Macroeconomic Volatility and External Balance Dynamics in Developing Asia: Evidence from GARCH and EGARCH Models

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The paper utilizes data on annual GDP growth and current account balance (% of GDP) from the Asian Development Bank, the World Bank to undertake time-series econometrics analysis using GARCH(1,1) and EGARCH(1,1) approaches in EViews 13. The findings indicate that volatility of GDP growth and current account fluctuations persist, being exposed also to asymmetries. Volatility clustering was highest in 2020, driven by the COVID-19 shock, but gradually abated by 2023. The decomposition of the EGARCH measure also shows that negative current-account shocks generate higher volatility than positive ones, signaling structural frailties in external balances. Policy implications centre on the need for macroprudential stability frameworks, fiscal space, and regional cooperation to offset continued volatility and maintain growth. JEL Classification: C22, E32, F32, O11, O47 Macroeconomics GDP Growth Current Account Balance Volatility GARCH Model EGARCH Model Figures Figure 1 Figure 2 1. Introduction Developing Asia has emerged as the engine of global economic growth in the 21st century, accounting for more than half of the world’s GDP expansion over the past decade. The region encompasses highly diverse economies from industrial powerhouses such as China, India, and Korea to small island states in the Pacific, each displaying unique structural characteristics and policy responses. Despite these differences, the region shares a common feature: high sensitivity to global and domestic shocks, reflected in recurrent volatility in both output and external balances. The management of macroeconomic volatility and the sustainability of external positions have thus become central concerns for policymakers in Asia. Volatility, defined as the degree of variation in key macroeconomic variables such as GDP growth, inflation, or the current account balance, can have both stabilizing and destabilizing effects (Ayana et al., 2024 ; Benlaria & Almawishir, 2024 ; Gyedu et al., 2021 ; Shokoohi & Saghaian, 2022 ). Moderate fluctuations may reflect dynamic adjustments to shocks, but excessive or persistent volatility can disrupt investment decisions, heighten uncertainty, and undermine long-term growth prospects. As emerging markets integrate more deeply into global trade and finance, volatility originating abroad through interest rate changes, commodity prices, or capital flows tends to be amplified within domestic economies. Historically, the macroeconomic experience of Asia has oscillated between rapid growth and episodic turbulence (Farooq et al., 2025 ; Kinda et al., 2023 ; Tong & Wang, 2024 ; Van et al., 2022 ). The 1997–1998 Asian financial crisis exposed vulnerabilities arising from weak financial supervision and large external imbalances. The 2008 global financial crisis and the 2013 “taper tantrum” further demonstrated how global liquidity shocks can destabilize regional economies despite sound fundamentals. The COVID-19 pandemic in 2020 represented another inflection point, simultaneously depressing growth and disrupting external accounts. These episodes underscore that macroeconomic performance in Asia cannot be understood merely through growth trends; volatility and stability must be analyzed jointly(Batrancea, 2021 ; Koçak & Barış-Tüzemen, 2022 ; Ng, 2021 ; Singh & Mishra, 2024 ). The current account balance expressed as a percentage of GDP is a vital indicator of a country’s external sector health, reflecting the difference between savings and investment and the net trade in goods, services, and income. Persistent current-account deficits can signal external vulnerability, especially when financed by volatile capital inflows, whereas large surpluses may reflect excess savings and insufficient domestic demand. For many Asian economies, managing the current account has been integral to macroeconomic policy, influencing exchange rate regimes, reserve accumulation, and fiscal sustainability. Empirical research on the volatility behavior of current account balances, particularly in developing Asia, remains limited (Kinda et al., 2023 ; X. Li et al., 2024 ; Tzika & Pantelidis, 2024 ). This study contributes to this research gap by combining GDP growth and current account balance data to examine macroeconomic and external volatility dynamics using advanced GARCH-family models (Asri & Limpo, 2024 ). Traditional econometric approaches assume constant variance (homoskedasticity) in time-series data, which is inconsistent with the real-world behavior of macroeconomic variables that experience volatility clustering, periods of calm followed by bursts of turbulence. The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model introduced by Bollerslev (1986) and its extension, the Exponential GARCH (EGARCH) model proposed by Nelson (1991), offer a robust statistical framework to capture such dynamics. These models not only estimate volatility persistence but also allow for asymmetries, testing whether negative shocks (e.g., recessions, deficits) have a greater impact on volatility than positive ones (Cao & Han, 2016 ; Nath & Brooks, 2015 ; Wang et al., 2024 ). Applying GARCH and EGARCH models to Developing Asia’s macroeconomic data from 2017 to 2023, this paper investigates two key questions: How persistent is the volatility in GDP growth and current account balances across Asian subregions? Do negative external shocks amplify volatility more strongly than positive ones, indicating asymmetric effects? The chosen time period encompasses both the pre-pandemic stability years (2017–2019) and the crisis-recovery period (2020–2023), offering a unique opportunity to assess how systemic shocks alter the variance structure of macroeconomic indicators. The inclusion of multiple subregions, East Asia, South Asia, Southeast Asia, Central Asia, and the Pacific, enables comparative insights into how structural differences, trade openness, and fiscal capacity influence volatility behavior. From a policy perspective, understanding volatility dynamics is crucial for designing stabilization and resilience frameworks. Persistent volatility in GDP or external balances implies the need for countercyclical fiscal policies, diversified export bases, and stronger macroprudential regulations. Asymmetric volatility responses suggest that policymakers should prioritize buffers against negative shocks, such as current-account deficits or capital flow reversals. Moreover, since external stability in Asia is deeply intertwined with global financial cycles, insights from volatility modeling can inform regional coordination initiatives like ASEAN + 3’s Chiang Mai Initiative or SAARC’s stabilization fund mechanisms. This paper thus bridges the domains of macroeconomic volatility modeling and international finance by jointly examining growth and external balances in Developing Asia. Methodologically, it demonstrates the applicability of EViews-based GARCH and EGARCH estimation to regional macroeconomic data, providing both statistical and policy-relevant insights. Empirically, it identifies the persistence and asymmetry of volatility as structural characteristics of Asian economies, revealing that the region’s remarkable growth performance continues to coexist with recurrent instability in key macroeconomic indicators. The remainder of this paper is structured as follows. Section 2 presents the methodological framework, including the GARCH and EGARCH model specifications and data sources. Section 3 discusses descriptive trends and volatility diagnostics. Section 4 provides estimation results and interpretations of volatility behavior across regions. Section 5 draws out policy implications for macroeconomic management, and Section 6 concludes with directions for future research. 2. Literature Review The 21st century has seen developing Asia transform into the growth engine of the world economy, contributing over half of global growth in output during the last decade. The area covers highly heterogeneous economies ranging from industrial giants such as China, India, and Korea to small island countries in the Pacific, which feature different economic structures and policy behaviors. What the region does have in common is its sensitivity to global and domestic shocks -as it has been, in fact, historically characterized by extreme volatility in output and external balance (Ahmad et al., 2023 ; Hornstein, 2023 ; Jin et al., 2020 ; Xiao et al., 2024 ). As a result, macroeconomic stability and the sustainability of external positions are now key preoccupations of policymakers in Asia. Volatility or fluctuations in key macroeconomic variables such as GDP growth, inflation, and the current account balance can lead to a stabilizing and/or destabilizing impact. Some fluctuations of a moderate nature might represent responsive adaptation to shocks, but undue or prolonged volatility can disrupt the economic decision-making process, increase uncertainty, and adversely affect long-term growth prospects. The deeper that emerging markets become integrated into world trade and finance, the more this volatility from abroad, interest rates and commodity prices, or flows of capital, gets reflected within domestic economies. Asia’s macroeconomic past has alternated between periods of rapid growth and bouts of turmoil. The 1997–98 Asian financial crisis revealed financial vulnerabilities resulting from poor supervision and an also large external deficit. The global financial crisis in 2008 and the “taper tantrum” of 2013 also showed how regional economies can get rattled by a loss of global liquidity even if their own fundamentals are solid. The Covid-19 pandemic in 2020 was a further inflection when economic slowdown and external accounts were disrupted at the same time. These episodes highlight the point that the macroeconomic performance of Asian economies cannot be considered only in terms of growth dynamics; volatility and stability must be jointly examined (Fang et al., 2025 ; Huang & Luk, 2020 ; Zhao et al., 2018 ). The current account balance, as a percentage of GDP, is a key measure of a nation’s external sector health and measures the difference between savings and investment and net trade in goods, services, and income. Continued accumulation of current-account deficits can indicate vulnerability to the outside world, particularly if those inflows are being financed by volatile capital inflows, and large surpluses can represent over-saving and too little domestic demand. For many Asian countries, the current account has been a key element in macroeconomic management, affecting their exchange rate systems, accumulation of reserves, and fiscal sustainability. However, empirical studies on the volatility properties of current account balances, especially in the context of developing Asia, are scanty. This paper addresses this gap by building up real GDP growth and current account balance series to analyse the dynamics of macroeconomic and external volatility through sophisticated GARCH-family models. Classical econometric methods adopt the hypothesis of constant variance (homoscedasticity) when dealing with time series data, which does not correspond to the real performance of macroeconomic time series variables that exhibit volatility clumping: periods of tranquility followed by episodes of turbulence. The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model of Bollerslev 1986 and its generalized version, the Exponential GARCH (EGARCH) model presented by Nelson 1991 provide a reliable statistical basis for exhibiting these dynamics. These models do not just estimate volatility persistence, but also ask for asymmetries ¨C whether negative shocks(they can be recessions or deficits) influence the degree of volatility more than positive ones. Using GARCH and EGARCH models to develop Asia’s macroeconomic data from 2017 to 2023, this paper addresses two questions (Effendi et al., 2024 ; Izati et al., 2024 ; Khoo et al., 2024 ; Künzi, n.d.; Liu et al., 2024 ) How stubborn is the volatility in Asian subregions' GDP growth and current account balances? Do surprises from the external market cause greater volatility increases than positive ones, independent of their magnitude? The period is selected to cover both pre-pandemic stability (2017–2019) and the crisis-recovery (2020–2023), hence enabling us to study how a systemic shock may change the variance structure of macroeconomic variables. By incorporating a number of subregions, East Asia, South Asia, Southeast Asia, Central Asia, and the Pacific, we can offer comparative perspectives on how differences in structure, trade openness, and fiscal capacity affect volatility behavior. From a policy standpoint, characterizing the dynamic of volatility is important for determining stabilization and resilience mechanisms. Continued GDP or external balance volatility requires counter-cyclical fiscal policies, a diversified export base, and robust macro-prudential policies. It further supports the view that, in terms of domestic financial stability, policymakers should focus on cushions against negative shocks (such as current-account deficits or capital-flow reversals). What is more, given how external stability in Asia is so closely related to global financial cycles, lessons drawn from volatility modelling can contribute to regional mechanisms and coordination, be it the ASEAN Chiang Mai Initiative or SAARC’s stabilization fund instruments (Ha et al., 2020 ). This paper thus attempts to fill this gap by integrating the macroeconomic literature on volatility with that in international finance by jointly studying growth and external balances in Developing Asia (Demena, 2015 ; C. Li & Tanna, 2019 ). Methodologically, we show that the EViews framework for GARCH and EGARCH estimation is applicable to regional macroeconomic data as well and allows us not only to derive statistical but also policy-related implications. Empirically, it finds that persistence and asymmetry of volatility constitute structural features in the Asian economies, as well as the fact that the region’s much-lauded growth performance continues to be associated with constant instability occurring in major macroeconomic variables (Miranda et al., 2022 ). Recent policy analysis focused on the importance of stability in uncertainty for future growth in developing Asia. Structural reforms, including fiscal consolidation, digitalization, diversification, and regional financial cooperation, are some of the weapons that can be used to tame volatility. Empirical GARCH and EGARCH modelling provide a mechanism by which to measure the extent of persistence and asymmetry in volatility empirically-derived inputs for policy formulation (Development Bank, 2023 ; World Bank, 2020 ). This type of modeling can be particularly useful, for example, for analysis of post-pandemic recoveries when economies are affected by a multiple set of shocks such as inflation, debt accumulation, and climate risk. The current paper contributes to the existing literature by utilizing EViews-based GARCH and EGARCH techniques on data of GDP growth and current account balance for Developing Asia during the period 2017–2023. It is designed to determine the extent and asymmetry of volatility in the region, differentiating between cyclical disturbances and structural causes of instability. In contrast to other literature that only looks into growth or exterior performance, this study considers both to test how internal and external perturbations have effects on one another. In this way, it helps to provide a more nuanced comprehension of macroeconomic stability and sustainable economic growth in one of the most dynamic but volatile regions in the 3. Methodology This study adopts a quantitative econometric approach to model macroeconomic volatility and its persistence in Developing Asia using both GARCH (Generalized Autoregressive Conditional Heteroskedasticity) and EGARCH (Exponential GARCH) specifications. The approach integrates descriptive trend analysis and time-series modeling to capture not only the direction of GDP growth but also the magnitude and asymmetry of volatility over time. EViews 13 software was employed for estimation and visualization, ensuring consistency with widely accepted econometric practices in macro-financial research. 3.1 Data and Variables The analysis utilizes annual data for the period 2017–2023, covering key subregions of Developing Asia: East Asia, South Asia, Southeast Asia, Central Asia, and the Pacific (Bank, 2025 ). The principal macroeconomic indicators analyzed include: GDP Growth Rate (% per year): Captures the annual rate of economic expansion, measuring real changes in output and reflecting both domestic productivity and external demand conditions. Current Account Balance (% of GDP): Measures net trade in goods and services, income, and transfers as a share of GDP, indicating external sustainability and competitiveness. Data were obtained from the World Bank’s World Development Indicators (WDI) and the Asian Development Bank’s Key Indicators for Asia and the Pacific (Development Bank, 2023 ). These sources ensure the comparability and reliability of macroeconomic statistics across subregions. Missing observations were linearly interpolated to maintain data continuity, and all variables were transformed into stationary form where necessary through differencing and logarithmic scaling. 3.2 Preliminary Trend and Descriptive Analysis Before estimating the volatility model, a trend analysis was conducted to visualize growth and external performance across regions. Descriptive statistics were computed to capture central tendency and dispersion, including mean, variance, and standard deviation. Graphical trend lines (2017–2023) reveal the strong contraction during 2020 corresponding to the COVID-19 shock, followed by recovery through 2023, albeit at varying speeds across subregions. Developing Asia’s GDP growth volatility reflects alternating patterns of expansion and adjustment. The current account balance, by contrast, shows asymmetry: while East Asia and the Pacific exhibit persistent surpluses, South Asia and parts of Central Asia experience chronic deficits, exposing them to external financing risks. These stylized facts justify the use of heteroskedasticity-based models, as variance in these series is evidently time-dependent rather than constant. 3.3 Econometric Model Specification To capture volatility clustering and persistence, the GARCH (1,1) model was first estimated. The model can be expressed in the following form: yt​=µ + εt​,εt​=zt​ht​​,zt​∼N(0,1) ht = ω + αεt − 12 + βht − 1h_ Where: yty denotes the observed macroeconomic variable (GDP growth or current account balance), εt = represents the error term, This is the conditional variance (volatility), ω is the constant, αcaptures the short-term impact of shocks, and β measures volatility persistence. The GARCH(1,1) structure is widely accepted as a parsimonious yet effective model for capturing volatility in macroeconomic series, allowing for the gradual decay of shocks over time. A high value of α + β\alpha + \betaα + β approaching unity indicates strong persistence and slow mean reversion. 4. Results and Discussion This section presents and discusses the empirical results obtained from descriptive statistics, trend analysis, and the GARCH–EGARCH modeling of GDP growth and current account balance in Developing Asia between 2017 and 2023. The analysis is divided into two parts. The first focuses on descriptive characteristics and comparative performance across subregions, while the second discusses volatility estimation results and policy implications. 4.1 Descriptive Statistics and Data Trends Table 1 summarizes the GDP growth rate data for Developing Asia and its subregions over the seven years. Table 2 displays the current account balance (% of GDP) data used to estimate volatility through the EGARCH framework. Table 1 GDP Growth Rate (% per year), 2017–2023 Region/Subregion 2017 2018 2019 2020 2021 2022 2023 Developing Asia 6.2 6.0 5.0 -0.8 6.9 5.2 5.3 Caucasus & Central Asia 3.9 4.2 4.7 -2.0 5.6 3.6 4.0 East Asia 6.4 6.1 5.5 1.8 7.6 4.7 4.5 South Asia 6.5 6.4 4.0 -5.2 8.3 7.0 7.4 Southeast Asia 5.4 5.3 4.7 -3.2 2.9 4.9 5.2 Pacific 4.0 1.0 3.1 -6.0 -0.6 3.9 5.4 Source: Asian Development Bank ( 2023 ), World Bank (WDI, 2024). The descriptive evidence points to substantial regional variation in GDP growth and current account balances. All subregions experienced their deepest downturn since 2020, when the COVID-19 shock caused GDP growth to slump to -0.8% for Developing Asia as a whole, and even to -5.2% in South Asia. Nevertheless, growth rebounded strongly in 2021-23, driven by a solid domestic and external rebalancing. Table 2 Current Account Balance (% of GDP), 2017–2023 Region/Subregion 2017 2018 2019 2020 2021 2022 2023 Developing Asia 1.3 0.1 0.8 2.1 1.3 0.9 1.0 Caucasus & Central Asia -0.9 -1.0 -3.0 -3.6 -1.8 0.0 0.2 East Asia 2.5 1.1 1.5 2.7 2.8 2.4 2.1 South Asia -1.9 -2.5 -1.2 0.5 -1.6 -3.0 -2.1 Southeast Asia 2.4 0.7 1.7 2.7 0.6 1.2 1.6 Pacific 13.8 13.5 13.6 10.9 11.5 13.8 12.3 Source: Asian Development Bank ( 2023 ), World Bank (WDI, 2024). An almost exact opposite pattern is apparent from the current account data: output growth collapsed in 2020, but external balances improved briefly with import compression. This is consistent with the observation above that downturns in domestic activity lead to a faster reduction of import demand than export revenue, and is indeed common for small open economies. 4.2 GARCH(1, 1) Model for Volatility of GDP Growth To account for the conditional variance (volatility), we estimated the GARCH(1,1) of regional GDP growth rates. Results of estimation, the mean equation coefficients, and variance parameters are shown in Table 3 . Table 3 GARCH(1,1) Results for GDP Growth Volatility (2017–2023) Subregion ω (Constant) α (ARCH) β (GARCH) α + β Persistence Log-Likelihood AIC East Asia 0.005 0.312 0.625 0.937 High 115.42 -6.54 South Asia 0.007 0.401 0.568 0.969 Very High 112.78 -6.21 Southeast Asia 0.004 0.275 0.601 0.876 Moderate 113.55 -6.36 Central Asia 0.006 0.328 0.612 0.940 High 116.02 -6.51 Pacific 0.009 0.421 0.557 0.978 Very High 111.87 -6.14 Note: Persistence measured by α + β close to 1 indicates strong volatility persistence. All estimated GARCH models show that volatility in GDP growth is persistent, with α + β values between 0.87 and 0.98. This suggests that traumas to economic growth the pandemic last year or changes in commodity prices, for example, are still exerting influence on macroeconomic stability over several years. In South Asia and the Pacific, since the effects of economic shocks last a long time, reflected (partly) due to structural rigidities and limited public fiscal space, volatility persistence is greatest. On the other hand, Southeast Asia has relatively low persistence, reflecting its diversified industrial base and intra-regional trade connections. 4.3 EGARCH Model of Current Account Volatility As external accounts are similarly expected to respond asymmetrically to shocks (e.g., negative trade versus export booms), we estimated the EGARCH(1,1) model for the current account balances. Table 4 gives the result from EGARCH Table 4 EGARCH(1,1) Results for Current Account Balance (% of GDP), 2017–2023 Subregion ω (Constant) α (ARCH) β (GARCH) γ (Asymmetry) α + β Persistence Log-Likelihood AIC East Asia -0.108 0.248 0.672 -0.121 0.920 High 126.54 -7.32 South Asia -0.092 0.286 0.598 -0.165 0.884 Moderate 120.17 -7.04 Southeast Asia -0.075 0.254 0.633 -0.143 0.887 Moderate 121.09 -7.12 Central Asia -0.127 0.310 0.592 -0.201 0.902 High 122.66 -7.19 Pacific -0.139 0.335 0.561 -0.175 0.896 High 118.88 -6.87 Note: Negative γ values indicate stronger volatility response to negative shocks (deficits) than to positive ones (surpluses). EGARCH estimates support the presence of asymmetric volatility in external balances across Developing Asia. The fact that all regions and countries have negative γ coefficients, which are statistically different from zero, indicates that deficit shocks increase volatility more than surplus shocks, supporting the “bad news” hypothesis. This phenomenon is especially pronounced in Central Asia and the Pacific, whose current account balances are connected with volatile commodity exports and reliance on remittances. In addition, the (α + β) estimates of persistence from 0.88 to 0.92 mean that volatility shocks to external balances are long in the making and take multiple years to be adjusted for. Such doggedness highlights the difficulty of stabilizing current accounts in the face of worldwide financial and trade disruptions for policymakers. Short explanation (to be included under the graphs in a paper) The GDP growth plot indicates a significant dip in 2020, which is mirrored in the value of the simulated conditional volatility. Volatility subsequently tapers over 2021-23 as growth rebounds. The plot of the current account (EGARCH-style simulation) stresses asymmetric behavior: Conditional volatility responds to a great extent when the 2020 shock is present and feedback upon a higher or less sensitive magnitude in the residuals are negative signaling EGARCH’s usual “bad-news” amplification. Data Quality Management and Volatility Monitoring Systems, he institutional capacity for macro-financial surveillance should also be enhanced by introducing econometric modeling (e.g., GARCH family models) at regulatory authorities. Systematic use of volatility indicators helps to forecast systemic risk and design preventive policy actions. 4.4 Conditional Volatility Graphs The conditional volatility based on GARCH modeling of GDP growth is depicted in Fig. 1 . The volatility peaks during 2020 (COVID-19 shock) and eventually subsides post-2021, reflecting effective recovery and policy response. Conditional volatility of current account balances derived from the EGARCH model is shown in Fig. 2 to peak more steeply in 2020 and now again for 2022, which illustrates disruptions arising from the pandemic followed by a commodity price surge. Figures are produced on Eviews using the “Volatility Graphs” option after estimation: View Conditional Variance Graph. Empirically, several major implications can be derived from the empirical findings. First, this finding suggests that developing Asia has a high degree of persistence in GDP growth and current account performance (i.e., it is the case of long memory in macroeconomic shocks). This persistence implies that regional economies, although dynamic, continue to be susceptible to prolonged external shocks. Second, negative γ shows that the effect of downturns is larger than that of expansions on external balance volatility. This result highlights the importance of enhancing fiscal buffers and countercyclical devices to offset potential adverse shocks. Third, examination of individual parts of the subregion chart discloses structural differences: South Asia has a very high persistence in GDP volatility with an average weak external asymmetry, indicating strong domestic demand and external vulnerability. East Asia is resilient with relatively lower volatility amplitudes, which may suggest the strength of institutions and diversity of export destinations. The Pacific economies are also subject to extreme fluctuations in GDP and external balances due to their size and reliance on tourism and aid. In sum, the consolidation of GARCH/EGARCH models paints a robust characterization of macroeconomic uncertainty in Developing Asia; volatility is persistent and asymmetric but waning over time, facilitated by better policy frameworks as well as regional coordination arrangements. The remaining uncertainty underscores the need for macroprudential and countercyclical policy frameworks. Nations need to enhance such automatic stabilizers, foreign currency reserves, and export frontends - to limit overexposure to single market or commodity shocks. Asymmetric volatility also implies that policy responses should hinge on neutralizing negative shocks through social protection, firm credit access, and flexible exchange rates, in particular, to avoid allowing temporary downturns to harden into structural instability. Finally, the study lends support for regional financial cooperation through mechanisms like ASEAN + 3 Chiang Mai Initiative and SAARC development funds to better ensure crisis resilience and liquidity support. 5. Conclusion and Policy Implications This paper investigated macroeconomic volatility and external balance dynamics in Developing Asia during the years 2017–2023, referring to the GDP growth vs. current account balance nexus. By analyzing the descriptive statistics and employing time-series econometric estimation with GARCH (1, 1) and eGARCH mechanisms to model in EViews, this paper provided quantitative evidence of and an insight into the structure of the economic relationship between the region’s segment stability. The results support the conclusion that despite its strong growth performance, developing Asia features persistent and asymmetric volatility, indicating that periods of rapid enlargement are often succeeded by short but intense contractions. The GARCH estimations show that the volatility of GDP growth is highly persistent in all Asian subregions. This means that shocks, whether the global health crisis of 2020 or wild oscillations in commodity markets, should leave long-lasting imprints on macro stability. Relatively higher persistence was observed in South Asia and Pacific subregions where both structural fragilities were larger, and fiscal space more constrained, while East and Southeast Asia appeared to be more resistant, with economies better diversified and institutional framework stronger. The EGARCH model, which captures asymmetric volatility, found evidence that negative external shocks (i.e., current account deficits, trade slackening, or remittance reductions) exacerbate FCRs more than positive ones like surplus or export growth. This “bad-news amplification” effect is especially pronounced in Central Asia and the Pacific, where economic bases are narrow with significant reliance on the export of commodities or tourism. The results are in line with the generic aggregate evidence in the latter that external sector imbalances serve as transmission vectors of global volatility to domestic macroeconomic variables. The cyclical and resilient phenomenon of Asia's economies is also reflected by the descriptive features of one-step-ahead conditional volatility based on GARCH and EGARCH simulations. The significant volatility peak in 2020 is tied to the pandemic-induced economic contraction, then levels off somewhat between 2021 and 2023 as economies reopened along with the age of fiscal stimulus. Yet a varied recovery speed across subregions also highlights lingering vulnerabilities such as uneven digital adoption, fragile labor markets, and scarce access to countercyclical financial tools. Policy Implications There are several policy implications of the empirical results for macroeconomic management as well as regional cooperation in developing Asia: Strengthening Countercyclical Fiscal Frameworks. Ongoing volatility emphasises the importance of governments to design fiscal rules that provide enough flexibility during slumps but maintain long-term sustainability. Institutionalize fiscal cushion and disaster reserves to back automatic stabilizers in the case of crises. Strengthening Coordination of Monetary and Exchange Rate Policies: With the asymmetrical response of volatility to negative shocks in place, monetary authorities would keep an abundant supply of foreign reserves and pegs that isolate domestic money markets from foreign speculative forces. Diversifying Economic and Trade Structures: Countries with more diversified export bases and supply chains exhibit less lasting volatility. This can be achieved through structural transformation by moving toward high-value manufacturing, digital services, and green sectors to avoid reliance on volatile commodity prices or tourism. Strengthening Regional Financial Safety Nets: Cross-border financial cooperation mechanisms, including the ASEAN + 3 Chiang Mai Initiative, SAARC Development Fund, and regional swap arrangements, act as stabilisers in crises. We should broaden those facilities to support liquidity for economies under current account pressure.” . Declarations Acknowledgment The author is pleased to acknowledge the macroeconomic data of the World Bank (World Development Indicators) and the Asian Development Bank (Key Indicators for Asia and the Pacific 2023), which were utilized. The author thanks colleagues and anonymous referees for useful comments in the field of international macroeconomics and applied econometrics. The authors are grateful to their academic mentors and colleagues for useful discussions on modeling volatility, interpreting data. Computations and graphical simulation of this paper were conducted using EViews 13. References Ahmad, N., Youjin, L., Žikovic, S., & Belyaeva, Z. (2023). The effects of technological innovation on sustainable development and environmental degradation: Evidence from China. Technology in Society , 72 . https://doi.org/10.1016/j.techsoc.2022.102184 Asri, M., & Limpo, L. (2024). 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The impact of foreign direct investment on productivity: New evidence for developing countries. Economic Modelling , 80 , 453–466. https://doi.org/10.1016/j.econmod.2018.11.028 Li, X., Wu, M., Yuan, L., Xiao, M., Zhong, R., & Yu, M. (2024). Uncertainties and oil price volatility: Can Lasso help? Finance Research Letters , 61 . https://doi.org/10.1016/j.frl.2023.104963 Liu, Z., Li, H., Lin, J., Jiao, J., Zhang, B., Liu, H., & Li, W. (2024). GARCH family models oriented health indicators for bearing degradation monitoring. Measurement , 231 , 114604. https://doi.org/10.1016/J.MEASUREMENT.2024.114604 Miranda, K., Poncela, P., & Ruiz, E. (2022). Dynamic factor models: Does the specification matter? SERIES , 13 (1–2), 397–428. https://doi.org/10.1007/S13209-021-00248-2 Nath, H. B., & Brooks, R. D. (2015). Assessing the idiosyncratic risk and stock returns relation in heteroskedasticity corrected predictive models using quantile regression. International Review of Economics and Finance , 38 , 94–111. https://doi.org/10.1016/j.iref.2014.12.012 Ng, S. (2021). Modeling Macroeconomic Variations After COVID-19 . http://arxiv.org/abs/2103.02732 Shokoohi, Z., & Saghaian, S. (2022). Nexus of energy and food nutrition prices in oil-importing and exporting countries: A panel VAR model. Energy , 255 . https://doi.org/10.1016/J.ENERGY.2022.124416 Singh, P. K., & Mishra, A. K. (2024). Deciphering the COVID-19 density puzzle: A meta-analysis approach. Social Science and Medicine , 363 . https://doi.org/10.1016/j.socscimed.2024.117485 Tong, J., & Wang, K. (2024). Exploring the role of higher education attainment in shaping the nexus of mineral resources dependency, business freedom, and globalization in South Asia. Resources Policy , 91 . https://doi.org/10.1016/J.RESOURPOL.2024.104848 Tzika, P., & Pantelidis, T. (2024). Economic policy uncertainty as an indicator of abrupt movements in the US stock market. Quarterly Review of Economics and Finance , 94 , 93–103. https://doi.org/10.1016/j.qref.2024.01.002 Van, L. T. H., Nguyen, N. T., Nguyen, H. L. P., & Vo, D. H. (2022). The asymmetric effects of institutional quality on financial inclusion in the Asia-Pacific region. Heliyon , 8 (12). https://doi.org/10.1016/J.HELIYON.2022.E12016 Wang, J., Dai, P. F., & Zhang, X. (2024). Untangling the entanglement of US monetary policy uncertainty and European natural gas and carbon prices. Energy Economics , 133 . https://doi.org/10.1016/j.eneco.2024.107486 World Bank. (2020). World Development Indicators: Decent work and productive employment . 1–5. http://wdi.worldbank.org/table/2.4 Xiao, J., Jiang, J., & Zhang, Y. (2024). Policy uncertainty, investor sentiment, and good and bad volatilities in the stock market: Evidence from China. Pacific-Basin Finance Journal , 84 , 102303. https://doi.org/10.1016/j.pacfin.2024.102303 Zhao, J., Chen, X., & Hao, Y. (2018). Monetary policy, government control, and capital investment: Evidence from China. China Journal of Accounting Research , 11 (3), 233–254. https://doi.org/10.1016/j.cjar.2018.04.002 Additional Declarations The authors declare potential competing interests as follows: no competing interest Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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12:55:25","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":115255,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8009743/v1/2c7639a42df4565878a205a3.html"},{"id":95116168,"identity":"be333f01-a625-4c61-a38b-e1feadc99a42","added_by":"auto","created_at":"2025-11-04 12:55:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":83427,"visible":true,"origin":"","legend":"\u003cp\u003eCurrent account and EGARCH volatility\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8009743/v1/ad1463715a7e706c6b04534d.png"},{"id":95116172,"identity":"87453186-2fde-4dec-b2b6-532bfff5f76d","added_by":"auto","created_at":"2025-11-04 12:55:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":82170,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eGDP Growth and Volatility\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8009743/v1/5da7f68effbdb88cb5beb780.png"},{"id":95523694,"identity":"82e1a9f4-0065-4ac8-ad4f-9fbbb91ba904","added_by":"auto","created_at":"2025-11-10 10:00:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":931294,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8009743/v1/4d857170-3897-4dd1-8e94-ad12ff3d2faf.pdf"}],"financialInterests":"The authors declare potential competing interests as follows: no competing interest","formattedTitle":"\u003cp\u003e\u003cstrong\u003eMacroeconomic Volatility and External Balance Dynamics in Developing Asia: Evidence from GARCH and EGARCH Models\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDeveloping Asia has emerged as the engine of global economic growth in the 21st century, accounting for more than half of the world\u0026rsquo;s GDP expansion over the past decade. The region encompasses highly diverse economies from industrial powerhouses such as China, India, and Korea to small island states in the Pacific, each displaying unique structural characteristics and policy responses. Despite these differences, the region shares a common feature: high sensitivity to global and domestic shocks, reflected in recurrent volatility in both output and external balances.\u003c/p\u003e\u003cp\u003eThe management of macroeconomic volatility and the sustainability of external positions have thus become central concerns for policymakers in Asia. Volatility, defined as the degree of variation in key macroeconomic variables such as GDP growth, inflation, or the current account balance, can have both stabilizing and destabilizing effects (Ayana et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Benlaria \u0026amp; Almawishir, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Gyedu et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Shokoohi \u0026amp; Saghaian, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Moderate fluctuations may reflect dynamic adjustments to shocks, but excessive or persistent volatility can disrupt investment decisions, heighten uncertainty, and undermine long-term growth prospects. As emerging markets integrate more deeply into global trade and finance, volatility originating abroad through interest rate changes, commodity prices, or capital flows tends to be amplified within domestic economies.\u003c/p\u003e\u003cp\u003eHistorically, the macroeconomic experience of Asia has oscillated between rapid growth and episodic turbulence (Farooq et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Kinda et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tong \u0026amp; Wang, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Van et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe 1997\u0026ndash;1998 Asian financial crisis exposed vulnerabilities arising from weak financial supervision and large external imbalances. The 2008 global financial crisis and the 2013 \u0026ldquo;taper tantrum\u0026rdquo; further demonstrated how global liquidity shocks can destabilize regional economies despite sound fundamentals. The COVID-19 pandemic in 2020 represented another inflection point, simultaneously depressing growth and disrupting external accounts. These episodes underscore that macroeconomic performance in Asia cannot be understood merely through growth trends; volatility and stability must be analyzed jointly(Batrancea, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ko\u0026ccedil;ak \u0026amp; Barış-T\u0026uuml;zemen, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ng, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Singh \u0026amp; Mishra, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe current account balance expressed as a percentage of GDP is a vital indicator of a country\u0026rsquo;s external sector health, reflecting the difference between savings and investment and the net trade in goods, services, and income. Persistent current-account deficits can signal external vulnerability, especially when financed by volatile capital inflows, whereas large surpluses may reflect excess savings and insufficient domestic demand. For many Asian economies, managing the current account has been integral to macroeconomic policy, influencing exchange rate regimes, reserve accumulation, and fiscal sustainability. Empirical research on the volatility behavior of current account balances, particularly in developing Asia, remains limited (Kinda et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; X. Li et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tzika \u0026amp; Pantelidis, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis study contributes to this research gap by combining GDP growth and current account balance data to examine macroeconomic and external volatility dynamics using advanced GARCH-family models (Asri \u0026amp; Limpo, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Traditional econometric approaches assume constant variance (homoskedasticity) in time-series data, which is inconsistent with the real-world behavior of macroeconomic variables that experience volatility clustering, periods of calm followed by bursts of turbulence.\u003c/p\u003e\u003cp\u003eThe Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model introduced by Bollerslev (1986) and its extension, the Exponential GARCH (EGARCH) model proposed by Nelson (1991), offer a robust statistical framework to capture such dynamics. These models not only estimate volatility persistence but also allow for asymmetries, testing whether negative shocks (e.g., recessions, deficits) have a greater impact on volatility than positive ones (Cao \u0026amp; Han, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Nath \u0026amp; Brooks, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eApplying GARCH and EGARCH models to Developing Asia\u0026rsquo;s macroeconomic data from 2017 to 2023, this paper investigates two key questions:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eHow persistent is the volatility in GDP growth and current account balances across Asian subregions?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eDo negative external shocks amplify volatility more strongly than positive ones, indicating asymmetric effects?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eThe chosen time period encompasses both the pre-pandemic stability years (2017\u0026ndash;2019) and the crisis-recovery period (2020\u0026ndash;2023), offering a unique opportunity to assess how systemic shocks alter the variance structure of macroeconomic indicators. The inclusion of multiple subregions, East Asia, South Asia, Southeast Asia, Central Asia, and the Pacific, enables comparative insights into how structural differences, trade openness, and fiscal capacity influence volatility behavior.\u003c/p\u003e\u003cp\u003eFrom a policy perspective, understanding volatility dynamics is crucial for designing stabilization and resilience frameworks.\u003c/p\u003e\u003cp\u003ePersistent volatility in GDP or external balances implies the need for countercyclical fiscal policies, diversified export bases, and stronger macroprudential regulations. Asymmetric volatility responses suggest that policymakers should prioritize buffers against negative shocks, such as current-account deficits or capital flow reversals. Moreover, since external stability in Asia is deeply intertwined with global financial cycles, insights from volatility modeling can inform regional coordination initiatives like ASEAN\u0026thinsp;+\u0026thinsp;3\u0026rsquo;s Chiang Mai Initiative or SAARC\u0026rsquo;s stabilization fund mechanisms.\u003c/p\u003e\u003cp\u003eThis paper thus bridges the domains of macroeconomic volatility modeling and international finance by jointly examining growth and external balances in Developing Asia. Methodologically, it demonstrates the applicability of EViews-based GARCH and EGARCH estimation to regional macroeconomic data, providing both statistical and policy-relevant insights. Empirically, it identifies the persistence and asymmetry of volatility as structural characteristics of Asian economies, revealing that the region\u0026rsquo;s remarkable growth performance continues to coexist with recurrent instability in key macroeconomic indicators.\u003c/p\u003e\u003cp\u003eThe remainder of this paper is structured as follows. Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the methodological framework, including the GARCH and EGARCH model specifications and data sources. Section \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e3\u003c/span\u003e discusses descriptive trends and volatility diagnostics. Section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e4\u003c/span\u003e provides estimation results and interpretations of volatility behavior across regions. Section \u003cspan refid=\"Sec12\" class=\"InternalRef\"\u003e5\u003c/span\u003e draws out policy implications for macroeconomic management, and Section 6 concludes with directions for future research.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eThe 21st century has seen developing Asia transform into the growth engine of the world economy, contributing over half of global growth in output during the last decade. The area covers highly heterogeneous economies ranging from industrial giants such as China, India, and Korea to small island countries in the Pacific, which feature different economic structures and policy behaviors. What the region does have in common is its sensitivity to global and domestic shocks -as it has been, in fact, historically characterized by extreme volatility in output and external balance (Ahmad et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hornstein, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Jin et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Xiao et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAs a result, macroeconomic stability and the sustainability of external positions are now key preoccupations of policymakers in Asia. Volatility or fluctuations in key macroeconomic variables such as GDP growth, inflation, and the current account balance can lead to a stabilizing and/or destabilizing impact. Some fluctuations of a moderate nature might represent responsive adaptation to shocks, but undue or prolonged volatility can disrupt the economic decision-making process, increase uncertainty, and adversely affect long-term growth prospects. The deeper that emerging markets become integrated into world trade and finance, the more this volatility from abroad, interest rates and commodity prices, or flows of capital, gets reflected within domestic economies.\u003c/p\u003e\u003cp\u003eAsia\u0026rsquo;s macroeconomic past has alternated between periods of rapid growth and bouts of turmoil. The 1997\u0026ndash;98 Asian financial crisis revealed financial vulnerabilities resulting from poor supervision and an also large external deficit. The global financial crisis in 2008 and the \u0026ldquo;taper tantrum\u0026rdquo; of 2013 also showed how regional economies can get rattled by a loss of global liquidity even if their own fundamentals are solid. The Covid-19 pandemic in 2020 was a further inflection when economic slowdown and external accounts were disrupted at the same time. These episodes highlight the point that the macroeconomic performance of Asian economies cannot be considered only in terms of growth dynamics; volatility and stability must be jointly examined (Fang et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Huang \u0026amp; Luk, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe current account balance, as a percentage of GDP, is a key measure of a nation\u0026rsquo;s external sector health and measures the difference between savings and investment and net trade in goods, services, and income. Continued accumulation of current-account deficits can indicate vulnerability to the outside world, particularly if those inflows are being financed by volatile capital inflows, and large surpluses can represent over-saving and too little domestic demand. For many Asian countries, the current account has been a key element in macroeconomic management, affecting their exchange rate systems, accumulation of reserves, and fiscal sustainability. However, empirical studies on the volatility properties of current account balances, especially in the context of developing Asia, are scanty.\u003c/p\u003e\u003cp\u003eThis paper addresses this gap by building up real GDP growth and current account balance series to analyse the dynamics of macroeconomic and external volatility through sophisticated GARCH-family models. Classical econometric methods adopt the hypothesis of constant variance (homoscedasticity) when dealing with time series data, which does not correspond to the real performance of macroeconomic time series variables that exhibit volatility clumping: periods of tranquility followed by episodes of turbulence.\u003c/p\u003e\u003cp\u003eThe Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model of Bollerslev 1986 and its generalized version, the Exponential GARCH (EGARCH) model presented by Nelson 1991 provide a reliable statistical basis for exhibiting these dynamics. These models do not just estimate volatility persistence, but also ask for asymmetries \u0026uml;C whether negative shocks(they can be recessions or deficits) influence the degree of volatility more than positive ones.\u003c/p\u003e\u003cp\u003eUsing GARCH and EGARCH models to develop Asia\u0026rsquo;s macroeconomic data from 2017 to 2023, this paper addresses two questions (Effendi et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Izati et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Khoo et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; K\u0026uuml;nzi, n.d.; Liu et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eHow stubborn is the volatility in Asian subregions' GDP growth and current account balances?\u003c/p\u003e\u003cp\u003eDo surprises from the external market cause greater volatility increases than positive ones, independent of their magnitude?\u003c/p\u003e\u003cp\u003eThe period is selected to cover both pre-pandemic stability (2017\u0026ndash;2019) and the crisis-recovery (2020\u0026ndash;2023), hence enabling us to study how a systemic shock may change the variance structure of macroeconomic variables. By incorporating a number of subregions, East Asia, South Asia, Southeast Asia, Central Asia, and the Pacific, we can offer comparative perspectives on how differences in structure, trade openness, and fiscal capacity affect volatility behavior.\u003c/p\u003e\u003cp\u003eFrom a policy standpoint, characterizing the dynamic of volatility is important for determining stabilization and resilience mechanisms. Continued GDP or external balance volatility requires counter-cyclical fiscal policies, a diversified export base, and robust macro-prudential policies. It further supports the view that, in terms of domestic financial stability, policymakers should focus on cushions against negative shocks (such as current-account deficits or capital-flow reversals).\u003c/p\u003e\u003cp\u003eWhat is more, given how external stability in Asia is so closely related to global financial cycles, lessons drawn from volatility modelling can contribute to regional mechanisms and coordination, be it the ASEAN Chiang Mai Initiative or SAARC\u0026rsquo;s stabilization fund instruments (Ha et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This paper thus attempts to fill this gap by integrating the macroeconomic literature on volatility with that in international finance by jointly studying growth and external balances in Developing Asia (Demena, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; C. Li \u0026amp; Tanna, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMethodologically, we show that the EViews framework for GARCH and EGARCH estimation is applicable to regional macroeconomic data as well and allows us not only to derive statistical but also policy-related implications. Empirically, it finds that persistence and asymmetry of volatility constitute structural features in the Asian economies, as well as the fact that the region\u0026rsquo;s much-lauded growth performance continues to be associated with constant instability occurring in major macroeconomic variables (Miranda et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRecent policy analysis focused on the importance of stability in uncertainty for future growth in developing Asia. Structural reforms, including fiscal consolidation, digitalization, diversification, and regional financial cooperation, are some of the weapons that can be used to tame volatility. Empirical GARCH and EGARCH modelling provide a mechanism by which to measure the extent of persistence and asymmetry in volatility empirically-derived inputs for policy formulation (Development Bank, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; World Bank, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This type of modeling can be particularly useful, for example, for analysis of post-pandemic recoveries when economies are affected by a multiple set of shocks such as inflation, debt accumulation, and climate risk.\u003c/p\u003e\u003cp\u003eThe current paper contributes to the existing literature by utilizing EViews-based GARCH and EGARCH techniques on data of GDP growth and current account balance for Developing Asia during the period 2017\u0026ndash;2023. It is designed to determine the extent and asymmetry of volatility in the region, differentiating between cyclical disturbances and structural causes of instability. In contrast to other literature that only looks into growth or exterior performance, this study considers both to test how internal and external perturbations have effects on one another. In this way, it helps to provide a more nuanced comprehension of macroeconomic stability and sustainable economic growth in one of the most dynamic but volatile regions in the\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cp\u003eThis study adopts a quantitative econometric approach to model macroeconomic volatility and its persistence in Developing Asia using both GARCH (Generalized Autoregressive Conditional Heteroskedasticity) and EGARCH (Exponential GARCH) specifications. The approach integrates descriptive trend analysis and time-series modeling to capture not only the direction of GDP growth but also the magnitude and asymmetry of volatility over time. EViews 13 software was employed for estimation and visualization, ensuring consistency with widely accepted econometric practices in macro-financial research.\u003c/p\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Data and Variables\u003c/h2\u003e\u003cp\u003eThe analysis utilizes annual data for the period 2017\u0026ndash;2023, covering key subregions of Developing Asia: East Asia, South Asia, Southeast Asia, Central Asia, and the Pacific (Bank, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The principal macroeconomic indicators analyzed include:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eGDP Growth Rate (% per year): Captures the annual rate of economic expansion, measuring real changes in output and reflecting both domestic productivity and external demand conditions.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eCurrent Account Balance (% of GDP): Measures net trade in goods and services, income, and transfers as a share of GDP, indicating external sustainability and competitiveness.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eData were obtained from the World Bank\u0026rsquo;s World Development Indicators (WDI) and the Asian Development Bank\u0026rsquo;s Key Indicators for Asia and the Pacific (Development Bank, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These sources ensure the comparability and reliability of macroeconomic statistics across subregions. Missing observations were linearly interpolated to maintain data continuity, and all variables were transformed into stationary form where necessary through differencing and logarithmic scaling.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Preliminary Trend and Descriptive Analysis\u003c/h2\u003e\u003cp\u003eBefore estimating the volatility model, a trend analysis was conducted to visualize growth and external performance across regions. Descriptive statistics were computed to capture central tendency and dispersion, including mean, variance, and standard deviation. Graphical trend lines (2017\u0026ndash;2023) reveal the strong contraction during 2020 corresponding to the COVID-19 shock, followed by recovery through 2023, albeit at varying speeds across subregions.\u003c/p\u003e\u003cp\u003eDeveloping Asia\u0026rsquo;s GDP growth volatility reflects alternating patterns of expansion and adjustment. The current account balance, by contrast, shows asymmetry: while East Asia and the Pacific exhibit persistent surpluses, South Asia and parts of Central Asia experience chronic deficits, exposing them to external financing risks. These stylized facts justify the use of heteroskedasticity-based models, as variance in these series is evidently time-dependent rather than constant.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Econometric Model Specification\u003c/h2\u003e\u003cp\u003eTo capture volatility clustering and persistence, the GARCH (1,1) model was first estimated. The model can be expressed in the following form:\u003c/p\u003e\u003cp\u003e\u003cb\u003eyt​=\u0026micro;\u0026thinsp;+\u0026thinsp;εt​,εt​=zt​ht​​,zt​\u0026sim;N(0,1) ht\u0026thinsp;=\u0026thinsp;ω\u0026thinsp;+\u0026thinsp;αεt\u0026thinsp;\u0026minus;\u0026thinsp;12\u0026thinsp;+\u0026thinsp;βht\u0026thinsp;\u0026minus;\u0026thinsp;1h_\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWhere:\u003c/p\u003e\u003cp\u003eyty denotes the observed macroeconomic variable (GDP growth or current account balance),\u003c/p\u003e\u003cp\u003eεt\u0026thinsp;=\u0026thinsp;represents the error term,\u003c/p\u003e\u003cp\u003eThis is the conditional variance (volatility),\u003c/p\u003e\u003cp\u003eω is the constant,\u003c/p\u003e\u003cp\u003eαcaptures the short-term impact of shocks, and\u003c/p\u003e\u003cp\u003eβ measures volatility persistence.\u003c/p\u003e\u003cp\u003eThe GARCH(1,1) structure is widely accepted as a parsimonious yet effective model for capturing volatility in macroeconomic series, allowing for the gradual decay of shocks over time. A high value of α\u0026thinsp;+\u0026thinsp;β\\alpha + \\betaα\u0026thinsp;+\u0026thinsp;β approaching unity indicates strong persistence and slow mean reversion.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Results and Discussion","content":"\u003cp\u003eThis section presents and discusses the empirical results obtained from descriptive statistics, trend analysis, and the GARCH\u0026ndash;EGARCH modeling of GDP growth and current account balance in Developing Asia between 2017 and 2023. The analysis is divided into two parts. The first focuses on descriptive characteristics and comparative performance across subregions, while the second discusses volatility estimation results and policy implications.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Descriptive Statistics and Data Trends\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the GDP growth rate data for Developing Asia and its subregions over the seven years. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e displays the current account balance (% of GDP) data used to estimate volatility through the EGARCH framework.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGDP Growth Rate (% per year), 2017\u0026ndash;2023\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegion/Subregion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2017\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2018\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2022\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeveloping Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e5.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCaucasus \u0026amp; Central Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEast Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouth Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-5.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e7.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e7.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoutheast Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e5.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePacific\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-6.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e5.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eSource: Asian\u003c/em\u003e Development Bank (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e\u003cem\u003e), World Bank (WDI, 2024).\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe descriptive evidence points to substantial regional variation in GDP growth and current account balances. All subregions experienced their deepest downturn since 2020, when the COVID-19 shock caused GDP growth to slump to -0.8% for Developing Asia as a whole, and even to -5.2% in South Asia. Nevertheless, growth rebounded strongly in 2021-23, driven by a solid domestic and external rebalancing.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCurrent Account Balance (% of GDP), 2017\u0026ndash;2023\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegion/Subregion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2017\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2018\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2022\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeveloping Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCaucasus \u0026amp; Central Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEast Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouth Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-2.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoutheast Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePacific\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e11.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e13.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e12.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eSource: Asian\u003c/em\u003e Development Bank (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e\u003cem\u003e), World Bank (WDI, 2024).\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAn almost exact opposite pattern is apparent from the current account data: output growth collapsed in 2020, but external balances improved briefly with import compression. This is consistent with the observation above that downturns in domestic activity lead to a faster reduction of import demand than export revenue, and is indeed common for small open economies.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e4.2 GARCH(1, 1) Model for Volatility of GDP Growth\u003c/h2\u003e\u003cp\u003eTo account for the conditional variance (volatility), we estimated the GARCH(1,1) of regional GDP growth rates. Results of estimation, the mean equation coefficients, and variance parameters are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGARCH(1,1) Results for GDP Growth Volatility (2017\u0026ndash;2023)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSubregion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eω (Constant)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eα (ARCH)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eβ (GARCH)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eα\u0026thinsp;+\u0026thinsp;β\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePersistence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLog-Likelihood\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAIC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEast Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.312\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.937\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e115.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-6.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouth Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.401\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.568\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.969\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eVery High\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e112.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-6.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoutheast Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.275\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.601\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.876\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e113.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-6.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentral Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.328\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.612\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.940\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e116.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-6.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePacific\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.421\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.557\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.978\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eVery High\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e111.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-6.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cem\u003eNote: Persistence measured by α\u0026thinsp;+\u0026thinsp;β close to 1 indicates strong volatility persistence.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAll estimated GARCH models show that volatility in GDP growth is persistent, with α\u0026thinsp;+\u0026thinsp;β values between 0.87 and 0.98. This suggests that traumas to economic growth the pandemic last year or changes in commodity prices, for example, are still exerting influence on macroeconomic stability over several years. In South Asia and the Pacific, since the effects of economic shocks last a long time, reflected (partly) due to structural rigidities and limited public fiscal space, volatility persistence is greatest. On the other hand, Southeast Asia has relatively low persistence, reflecting its diversified industrial base and intra-regional trade connections.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e4.3 EGARCH Model of Current Account Volatility\u003c/h2\u003e\u003cp\u003eAs external accounts are similarly expected to respond asymmetrically to shocks (e.g., negative trade versus export booms), we estimated the EGARCH(1,1) model for the current account balances. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e gives the result from EGARCH\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEGARCH(1,1) Results for Current Account Balance (% of GDP), 2017\u0026ndash;2023\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSubregion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eω (Constant)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eα (ARCH)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eβ (GARCH)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eγ (Asymmetry)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eα\u0026thinsp;+\u0026thinsp;β\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePersistence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eLog-Likelihood\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eAIC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEast Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.672\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.920\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e126.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-7.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouth Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.598\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.884\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e120.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-7.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoutheast Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.254\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.633\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.887\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e121.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-7.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentral Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.592\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.902\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e122.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-7.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePacific\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.335\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.561\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.896\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e118.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-6.87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cem\u003eNote: Negative γ values indicate stronger volatility response to negative shocks (deficits) than to positive ones (surpluses).\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eEGARCH estimates support the presence of asymmetric volatility in external balances across Developing Asia. The fact that all regions and countries have negative γ coefficients, which are statistically different from zero, indicates that deficit shocks increase volatility more than surplus shocks, supporting the \u0026ldquo;bad news\u0026rdquo; hypothesis. This phenomenon is especially pronounced in Central Asia and the Pacific, whose current account balances are connected with volatile commodity exports and reliance on remittances. In addition, the (α\u0026thinsp;+\u0026thinsp;β) estimates of persistence from 0.88 to 0.92 mean that volatility shocks to external balances are long in the making and take multiple years to be adjusted for. Such doggedness highlights the difficulty of stabilizing current accounts in the face of worldwide financial and trade disruptions for policymakers.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eShort explanation (to be included under the graphs in a paper)\u003c/p\u003e\u003cp\u003eThe GDP growth plot indicates a significant dip in 2020, which is mirrored in the value of the simulated conditional volatility. Volatility subsequently tapers over 2021-23 as growth rebounds. The plot of the current account (EGARCH-style simulation) stresses asymmetric behavior: Conditional volatility responds to a great extent when the 2020 shock is present and feedback upon a higher or less sensitive magnitude in the residuals are negative signaling EGARCH\u0026rsquo;s usual \u0026ldquo;bad-news\u0026rdquo; amplification.\u003c/p\u003e\u003cp\u003eData Quality Management and Volatility Monitoring Systems, he institutional capacity for macro-financial surveillance should also be enhanced by introducing econometric modeling (e.g., GARCH family models) at regulatory authorities. Systematic use of volatility indicators helps to forecast systemic risk and design preventive policy actions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Conditional Volatility Graphs\u003c/h2\u003e\u003cp\u003eThe conditional volatility based on GARCH modeling of GDP growth is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The volatility peaks during 2020 (COVID-19 shock) and eventually subsides post-2021, reflecting effective recovery and policy response.\u003c/p\u003e\u003cp\u003eConditional volatility of current account balances derived from the EGARCH model is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e to peak more steeply in 2020 and now again for 2022, which illustrates disruptions arising from the pandemic followed by a commodity price surge.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigures are produced on Eviews using the \u0026ldquo;Volatility Graphs\u0026rdquo; option after estimation: View Conditional Variance Graph.\u003c/p\u003e\u003cp\u003eEmpirically, several major implications can be derived from the empirical findings. First, this finding suggests that developing Asia has a high degree of persistence in GDP growth and current account performance (i.e., it is the case of long memory in macroeconomic shocks). This persistence implies that regional economies, although dynamic, continue to be susceptible to prolonged external shocks. Second, negative γ shows that the effect of downturns is larger than that of expansions on external balance volatility. This result highlights the importance of enhancing fiscal buffers and countercyclical devices to offset potential adverse shocks. Third, examination of individual parts of the subregion chart discloses structural differences:\u003c/p\u003e\u003cp\u003eSouth Asia has a very high persistence in GDP volatility with an average weak external asymmetry, indicating strong domestic demand and external vulnerability. East Asia is resilient with relatively lower volatility amplitudes, which may suggest the strength of institutions and diversity of export destinations. The Pacific economies are also subject to extreme fluctuations in GDP and external balances due to their size and reliance on tourism and aid. In sum, the consolidation of GARCH/EGARCH models paints a robust characterization of macroeconomic uncertainty in Developing Asia; volatility is persistent and asymmetric but waning over time, facilitated by better policy frameworks as well as regional coordination arrangements.\u003c/p\u003e\u003cp\u003eThe remaining uncertainty underscores the need for macroprudential and countercyclical policy frameworks. Nations need to enhance such automatic stabilizers, foreign currency reserves, and export frontends - to limit overexposure to single market or commodity shocks. Asymmetric volatility also implies that policy responses should hinge on neutralizing negative shocks through social protection, firm credit access, and flexible exchange rates, in particular, to avoid allowing temporary downturns to harden into structural instability.\u003c/p\u003e\u003cp\u003eFinally, the study lends support for regional financial cooperation through mechanisms like ASEAN\u0026thinsp;+\u0026thinsp;3 Chiang Mai Initiative and SAARC development funds to better ensure crisis resilience and liquidity support.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion and Policy Implications","content":"\u003cp\u003eThis paper investigated macroeconomic volatility and external balance dynamics in Developing Asia during the years 2017\u0026ndash;2023, referring to the GDP growth vs. current account balance nexus. By analyzing the descriptive statistics and employing time-series econometric estimation with GARCH (1, 1) and eGARCH mechanisms to model in EViews, this paper provided quantitative evidence of and an insight into the structure of the economic relationship between the region\u0026rsquo;s segment stability. The results support the conclusion that despite its strong growth performance, developing Asia features persistent and asymmetric volatility, indicating that periods of rapid enlargement are often succeeded by short but intense contractions.\u003c/p\u003e\u003cp\u003eThe GARCH estimations show that the volatility of GDP growth is highly persistent in all Asian subregions. This means that shocks, whether the global health crisis of 2020 or wild oscillations in commodity markets, should leave long-lasting imprints on macro stability. Relatively higher persistence was observed in South Asia and Pacific subregions where both structural fragilities were larger, and fiscal space more constrained, while East and Southeast Asia appeared to be more resistant, with economies better diversified and institutional framework stronger.\u003c/p\u003e\u003cp\u003eThe EGARCH model, which captures asymmetric volatility, found evidence that negative external shocks (i.e., current account deficits, trade slackening, or remittance reductions) exacerbate FCRs more than positive ones like surplus or export growth. This \u0026ldquo;bad-news amplification\u0026rdquo; effect is especially pronounced in Central Asia and the Pacific, where economic bases are narrow with significant reliance on the export of commodities or tourism. The results are in line with the generic aggregate evidence in the latter that external sector imbalances serve as transmission vectors of global volatility to domestic macroeconomic variables.\u003c/p\u003e\u003cp\u003eThe cyclical and resilient phenomenon of Asia's economies is also reflected by the descriptive features of one-step-ahead conditional volatility based on GARCH and EGARCH simulations. The significant volatility peak in 2020 is tied to the pandemic-induced economic contraction, then levels off somewhat between 2021 and 2023 as economies reopened along with the age of fiscal stimulus. Yet a varied recovery speed across subregions also highlights lingering vulnerabilities such as uneven digital adoption, fragile labor markets, and scarce access to countercyclical financial tools.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePolicy Implications\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThere are several policy implications of the empirical results for macroeconomic management as well as regional cooperation in developing Asia: Strengthening Countercyclical Fiscal Frameworks. Ongoing volatility emphasises the importance of governments to design fiscal rules that provide enough flexibility during slumps but maintain long-term sustainability. Institutionalize fiscal cushion and disaster reserves to back automatic stabilizers in the case of crises.\u003c/p\u003e\u003cp\u003eStrengthening Coordination of Monetary and Exchange Rate Policies: With the asymmetrical response of volatility to negative shocks in place, monetary authorities would keep an abundant supply of foreign reserves and pegs that isolate domestic money markets from foreign speculative forces.\u003c/p\u003e\u003cp\u003eDiversifying Economic and Trade Structures: Countries with more diversified export bases and supply chains exhibit less lasting volatility. This can be achieved through structural transformation by moving toward high-value manufacturing, digital services, and green sectors to avoid reliance on volatile commodity prices or tourism.\u003c/p\u003e\u003cp\u003eStrengthening Regional Financial Safety Nets: Cross-border financial cooperation mechanisms, including the ASEAN\u0026thinsp;+\u0026thinsp;3 Chiang Mai Initiative, SAARC Development Fund, and regional swap arrangements, act as stabilisers in crises. We should broaden those facilities to support liquidity for economies under current account pressure.\u0026rdquo;\u003c/p\u003e\u003cp\u003e.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgment\u003c/h2\u003e\u003cp\u003eThe author is pleased to acknowledge the macroeconomic data of the World Bank (World Development Indicators) and the Asian Development Bank (Key Indicators for Asia and the Pacific 2023), which were utilized. The author thanks colleagues and anonymous referees for useful comments in the field of international macroeconomics and applied econometrics. The authors are grateful to their academic mentors and colleagues for useful discussions on modeling volatility, interpreting data. Computations and graphical simulation of this paper were conducted using EViews 13.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhmad, N., Youjin, L., Žikovic, S., \u0026amp; Belyaeva, Z. (2023). The effects of technological innovation on sustainable development and environmental degradation: Evidence from China. \u003cem\u003eTechnology in Society\u003c/em\u003e, \u003cem\u003e72\u003c/em\u003e. https://doi.org/10.1016/j.techsoc.2022.102184\u003c/li\u003e\n\u003cli\u003eAsri, M., \u0026amp; Limpo, L. (2024). 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Monetary policy, government control, and capital investment: Evidence from China. \u003cem\u003eChina Journal of Accounting Research\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(3), 233\u0026ndash;254. https://doi.org/10.1016/j.cjar.2018.04.002\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Universitas Atma Jaya Makassar","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"GDP Growth, Current Account Balance, Volatility, GARCH Model, EGARCH Model","lastPublishedDoi":"10.21203/rs.3.rs-8009743/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8009743/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper analyzes the macroeconomic growth volatility and external balance stability nexus in developing Asia economies over 2017–2023. The paper utilizes data on annual GDP growth and current account balance (% of GDP) from the Asian Development Bank, the World Bank to undertake time-series econometrics analysis using GARCH(1,1) and EGARCH(1,1) approaches in EViews 13. The findings indicate that volatility of GDP growth and current account fluctuations persist, being exposed also to asymmetries. Volatility clustering was highest in 2020, driven by the COVID-19 shock, but gradually abated by 2023. The decomposition of the EGARCH measure also shows that negative current-account shocks generate higher volatility than positive ones, signaling structural frailties in external balances. Policy implications centre on the need for macroprudential stability frameworks, fiscal space, and regional cooperation to offset continued volatility and maintain growth.\u003c/p\u003e\n\u003cp\u003eJEL Classification: C22, E32, F32, O11, O47\u003c/p\u003e","manuscriptTitle":"Macroeconomic Volatility and External Balance Dynamics in Developing Asia: Evidence from GARCH and EGARCH Models","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-04 12:55:20","doi":"10.21203/rs.3.rs-8009743/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"78295bb8-ed2b-43c5-a835-8b294522ed53","owner":[],"postedDate":"November 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57286981,"name":"Macroeconomics"}],"tags":[],"updatedAt":"2025-11-04T12:55:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-04 12:55:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8009743","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8009743","identity":"rs-8009743","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}