474, which is highly significant (z-statistic of 77.15495) indicates a very strong autoregressive characteristic, where the current real effective exchange rate is almost completely determined by its value in the previous period. This high degree of persistence suggests that the nominal exchange rate in Indonesia changes gradually over time, providing a predictable pattern based on historical values. The variance equation in the GARCH model is designed to capture David UMORU, Beauty IGBINOVIA, Mohammed Farid ALIYU 56 the volatility of real effective exchange rate, including terms for both the RESID(- 1)^2 and the lagged conditional variance (GARCH(-1)). The coefficient for RESID(- 1)^2 is -0.027529, significant with a negative sign (z-statistic of -13.57907), indicating a mean-reversion of volatility. This implies that higher volatility in one period tends to be followed by reduced volatility, aligning with typical financial time series behavior where high-volatility events often stabilize over time. The GARCH(- 1) coefficient is 0.564221, significantly positive (z-statistic of 168.5564), highlighting the persistence of volatility. This means that if the exchange rate was volatile in the past, it is likely to remain volatile, pointing to a sustained impact of past volatility on current volatility levels. Table 12. GARCH results for Singapore Mean Equation Variable Coefficient Std. Error z-Statistic Prob. C 0.355531 0.176263 2.017048 0.0437 NEXC(-1) 0.970111 0.012316 78.76919 0.0000 Variance Equation C 0.086710 0.037361 2.320868 0.0203 RESD(-1)^2 -0.015018 0.001411 -10.64321 0.0000 GARCH(-1) 0.864199 0.063414 13.62782 0.0000 Source: Authors’ estimation results with Eviews 10. In Table 12, the ARCH and the GARCH coefficients for Singapore nominal exchange rate shows the significant volatility its economy has experienced due to various economic sanctions and commodity price fluctuations. The one-period lagged value of exchange rate is 0.970111, which is highly significant (z-statistic of 78.76919). This suggests that the exchange rate from one period strongly influences the rate in the subsequent period, indicating a gradual adjustment to new information and a tendency for the exchange rate to follow a smooth path over time. In the variance equation, the baseline volatility of the real effective exchange rate is captured by the constant term (C=0.086710), which is significant (p=0.0203). This suggests a foundational level of volatility inherent in the Singapore exchange rate market is influenced by economic policy uncertainty. The GARCH(-1) coefficient of 0.864199 Exchange rate behaviour in ASEAN countries – a sensitivity analysis 57 is significantly positive, indicating that past volatility has a strong predictive power on future volatility. This high persistence in volatility suggests that shocks to the exchange rate have long-lasting effects, which could exacerbate the impact of political events on the market stability. Table 13. GARCH results for Philippines Mean Equation Variable Coefficient Std. Error z-Statistic Prob. C 0.173271 0.192182 0.901599 0.3673 NEXC(-1) 0.979277 0.017725 55.24747 0.0000 Variance Equation C 0.037690 0.043979 0.857004 0.3914 RESD(-1)^2 -0.006025 0.001299 -4.639478 0.0000 GARCH(-1) 0.561294 0.512449 1.095317 0.2734 Source: Authors’ estimation results with Eviews 10. The GARCH results in Table 13 suggest that the current nominal exchange rate is heavily influenced by its immediate past value, reflecting a stable and predictable behaviour in the short term. This level of persistence is typical for economies with stable macroeconomic policies where the exchange rate adjusts gradually to changes. The intercept (C) of 0.173271, although not statistically significant (p=0.3673), indicates a minor constant impact on the real effective exchange rate that is not explained by its historical performance. The GARCH(-1) coefficient of 0.561294, although not significant (p=0.2734), suggests some degree of volatility persistence. This indicates that while past volatility influences current volatility, the effect is not as strong as it might be in more volatile or unstable economies. For India, the GARCH model’s shows how past exchange rate levels and their volatility affect current and future rates. The results of the GARCH result in Table 14 show that nominal exchange rate shows a high coefficient of 0.976900 with a significant z-statistic of 45.54392. This indicates that the nominal exchange rate is heavily influenced by its previous values, showcasing strong persistence. Such a high level of autoregression suggests that changes in the nominal exchange rate are gradual and predictable over short intervals, typical for an economy where exchange rates are managed within a policy framework David UMORU, Beauty IGBINOVIA, Mohammed Farid ALIYU 58 designed to maintain stability. The intercept (C) of 0.120945, though not significant (p=0.3652), indicates a minimal baseline impact on real effective exchange rate, which is not explained merely by its lagged values. This denotes underlying macroeconomic trends or policy shifts that exert a constant but subtle influence on the real effective exchange rate. The GARCH(-1) coefficient is 0.565968, which is not significant (p=0.1795). This suggests that past volatility has a moderate influence on future volatility, this relationship is not as strong as might be seen in more freely floating currencies. Table 14. GARCH results for Thailand Mean Equation Variable Coefficient Std. Error z-Statistic Prob. C 0.120945 0.133560 0.905547 0.3652 NEXC(-1) 0.976900 0.021450 45.54392 0.0000 Variance Equation C 0.016618 0.016093 1.032599 0.3018 RESD(-1)^2 -0.008602 0.000392 -21.95920 0.0000 GARCH(-1) 0.565968 0.421639 1.342305 0.1795 Source: Authors’ estimation results with Eviews 10. Table 15. GARCH results for Vietnam Mean Equation Variable Coefficient Std. Error z-Statistic Prob. C 0.355531 0.176263 2.017048 0.0437 NEXC(-1) 0.970111 0.012316 78.76919 0.0000 Variance Equation C 0.086710 0.037361 2.320868 0.0203 RESD(-1)^2 -0.015018 0.001411 -10.64321 0.0000 GARCH(-1) 0.864199 0.063414 13.62782 0.0000 Source: Authors’ estimation results with Eviews 10. The GARCH model results of Table 15 above for Vietnam's NEXC lag one period shows a coefficient of 0.970111 and a z-statistic of 78.76919, indicating extremely Exchange rate behaviour in ASEAN countries – a sensitivity analysis 59 high statistical significance. This strong autoregressive component suggests that the NEXC is highly persistent, with past values being a strong predictor of future rates. Such behavior indicates a stable but slowly adjusting exchange rate environment, where changes are gradual and not abrupt, likely reflecting Vietnam’s monetary policy aimed at stabilizing the exchange rate to avoid economic shocks. The variance equation of the GARCH model shows the constant term of 0.086710, significant at the 0.0203 level, indicates a foundational level of volatility inherent to the real effective exchange rate. This level of baseline volatility is influenced by external economic pressures, internal economic policy changes, or market perceptions affecting the South African economy. The GARCH(-1) term at 0.864199, significantly high (z-statistic of 13.62782), shows that past volatility has a strong and persistent influence on current volatility, indicating that volatility shocks tend to have long-lasting effects. Table 16. GARCH results for Cambodia Mean Equation Variable Coefficient Std. Error z-Statistic Prob. C 0.070835 0.157886 0.448649 0.6537 NEXC(-1) 0.996306 0.008428 118.2096 0.0000 Variance Equation C 0.074293 0.013774 5.393590 0.0000 RESD(-1)^2 -0.013525 0.002767 -4.888788 0.0000 GARCH(-1) 0.663125 0.062997 10.52629 0.0000 Source: Authors’ estimation results with Eviews 10. Table 16 reported GARCH model analysis of Nigeria’s nominal exchange rate. The significant autoregressive component in the mean equation, with nominal exchange rate showing a coefficient of 0.996306 and an exceptionally high z-statistic of 118.2096, demonstrates extreme persistence. This implies that the NEXC is almost perfectly predicted by its value in the preceding period, suggesting that the exchange rate evolves in a highly predictable manner with little deviation from its historical path. This can be indicative of a tightly managed exchange rate system where policy interventions ensure stability and reduce unpredictability in the forex market. The GARCH(-1) term at 0.663125 (significant with a z-statistic of 10.52629) indicates David UMORU, Beauty IGBINOVIA, Mohammed Farid ALIYU 60 that previous periods’ volatility has a substantial carryover effect into current volatility. This points to a scenario where shocks to the exchange rate can have prolonged impacts, influencing future volatility levels significantly. Table 17. GARCH results for Myanmar Mean Equation Variable Coefficient Std. Error z-Statistic Prob. C 0.193976 0.162216 1.195786 0.2318 NEXC(-1) 0.988742 0.009222 107.2133 0.0000 Variance Equation C 0.049900 0.041271 1.209101 0.2266 RESD(-1)^2 -0.012306 0.003290 -3.740106 0.0002 GARCH(-1) 0.800592 0.168963 4.738260 0.0000 Source: Authors’ estimation results with Eviews 10. Table 17 reported GARCH results and shows that the coefficient of nominal exchange rate lagged one-period is 0.988742. It is highly significant (z-statistic of 107.2133). This indicates that the NEXC is predominantly influenced by its value in the previous period, signifying strong continuity and predictability in exchange rate movements. Such high persistence often characterizes exchange rate systems where policy interventions aim to maintain stability or where economic conditions do not fluctuate dramatically in the short term. The GARCH(-1) term at 0.800592, with a significant z-statistic of 4.738260, reflects high volatility persistence. This finding suggests that once the nominal exchange rate exhibits volatility; such fluctuations are likely to continue into the future, emphasizing the impact of past volatility on current and future volatility levels. The results of the GARCH in Table 18 for Malaysia show that the coefficient of one-period lagged nominal exchange rate stands at 0.986980 with a strikingly high zstatistic of 136.7875, indicating an extremely high level of persistence. This suggests that the current nominal exchange rate is almost entirely dependent on its immediate past value, reflecting a stable and predictable exchange rate behavior over the short term. This characteristic is often indicative of an economy with effective regulatory oversight where exchange rates are managed to ensure gradual adjustments rather than abrupt swings, providing stability which is crucial for international trade and Exchange rate behaviour in ASEAN countries – a sensitivity analysis 67 have a persistent impact over time. The analysis suggests that the nominal exchange rate is characterized by a high level of persistence and potential heavy tails in its distribution, but recent shocks and their asymmetric impact are not playing a significant role in forecasting current volatility. Table 25. FIGARCH-DCC results for Myanmar Mean Equation Variable Coefficient Std. Error z-Statistic Prob. C 0.136302 0.002126 64.12275 0.0000 NEXC(-1) 0.993281 3.02E-05 32924.95 0.0000 Variance Equation C -0.187750 0.006239 -30.09357 0.0000 ARCH (-1)^2 -0.321892 0.041477 -7.760684 0.0000 GARCH (1) 0.579298 0.012131 47.75296 0.0000 d-coefficient 0.808820 0.002469 327.59011 0.0000 Source: Authors’ estimation results with Eviews 10 In Table 25 above, the coefficient 𝐶, representing the intercept in the variance equation, is highly significant with a near-zero probability (p-value), indicating a strong baseline level of volatility in the exchange rate movements. The lagged variable of nominal exchange rate RER(-1) with a very high z-statistic and a p-value of zero confirms the past nominal exchange rate values are extremely predictive of current NEXC values, signifying a strong autoregressive character in the exchange rate series. The coefficients ARCH term and its residual represent the short-run components of volatility, the ARCH term, and the measure of asymmetry in the impact of residuals (leverage effect), respectively. The negative sign of ARCH, along with its significant z-statistic, suggests a less than proportional reaction of volatility to past squared residuals, The coefficient of GARCH term and its high z-statistic and zero probability reveal that past volatility is highly predictive of future volatility, signifying a long memory characteristic of the volatility process. This could mean that volatility shocks to the nominal exchange rate are not only impactful in the short run but also persist over a longer period, influencing future volatility levels. Thus, in Myanmar, an emerging economy with active engagement in bitcoin and other cryptocurrencies, this volatility dynamic could be crucial. A higher persistence in volatility indicates that David UMORU, Beauty IGBINOVIA, Mohammed Farid ALIYU 68 external shocks, possibly including those related to bitcoin demand fluctuations, have long-lasting effects on the stability of the real effective exchange rate. Such findings reflects the impact decisions related to risk management, investment, and economic policy, as sustained volatility in exchange rates may affect international trade, investment flows, and economic stability. The FIGARCH for Malaysia are reported in Table 26. The FIGARCH model results for Malaysia’s nominal exchange rate signify a complex dynamic of volatility in exchange rate movements influenced by Bitcoin demand and other economic factors. The constant being negative and significant indicates that the baseline volatility is subject to mean reversion. The ARCH coefficient is also negative and significant; suggesting that past volatility has a strong shock impact on current volatility. This can be indicative of a market that reacts strongly to past volatility shocks, where the effects of large changes tend to be followed by further large changes, which may remain over long periods. This trait is particularly important for financial risk management as it points to a potentially higher risk environment for investors and policymakers. The GARCH coefficient being positive and significant suggests that volatility shocks are persistent over time, which aligns with the concept of long memory in volatility. This indicates that the effects of shocks to volatility do not decay quickly and that past periods of instability can influence future volatility over a long horizon. The FIGARCH model indicates a complex volatility structure in real effective exchange rate, likely driven by both external economic forces and internal policy measures. Table 26. FIGARCH-DCC results for Malaysia Mean Equation Variable Coefficient Std. Error z-Statistic Prob. C 0.004035 4.41E-05 91.56762 0.0000 NEXC(-1) 0.999141 1.23E-05 81386.46 0.0000 Variance Equation C -3.216785 0.157172 -20.46668 0.0000 ARCH(-1)^2 -0.406821 0.014469 -28.11642 0.0000 GARCH (-1) 0.250863 0.036499 6.873086 0.0000 d-coefficient 0.452890 0.001167 388.08054 0.0000 Source: Authors’ estimation results with Eviews 10. Exchange rate behaviour in ASEAN countries – a sensitivity analysis 69 Discussion When comparing our research findings with those from other studies, it is important to note that while Yuliadi et al. (2024) confirm low volatility in currency exchange rates due to Singapore’s managed floating exchange rate system, our research findings which show a significant persistence of shocks in the exchange rate of the Singaporean currency do not entirely agree with these findings. Our research findings indeed corroborated those of Yuliadi et al. (2024), who indicated that the government should keep an eye out for economic measures that would lessen the impact of ASEAN countries’ exchange rate volatility. In addition, our results are consistent with those of Rossanto et al. (2023), who found that currency exchange rate volatility in nations that adhered to the free float regime persisted even ten years after the financial crisis; Jonathan (2022) obtained evidence of an asymmetric response to exchange rate fluctuations that corroborates ours; Ain (2022) produced results from the wavelet power spectrum analysis that are consistent with our results for ASEAN nations. According to Ain’s (2022) research findings; four ASEAN countries namely, Singapore, Thailand, Malaysia, and Indonesia were found to have highly volatile currency rates. Thailand has little shortterm volatility and no increased long-term volatility, in contrast to the Philippines’ slight volatility. Our study’s outcome suggests that the real exchange rate in Philippines exhibits time-varying volatility that can be partially captured by its own past values. This research outcome is consistent with those of Abdul, Hsia Hua Sheng, and Natalia Diniz-Maganini (2021). These authors demonstrated that exchange rate volatility is asymmetric and time-varying by using empirical research based on the EGARCH (1, 1) specification fitted on monthly Asian currencies. The findings indicate that three currencies have indications of asymmetry in their conditional variance prior to the Asian crisis. With the exception of one, all of them displayed a noticeable increase in volatility and asymmetry effect. The findings from our study also corroborate those reported by Goda & Priewe (2020). In fact, the authors evaluated the primary causes of cyclical REER behavior in emerging market economies (EME). The research employed a sample size of fifteen emerging market economies from 1996 to 2016, covering several significant economic shocks such as the Asian crises of 1997–1998; the Russian crisis of 1998; David UMORU, Beauty IGBINOVIA, Mohammed Farid ALIYU 70 and the Turkish crisis of 2001. The 15 nations are divided into two categories by the study: industrial emerging market economies and commodities developing market economies. The dynamic panel fixed effects model was the econometric method employed to determine the factors influencing REER. Based on the findings, developing market economies that rely on commodities typically have higher REER volatility. 5. Conclusion The study evaluated the behavior of the nominal exchange rate in ASEAN countries, namely, Indonesia, Singapore, the Philippines, Thailand, Vietnam, Cambodia, Myanmar, and Malaysia. The current research aims to analyse the behavior of exchange rates in ASEAN countries using quantile regression sensitivity analysis. ARCH, GARCH, and FIGARCH modeling are also performed for robustness checks. This study made use of daily exchange rate data covering the period from January 1, 1990, to December 30, 2023, inclusive of weekends and holidays. The data were handled in two eras (before and after the financial crisis). The results are discussed based on each country. The models are well explained, and the results are presented in an organized manner. The GARCH and FIGARCH-DCC modeling techniques were executed to further gauge the impulsive performance of exchange rates of the aforementioned countries. Our research findings uphold asymmetry and persistence in the behavior of exchange rates of ASEAN currencies. In terms of comparative discussions, our research findings considerably align with the findings of other researchers, namely Yuliadi et al. (2024), Rossanto et al. (2023), Jonathan (2022), Ain (2022), and Natalia et al. (2021). In Indonesia, the FIGARCH results show high persistence in volatility, as indicated by a significant GARCH term, suggesting that shocks to the exchange rate have lasting effects. Singapore’s exchange rate exhibited significant persistence of shocks, indicating that the effects of shocks on the exchange rate are enduring. Philippines’s FIGARCH model illustrates a high level of volatility persistence. For Thailand, the volatility is characterized by long-memory and a considerable volatility Exchange rate behaviour in ASEAN countries – a sensitivity analysis 71 shock, which reflects the sensitivity of the exchange rate to market dynamics. In Vietnam, exchange rate volatility does not die off quickly, and this has the tendency to stimulate speculative chances. Cambodia exhibits weighty persistence in exchange rate volatility. Myanmar’s FIGARCH analysis shows permanent exchange rate shocks. Lastly, in Malaysia, volatility persistence in the nominal exchange rate is robust. 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