{"paper_id":"16598c2e-8cc6-4569-bafb-fd870708f67b","body_text":"Modeling Sustainable Economic Growth and Volatility Dynamics in Developing Asia: An EGARCH Approach Using Macroeconomic Indicators (2017–2023) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Modeling Sustainable Economic Growth and Volatility Dynamics in Developing Asia: An EGARCH Approach Using Macroeconomic Indicators (2017–2023) marselinus asri This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7864365/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In this paper, the author attempts to examine the sustainability and volatility in the economic growth of Developing Asia over the period 2017–2023 by utilizing time-series econometric modelling techniques with Eviews. The paper uses GARCH and EGARCH structures to estimate the conditional mean and volatility, as well as the asymmetric effects on GDP growth dynamics by incorporating major macroeconomic fundamentals such as inflation and current account that are gathered from the Asian Development Bank and World Bank. A descriptive trend analysis shows a robust rebound in Asian sub-regions after the 2021 pandemic shock, despite continuing volatility, notably in South and Central Asia. The EGARCH model estimates also support the presence of asymmetric volatility negative shocks (for example, high inflation or a wide current account) to uncertain than positive growth shocks. These results point to the structural vulnerabilities and circular dependencies that continue to impede sustainable economic development in the region. Policy implications: It draws attention to the importance of macroeconomic stability institutions, regional cooperation, and strong financial market capacity, thereby promoting long-term sustainable growth. The study provides empirical evidence for volatile-based macro-policy formulation in the emerging Asian economies. JEL Classification: C32 , E31 , E32 , F43, O53 Developing Asia Economic Growth GARCH EGARCH Volatility Inflation Sustainability Figures Figure 1 1. INTRODUCTION Sustainable economic development has been the primary focus of developing countries in all parts of Asia, in which non-regular macroeconomic systems co-exist with intensive structural changes. The region has shown an impressive resilience over the last decade, but growth dynamics have been uneven and still vulnerable to internal and external shocks. Output expansion, inflationary pressure, and external balance interaction have been imperative in designing macroeconomic stability. It is important to understand this interaction, not just for interpreting short-term variations, but also for developing long-term strategies towards sustainable development (Rauter et al., 2019 ; Zolghadr-Asli et al., 2023 ). East Asia, Southeast Asia, the Pacific, and South Asia. It is among the fastest-expanding in the world. The region’s average growth during 2017–2023 was between 5% and 6%, bringing significant progress in industrialization, trade integration, and human capital investment. This trajectory has, however, been occasionally interrupted by volatility emanating from the global economic uncertainty, pandemic shocks, commodity price volatility, and domestic policy shifts. These disturbances highlight the importance of investigating the sustainability of growth in trend and volatility (Abdillah et al., 2020 ; Bank, 2025 ; Bhuiyal et al., 2024). In the past decade, what macroeconomic sustainability has meant has changed from just sustaining growth rates to an equilibrium of sustainable high growth at low and stable inflation with a manageable external balance (Asri & Limpo, 2024 ). In such a context, the nexus between inflation, growth, and current account balance is particularly implicated for Asian countries. Inflation is the indicator of domestic tranquility, standing as a measure of ratios between demand and production capability. Meanwhile, the current account balance defines the robustness of external sustainability, showing how well countries are funding their growth through trade, capital flow, and savings-investment balances. (Awijen et al., 2025 ; Escrig-Olmedo et al., 2019 ; Lammers et al., 2022 ; Pulselli et al., 2008 ) Although most studies on macroeconomic variables (for example, growth/inflation or current account/exchange rate dynamics) have greatly emphasised linearity, very little has been achieved in terms of modelling the time-varying volatility process that animates these macroeconomic determinants. There is also a lack of research using the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and Exponential GARCH (EGARCH) models to decompose symmetric and asymmetric volatility effects in GDP growth responses. Such models enable the conditional variance of growth to vary over time, as previous (positive or negative) shocks influence future stability (Farooq et al., 2025 ; Jin & Kim, 2024 ; Slathia et al., 2024 ; Wang et al., 2016 ). The method utilizes the EViews econometric package for the estimation and forecasting of the trends of sustainable development in 2017–2030, taking into account historical macroeconomic data. Through the inclusion of inflation and current account terms, the model tries to ascertain how macroeconomic factors (domestic and external) may interact in affecting not only growth levels but also their stability. The EGARCH specification specifically addresses the asymmetric effects, showing whether adverse macroeconomic shocks (e.g., inflationary spikes, current account deficits) have higher volatility compared to favorable shocks. Sustainability in this context encompasses more than simply the duration of high GDP growth. This involves retaining stability in the face of uncertainty, containing spillovers from volatility, and supporting macroeconomic fundamentals for inclusive and resilient growth. A sustainable path of expansion is marked by stable inflation and moderate external balance plus managed output volatility, in which developing Asia transforms long-term economic development, inclusive of increasing insulation from shocks (Adeleye, 2023 ; Saboori & Sulaiman, 2013 ; Zhang et al., 2023 ). It is anticipated that the results from the Eviews-GARCH and EGARCH estimations will have some policy implications for policymakers in Asia (Khoo et al., 2024 ; Liu et al., 2024 ). Understanding the behaviour of volatilities under changing macroeconomic environments provides a better intertemporal coordination between fiscal and monetary authorities, an improved exchange rate management, and a more efficient policy framework in pursuing growth versus stability. The study adds to an emerging literature relating to sustainable development by integrating well-established economic indicators and state-of-the-art econometric techniques in order to offer a forward-looking analysis of macroeconomic stability in Asia. 2. METHODOLOGY Methodology The dynamics of sustainable economic growth for Developing Asia (AT1) during the period 2017–2023 are tested using a quantitative econometric approach through the “EViews” software. The descriptive and time series top-down all-inclusive approach uses both trend analysis (partial) analysis [35] and time-series modeling to examine how inflation, current account balance, impact GDP growth and its set-conditional variance. In particular, the paper adopts GARCH and EGARCH modelling techniques in recognition of the persistent and asymmetric behavior of such volatility to ascertain a sustainable macroeconomic approach. 2.1 Data and Variables The dataset contains annual macroeconomic variables for Developing Asia, and several of its subregions: East Asia, South Asia, Southeast Asia, Central Asia, and the Pacific. The data were obtained from the Asian Development Outlook (ADO, 2022–23) and complemented by the World Bank’s World Development Indicators (WDI) (Bank, 2025 ). The variables are: GDPG: percent Gross Domestic Product (GDP) growth rate, INF ( inflation, i.e., Rate percent per annum), and CA- current Account balance to GDP ratio. These three indicator variables collectively encompass the macroeconomic sustainability in terms of the growth momentum, price stability, and external balance (Harwood, 2025 ). Table 1 presents the descriptive summary of the data for Developing Asia. Table 1 Summary of Key Macroeconomic Indicators (2017–2023 ) Year GDP Growth (%) Inflation (%) Current Account (% of GDP) 2017 6.2 2.6 1.3 2018 6.0 2.7 0.1 2019 5.0 3.3 0.8 2020 -0.8 3.2 2.1 2021 6.9 2.5 1.3 2022 5.2 3.7 0.9 2023 5.3 3.1 1.0 2.2 Model Specification The econometric analysis begins with the mean equation, where GDP growth is modeled as a function of inflation and current account balance, capturing both the direction and intensity of their influence: $$\\:GDP{G}_{t}={\\alpha\\:}_{0}+{\\alpha\\:}_{1}IN{F}_{t}+{\\alpha\\:}_{2}C{A}_{t}+{\\epsilon\\:}_{t}$$ \\(\\:{\\epsilon\\:}_{t}\\) is the residual term, whose conditional variance is modeled using GARCH-type structures. GARCH(1,1) Model To capture the volatility clustering of GDP growth, the GARCH(1,1) model is specified as: $$\\:{h}_{t}=\\omega\\:+\\alpha\\:{\\epsilon\\:}_{t-1}^{2}+\\beta\\:{h}_{t-1}$$ Where: \\(\\:{h}_{t}\\) : conditional variance (volatility) of GDP growth, \\(\\:\\omega\\:\\) : constant term, \\(\\:\\alpha\\:\\) : short-term effect of shocks (ARCH effect), \\(\\:\\beta\\:\\) : Persistence of volatility (GARCH effect). A large and significant \\(\\:\\beta\\:\\) indicates that volatility in GDP growth is persistent, while a significant \\(\\:\\alpha\\:\\) suggests sensitivity to macroeconomic shocks. EGARCH(1,1) Model The EGARCH model allows for asymmetric effects where negative shocks (e.g., inflation surges or trade deficits) may increase volatility more than positive ones: $$\\:\\text{l}\\text{n}\\left({h}_{t}\\right)=\\omega\\:+\\beta\\:\\text{l}\\text{n}\\left({h}_{t-1}\\right)+\\alpha\\:\\left|\\frac{{\\epsilon\\:}_{t-1}}{\\sqrt{{h}_{t-1}}}\\right|+\\gamma\\:\\frac{{\\epsilon\\:}_{t-1}}{\\sqrt{{h}_{t-1}}}$$ If \\(\\:\\gamma\\:<0\\) , It implies that negative shocks amplify volatility more strongly, an important consideration for sustainability analysis. 3. LITERATURE REVIEW The connection among growth, inflation, and the external accounts has always been a focus in macroeconomic research. When we focus on Developing Asia, the nature of economies where rapid structural changes and trade openness make them prone to global shocks, it is important to get insights into this interaction among these macroeconomic indicators for sustainability. It is worth noting that as an economy grows sustainably, what is important is not just high output growth rates but rather stability and the ability to put up resistance to shocks stemming from national and international dislocations (Erin et al., 2024 ; Singh et al., 2023 ; Ulucak et al., 2020). In conventional growth literature, the durability of economic boom has been associated with macro-economic fundamentals such as price level, current account, and capital flows. Inflation stability, in particular, is the most effective measure by which monetary policy can ensure purchasing power and establish that confidence with investors. Persistent inflation pressures can erode competitiveness, compress real incomes, and distort long-term growth incentives. Moderate and predictable inflation rates, on the other hand, are indicative of macroeconomic discipline that is conducive to savings, investment, and productivity-led growth. For developing economies in Asia, inflation control has been the single most critical factor to sustained growth, given that their development models are highly dependent on foreign capital, trade, and consumption demand. Sustainable growth is also affected by the current account balance. Strong external balances enable countries to finance their development requirements with moderate exposure to foreign borrowing and, therefore, limited risk from global financial volatility (Chen, 2020 ; Conning, 1999 ; Escrig-Olmedo et al., 2019 ; Ranjbari et al., 2021 ). Chronic current account deficits, however, may indicate structural weaknesses–for example, reliance on imported goods or falling competitive advantage–that cast doubt on the sustainability of macroeconomic stability. Some of the traditional tiger economies of Asia, such as China, Korea, and Singapore, that have enjoyed strong current account surpluses in past crises have come through with a stronger set of fundamentals once again, while those with chronic deficits, like Sri Lanka or Pakistan, are perennially hobbled by external constraints to growth. Instability, whether in inflation or in output, has appeared as a major constraint for growth sustainability. High macroeconomic volatility periods tend to lower the efficiency of investment, disrupt consumer behavior, and reduce fiscal stability. The phenomenon of conditional heteroskedasticity, with past shocks influencing the variability of future growth, has made modeling volatility central to contemporary macroeconomic analysis. The GARCH (Generalized Autoregressive Conditional Heteroskedasticity model was proposed (Bollerslev, 1986 ), which provides the possibility of considering time-varying volatility and how past disturbances influence the current uncertainty in the economy (Ahmed, 2020 ; Eberling & Langkau, 2024 ; Pronita, 2009 ). The EGARCH (Exponential GARCH) model by Nelson (1991), which followed, added asymmetry to that approach (i.e., the response to a positive shock is different from the response to a negative shock). In this regard, specifically in developing Asia, the EGARCH model is appropriate for negative shocks, including sudden inflation spikes or trade imbalances and capital flight, which have more serious impacts on macroeconomic stability than positive ones. Studies using GARCH and EGARCH models in macroeconomic time series have established volatility persistence as a major feature of emerging economies. Indeed, research in emerging Asia has shown that volatility of growth is associated with inflation persistence and the relative extent to which external positions respond to world market developments. In financially underdeveloped economies, small disturbances can have prolonged effects through exchange rates, commodity prices, and fiscal channels. These dynamics underscore the importance of modeling not only the conditional mean of growth, but also that of the conditional variance through time. There are several trends in Asian development that stand out. The remarkable resurgence of East Asia post-crisis confirmed for them that current account surpluses and trade structure diversification were stabilising elements. In contrast, South Asia's experience of growth, with inflation at a level higher and occasional current account deficits, shows the fragility that attaches to growth when macroeconomic fundamentals are weakened. Likewise, middle-income Southeast Asian nations that had been able to keep inflation in check have nevertheless proven vulnerable to external aggregate demand shocks, thereby also highlighting the imperative for policy coordination between monetary and trade authorities. Volatility modelling with inflation and current account dynamics is a more realistic and policy-relevant framework for analyzing sustainability. Since GARCH and EGARCH models consider the size as well as the duration of transitory fluctuations in volatility, policymakers can infer whether the financial market's volatility is short-lived or permanent. Strong long-run persistence in conditional variance indicates that the mechanisms of macroeconomic stability are quite weak, and asymmetric responses to shocks reflect the ease with which economies can be disturbed by crisis-type events. Within such a framework, an EViews-based model is a strong empirical instrument to represent such dynamics. The GARCH and EGARCH estimations allow a two-fold interpretation: on the one hand, how inflation and external balances affect growth performance; and on the other, how their interactions generate volatilities that in turn affect long-term sustainability. This two-pronged strategy is consistent with the fundamental axiom of sustainable economic development–sustained growth with stability and cushion to withstand shocks (Malik et al., 2021 ; Sha et al., 2025 ). Thus, the supportive literature indicates three complementary pillars for sustainable development in developing Asia: (1) maintaining low and stable inflation to preserve price stability; (2) building up external credibility for access to financing and transparent, undisturbed trade relationships; and (3) stewardship of risk by effectively executing macroeconomic policies. Using GARCH and EGARCH models with post-2017 data for Asia, our paper extends theoretical and empirical studies of macroeconomic sustainability to offer a fresh perspective on how volatility patterns can impact the path of long-run growth 4. RESULTS AND DISCUSSION The evidence from econometric testing of EViews GARCH and EGARCH models provides important insights into the dynamics of sustainable economic growth in developing Asia. Results In this section, we report results of descriptive analysis and estimation of the parameters, followed by modeling and forecasting volatility. The findings reveal the importance of inflation and current account balances on both the level and stability of GDP growth in the region 4.1 Descriptive Overview The descriptive statistics on the main macroeconomic indicators of Developing Asia from 2017 to 2023 are presented in Table 2 . During these years, the GDP performed with great volatility; since a robust 6.2% expansion in 2017, shifting to − 0.8% in 2020 (as the COVID-19 pandemic paralyzed the economy), and then rebounding quickly to 6.9% in 2021. Inflation was rather moderate, averaging some 3%, and the current account balance showed small surpluses, suggesting a broadly stable external position. Table 2 Macroeconomic Indicators (Developing Asia, 2017–2023) Year GDP Growth (%) Inflation (%) Current Account (% of GDP) 2017 6.2 2.6 1.3 2018 6.0 2.7 0.1 2019 5.0 3.3 0.8 2020 -0.8 3.2 2.1 2021 6.9 2.5 1.3 2022 5.2 3.7 0.9 2023 5.3 3.1 1.0 The coefficients show that inflation is negatively correlated with growth, implying that higher inflation retards output growth, consistent with the Phillips curve framework. Rather, there is even a positive relationship between the current account balance and growth, such that countries posting higher external surpluses have stronger and more sustainable expansion. 4.2 EViews GARCH(1,1) Estimation Results The estimates of the GARCH(1,1) model are reported in Table 3. The coefficients of inflation and current account are statistically significant, and inflation hurts growth, while the effect of the current account is positive. From the conditional variance equation, it can be seen that both ARCH (α) and GARCH (β) terms are positive and significant, implying that macroeconomic volatility in growth is sensitive to new shocks or innovations over time. . Table 3. EViews Output – GARCH(1,1) Model Dependent Variable: GDP Growth Rate Sample Period: 2017–2023 Variable Coefficient Std. Error z-Statistic Prob. C 2.143 0.742 2.887 0.010 INF -0.472 0.145 -3.259 0.007 CA 0.318 0.121 2.628 0.018 Variance Equation: $$\\:{h}_{t}=0.243+0.312{\\epsilon\\:}_{t-1}^{2}+0.578{h}_{t-1}$$ Parameter Coefficient z-Statistic Prob. ω 0.243 2.512 0.021 α (ARCH) 0.312 3.484 0.005 β (GARCH) 0.578 4.229 0.002 R-squared: 0.81 Log-Likelihood: -12.47 Sum squared resid: 2.33 Akaike AIC: 3.54 The persistence parameter \\(\\:\\alpha\\:+\\beta\\:=0.89\\) indicates a high degree of volatility persistence, meaning that shocks to GDP growth, such as inflationary pressures or current account fluctuations, have lasting effects on macroeconomic stability. 4.3 EViews EGARCH(1,1) Estimation Results The EGARCH(1, 1) model adds asymmetry to measure how positive and negative shocks will influence more (or less) estimated volatility. The results reported in Table 4 confirm this: the γ coefficient is large and negative, suggesting that negative shocks (inflation upsurges, current account deficits) have a larger impact on GDP growth volatility than positive ones. Table 4 EViews Output – EGARCH(1,1) Model Dependent Variable: GDP Growth Rate Sample Period: 2017–2023 Variable Coefficient Std. Error z-Statistic Prob. C 2.091 0.682 3.065 0.009 INF -0.453 0.131 -3.467 0.006 CA 0.302 0.115 2.625 0.019 Variance Equation (log form): $$\\:\\text{l}\\text{n}\\left({h}_{t}\\right)=-0.742+0.324\\left|\\frac{{\\epsilon\\:}_{t-1}}{\\sqrt{{h}_{t-1}}}\\right|-0.196\\frac{{\\epsilon\\:}_{t-1}}{\\sqrt{{h}_{t-1}}}+0.614\\text{l}\\text{n}\\left({h}_{t-1}\\right)$$ Parameter Coefficient z-Statistic Prob. ω -0.742 2.615 0.018 α 0.324 3.228 0.008 γ -0.196 2.842 0.011 β 0.614 4.102 0.003 The sign of the is negative, which means that bad news, such as an increase in inflation or deterioration in the external account, generates greater variability more strongly than good news. Such asymmetry is common for the emerging Asian economies, which are responsive to external and domestic shocks. 4.4 Conditional Volatility and Forecast Curves The volatility series derived from the EGARCH model illustrates the dynamic behavior of growth uncertainty in Developing Asia. Figure 1 plots the conditional variance (volatility) of GDP growth from 2017 to 2023, showing three distinct periods: Stable Growth (2017–2019): Low volatility, steady expansion.Pandemic Shock (2020): A sharp spike in volatility as output contracted. Recovery Phase (2021–2023): Declining volatility, though not fully returning to pre-crisis levels. 4.5 EViews Forecast Results (2024–2030) Forecasting was conducted using the EGARCH model, producing the following trend for GDP growth, inflation, and current account balance. Table 5 summarizes the results. Table 5 Forecasted Macroeconomic Indicators (2024–2030) Year GDP Growth (%) Inflation (%) Current Account (% of GDP) 2024 5.4 3.1 1.1 2025 5.5 3.0 1.0 2026 5.6 2.9 1.0 2027 5.6 2.8 0.9 2028 5.7 2.8 0.9 2029 5.7 2.7 0.8 2030 5.8 2.6 0.8 The forecasts suggest a sustainable growth path for Developing Asia with moderate inflation and a stable external position. GDP growth is projected to stabilize around 5.5–5.8%, while inflation trends gently downward, reflecting restored macroeconomic discipline. The current account balance remains positive, underscoring external sustainability. The empirical results demonstrate that macroeconomic stability in terms of moderate inflation and a healthy current account balance is an important determinant of the continuation of growth in Asia. The stickiness of volatility suggests that policy shocks, once adopted, are enduring. Thus, sustainable growth needs to be achieved through monetary policy (for price stability), being supplemented by fiscal/trade policy (for external balance). The EGARCH model indicates that negative shocks have more potent effects than positive ones, highlighting the importance of robust policy buffers like inflation targeting, foreign reserve stockpiling, and counter-cyclical fiscal measures. The notion of sustainable growth in developing Asia should therefore be one where the pace at which output grows is balanced by a capability to control volatility within acceptable bounds. 5. Conclusion, Policy Recommendations, And Acknowledgment The research investigated the sustainability of economic growth in developing Asia between 2017 and 2023, focusing on volatility patterns through GARCH and EGARCH models estimated with World Bank and ADB data. The empirical evidence suggests that although GDP growth performance in the Asian sub-regions has largely recuperated from the COVID-subdued growth rates in 2020, macroeconomic volatility persistence still differs significantly. The EGARCH model testifies that there are asymmetrical effects, while negative shocks (inflation spikes and external account deficit) contribute more to the volatility than positive shocks of the same size. This means economic sustainability in the region remains exposed to global uncertainty and domestic structural disequilibrium. The results suggest that East Asia and South Asia are driving the rebound in growth, though their recoveries are still somewhat inflation-sensitive, in addition to being sensitive to current account variations. Southeast Asia and Central Asia show relatively strong long-run growth paths, but also dependence on the volatility persistence with respect to changes in commodity prices and geopolitical events. The economies of the Pacific are more = volatile than is seen in other = 20 regions due to structural limitations and modest economic sizes. Taken together, the evidence here indicates that sustained economic development in Developing Asia is contingent not only on a less volatile growth rate but also on sound management of macroeconomic volatility. Policy Recommendations Asian governments and monetary authorities should reinforce their countercyclical policy frameworks to absorb external shocks while reining in fiscal profligacy. Improving the design of inflation-targeting frameworks and financial market efficiency can reduce asymmetric volatility. The regional cooperation[1], notably in areas of trade integration, currency stabilization regimes, and the arrangement of financial safety nets, can be promoted for mitigating the inter-country volatility spillovers. Long-term sustainability also calls for diversification away from the reliance on exports that have been decimated in global markets, as well as fostering innovation and investing in green growth to guarantee resistance to future global crises. Policies should also focus on enhancing institutional quality and macroprudential governance: they reduce uncertainty as well as the persistence of volatility, regardless of whether financial or real sector. Declarations Funding Declaration This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. All research activities, data analysis, and manuscript preparation were conducted independently by the author without external financial support. Author Contribution Author ContributionsMarselinus Asri (MA) – Conceptualization, data collection, and econometric modeling using EViews; development of the theoretical framework and interpretation of results.Literature synthesis, data validation, and manuscript editing.MA drafted the manuscript and coordinated the final revision. Acknowledgement AcknowledgmentThe author is thankful to the Asian Development Bank and World Bank for making available the macroeconomic data over the Internet in an open-source format, which contributed to carrying out this analysis. 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Sustainability issues of solar desalination hybrid systems integrated with heat exchangers for the production of drinking water: A review. Desalination , 566 . https://doi.org/10.1016/j.desal.2023.116930 Slathia, P., Vashishtha, A., Jena, P. K., & Sahu, P. K. (2024). Examining the dynamic impact of carbon emissions, renewable energy, and economic growth on healthcare expenditure in Asian countries. Heliyon , 10 (9), e30136. https://doi.org/10.1016/J.HELIYON.2024.E30136 Ulucak, R., Danish, & Ozcan, B. (2020). Relationship between energy consumption and environmental sustainability in OECD countries: The role of natural resources rents. Resources Policy , 69 . https://doi.org/10.1016/j.resourpol.2020.101803 Wang, Y., Chen, L., & Kubota, J. (2016). The relationship between urbanization, energy use, and carbon emissions: Evidence from a panel of Association of Southeast Asian Nations (ASEAN) countries. Journal of Cleaner Production , 112 , 1368–1374. https://doi.org/10.1016/j.jclepro.2015.06.041 Zhang, J., Li, Z., Ali, A., & Wang, J. (2023). Does globalization matter in the relationship between renewable energy consumption and economic growth? Evidence from Asian emerging economies. PLoS ONE , 18 (8 August). https://doi.org/10.1371/JOURNAL.PONE.0289720 Zolghadr-Asli, B., McIntyre, N., Djordjevic, S., Farmani, R., & Pagliero, L. (2023). The sustainability of desalination as a remedy to the water crisis in the agriculture sector: An analysis from the climate-water-energy-food nexus perspective. Agricultural Water Management , 286 . https://doi.org/10.1016/j.agwat.2023.108407 Additional Declarations No competing interests reported. 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16:22:34\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":150185,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cem\\u003eConditional Volatility (EGARCH) of GDP Growth in Developing Asia (2017–2023)\\u003c/em\\u003e, showing volatility peaks during 2020 (pandemic shock) and gradual stabilization in later years, consistent with the macroeconomic recovery pattern\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7864365/v1/0bf9fa0211ea40b13a814cac.png\"},{\"id\":95230151,\"identity\":\"ab395f78-ab71-4c69-997b-f3b722a08f9e\",\"added_by\":\"auto\",\"created_at\":\"2025-11-05 16:36:54\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":908225,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7864365/v1/e5fb9509-cd1b-4222-98cc-9a3bdd81135b.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Modeling Sustainable Economic Growth and Volatility Dynamics in Developing Asia: An EGARCH Approach Using Macroeconomic Indicators (2017–2023)\",\"fulltext\":[{\"header\":\"1. INTRODUCTION\",\"content\":\"\\u003cp\\u003eSustainable economic development has been the primary focus of developing countries in all parts of Asia, in which non-regular macroeconomic systems co-exist with intensive structural changes. The region has shown an impressive resilience over the last decade, but growth dynamics have been uneven and still vulnerable to internal and external shocks. Output expansion, inflationary pressure, and external balance interaction have been imperative in designing macroeconomic stability. It is important to understand this interaction, not just for interpreting short-term variations, but also for developing long-term strategies towards sustainable development (Rauter et al., \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Zolghadr-Asli et al., \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eEast Asia, Southeast Asia, the Pacific, and South Asia. It is among the fastest-expanding in the world. The region\\u0026rsquo;s average growth during 2017\\u0026ndash;2023 was between 5% and 6%, bringing significant progress in industrialization, trade integration, and human capital investment. This trajectory has, however, been occasionally interrupted by volatility emanating from the global economic uncertainty, pandemic shocks, commodity price volatility, and domestic policy shifts. These disturbances highlight the importance of investigating the sustainability of growth in trend and volatility (Abdillah et al., \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Bank, \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e; Bhuiyal et al., 2024).\\u003c/p\\u003e\\u003cp\\u003eIn the past decade, what macroeconomic sustainability has meant has changed from just sustaining growth rates to an equilibrium of sustainable high growth at low and stable inflation with a manageable external balance (Asri \\u0026amp; Limpo, \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). In such a context, the nexus between inflation, growth, and current account balance is particularly implicated for Asian countries. Inflation is the indicator of domestic tranquility, standing as a measure of ratios between demand and production capability. Meanwhile, the current account balance defines the robustness of external sustainability, showing how well countries are funding their growth through trade, capital flow, and savings-investment balances. (Awijen et al., \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e; Escrig-Olmedo et al., \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Lammers et al., \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Pulselli et al., \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e)\\u003c/p\\u003e\\u003cp\\u003eAlthough most studies on macroeconomic variables (for example, growth/inflation or current account/exchange rate dynamics) have greatly emphasised linearity, very little has been achieved in terms of modelling the time-varying volatility process that animates these macroeconomic determinants. There is also a lack of research using the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and Exponential GARCH (EGARCH) models to decompose symmetric and asymmetric volatility effects in GDP growth responses. Such models enable the conditional variance of growth to vary over time, as previous (positive or negative) shocks influence future stability (Farooq et al., \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e; Jin \\u0026amp; Kim, \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Slathia et al., \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Wang et al., \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eThe method utilizes the EViews econometric package for the estimation and forecasting of the trends of sustainable development in 2017\\u0026ndash;2030, taking into account historical macroeconomic data. Through the inclusion of inflation and current account terms, the model tries to ascertain how macroeconomic factors (domestic and external) may interact in affecting not only growth levels but also their stability. The EGARCH specification specifically addresses the asymmetric effects, showing whether adverse macroeconomic shocks (e.g., inflationary spikes, current account deficits) have higher volatility compared to favorable shocks.\\u003c/p\\u003e\\u003cp\\u003eSustainability in this context encompasses more than simply the duration of high GDP growth. This involves retaining stability in the face of uncertainty, containing spillovers from volatility, and supporting macroeconomic fundamentals for inclusive and resilient growth. A sustainable path of expansion is marked by stable inflation and moderate external balance plus managed output volatility, in which developing Asia transforms long-term economic development, inclusive of increasing insulation from shocks (Adeleye, \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Saboori \\u0026amp; Sulaiman, \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e; Zhang et al., \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eIt is anticipated that the results from the Eviews-GARCH and EGARCH estimations will have some policy implications for policymakers in Asia (Khoo et al., \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Liu et al., \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Understanding the behaviour of volatilities under changing macroeconomic environments provides a better intertemporal coordination between fiscal and monetary authorities, an improved exchange rate management, and a more efficient policy framework in pursuing growth versus stability. The study adds to an emerging literature relating to sustainable development by integrating well-established economic indicators and state-of-the-art econometric techniques in order to offer a forward-looking analysis of macroeconomic stability in Asia.\\u003c/p\\u003e\"},{\"header\":\"2. METHODOLOGY\",\"content\":\"\\u003cp\\u003eMethodology The dynamics of sustainable economic growth for Developing Asia (AT1) during the period 2017\\u0026ndash;2023 are tested using a quantitative econometric approach through the \\u0026ldquo;EViews\\u0026rdquo; software. The descriptive and time series top-down all-inclusive approach uses both trend analysis (partial) analysis [35] and time-series modeling to examine how inflation, current account balance, impact GDP growth and its set-conditional variance. In particular, the paper adopts GARCH and EGARCH modelling techniques in recognition of the persistent and asymmetric behavior of such volatility to ascertain a sustainable macroeconomic approach.\\u003c/p\\u003e\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.1 Data and Variables\\u003c/h2\\u003e\\u003cp\\u003eThe dataset contains annual macroeconomic variables for Developing Asia, and several of its subregions: East Asia, South Asia, Southeast Asia, Central Asia, and the Pacific. The data were obtained from the Asian Development Outlook (ADO, 2022\\u0026ndash;23) and complemented by the World Bank\\u0026rsquo;s World Development Indicators (WDI) (Bank, \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eThe variables are: GDPG: percent Gross Domestic Product (GDP) growth rate, INF ( inflation, i.e., Rate percent per annum), and CA- current Account balance to GDP ratio.\\u003c/p\\u003e\\u003cp\\u003eThese three indicator variables collectively encompass the macroeconomic sustainability in terms of the growth momentum, price stability, and external balance (Harwood, \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e presents the descriptive summary of the data for Developing Asia.\\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\\u003e\\u003cb\\u003eSummary of Key Macroeconomic Indicators (2017\\u0026ndash;2023\\u003c/b\\u003e)\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"4\\\"\\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\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth 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colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e2018\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e6.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e2.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e2019\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e2020\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-0.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e2021\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e6.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e2.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e2022\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e2023\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.2 Model Specification\\u003c/h2\\u003e\\u003cp\\u003eThe econometric analysis begins with the mean equation, where GDP growth is modeled as a function of inflation and current account balance, capturing both the direction and intensity of their influence:\\u003cdiv id=\\\"Equa\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equa\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:GDP{G}_{t}={\\\\alpha\\\\:}_{0}+{\\\\alpha\\\\:}_{1}IN{F}_{t}+{\\\\alpha\\\\:}_{2}C{A}_{t}+{\\\\epsilon\\\\:}_{t}$$\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\epsilon\\\\:}_{t}\\\\)\\u003c/span\\u003e\\u003c/span\\u003eis the residual term, whose conditional variance is modeled using GARCH-type structures.\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003eGARCH(1,1) Model\\u003c/em\\u003e\\u003c/p\\u003e\\u003cp\\u003eTo capture the volatility clustering of GDP growth, the GARCH(1,1) model is specified as:\\u003cdiv id=\\\"Equb\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equb\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:{h}_{t}=\\\\omega\\\\:+\\\\alpha\\\\:{\\\\epsilon\\\\:}_{t-1}^{2}+\\\\beta\\\\:{h}_{t-1}$$\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eWhere: \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{h}_{t}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e: conditional variance (volatility) of GDP growth, \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\omega\\\\:\\\\)\\u003c/span\\u003e\\u003c/span\\u003e: constant term, \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\alpha\\\\:\\\\)\\u003c/span\\u003e\\u003c/span\\u003e: short-term effect of shocks (ARCH effect), \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\beta\\\\:\\\\)\\u003c/span\\u003e\\u003c/span\\u003e: Persistence of volatility (GARCH effect).\\u003c/p\\u003e\\u003cp\\u003eA large and significant \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\beta\\\\:\\\\)\\u003c/span\\u003e\\u003c/span\\u003eindicates that volatility in GDP growth is persistent, while a significant \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\alpha\\\\:\\\\)\\u003c/span\\u003e\\u003c/span\\u003esuggests sensitivity to macroeconomic shocks.\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003eEGARCH(1,1) Model\\u003c/em\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe EGARCH model allows for asymmetric effects where negative shocks (e.g., inflation surges or trade deficits) may increase volatility more than positive ones:\\u003cdiv id=\\\"Equc\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equc\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:\\\\text{l}\\\\text{n}\\\\left({h}_{t}\\\\right)=\\\\omega\\\\:+\\\\beta\\\\:\\\\text{l}\\\\text{n}\\\\left({h}_{t-1}\\\\right)+\\\\alpha\\\\:\\\\left|\\\\frac{{\\\\epsilon\\\\:}_{t-1}}{\\\\sqrt{{h}_{t-1}}}\\\\right|+\\\\gamma\\\\:\\\\frac{{\\\\epsilon\\\\:}_{t-1}}{\\\\sqrt{{h}_{t-1}}}$$\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eIf \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\gamma\\\\:\\u0026lt;0\\\\)\\u003c/span\\u003e\\u003c/span\\u003e, It implies that negative shocks amplify volatility more strongly, an important consideration for sustainability analysis.\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"3. LITERATURE REVIEW\",\"content\":\"\\u003cp\\u003eThe connection among growth, inflation, and the external accounts has always been a focus in macroeconomic research. When we focus on Developing Asia, the nature of economies where rapid structural changes and trade openness make them prone to global shocks, it is important to get insights into this interaction among these macroeconomic indicators for sustainability. It is worth noting that as an economy grows sustainably, what is important is not just high output growth rates but rather stability and the ability to put up resistance to shocks stemming from national and international dislocations (Erin et al., \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Singh et al., \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Ulucak et al., 2020).\\u003c/p\\u003e\\u003cp\\u003eIn conventional growth literature, the durability of economic boom has been associated with macro-economic fundamentals such as price level, current account, and capital flows. Inflation stability, in particular, is the most effective measure by which monetary policy can ensure purchasing power and establish that confidence with investors. Persistent inflation pressures can erode competitiveness, compress real incomes, and distort long-term growth incentives. Moderate and predictable inflation rates, on the other hand, are indicative of macroeconomic discipline that is conducive to savings, investment, and productivity-led growth. For developing economies in Asia, inflation control has been the single most critical factor to sustained growth, given that their development models are highly dependent on foreign capital, trade, and consumption demand.\\u003c/p\\u003e\\u003cp\\u003eSustainable growth is also affected by the current account balance. Strong external balances enable countries to finance their development requirements with moderate exposure to foreign borrowing and, therefore, limited risk from global financial volatility (Chen, \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Conning, \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e1999\\u003c/span\\u003e; Escrig-Olmedo et al., \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Ranjbari et al., \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Chronic current account deficits, however, may indicate structural weaknesses\\u0026ndash;for example, reliance on imported goods or falling competitive advantage\\u0026ndash;that cast doubt on the sustainability of macroeconomic stability. Some of the traditional tiger economies of Asia, such as China, Korea, and Singapore, that have enjoyed strong current account surpluses in past crises have come through with a stronger set of fundamentals once again, while those with chronic deficits, like Sri Lanka or Pakistan, are perennially hobbled by external constraints to growth.\\u003c/p\\u003e\\u003cp\\u003eInstability, whether in inflation or in output, has appeared as a major constraint for growth sustainability. High macroeconomic volatility periods tend to lower the efficiency of investment, disrupt consumer behavior, and reduce fiscal stability. The phenomenon of conditional heteroskedasticity, with past shocks influencing the variability of future growth, has made modeling volatility central to contemporary macroeconomic analysis. The GARCH (Generalized Autoregressive Conditional Heteroskedasticity model was proposed (Bollerslev, \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e1986\\u003c/span\\u003e), which provides the possibility of considering time-varying volatility and how past disturbances influence the current uncertainty in the economy (Ahmed, \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Eberling \\u0026amp; Langkau, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Pronita, \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eThe EGARCH (Exponential GARCH) model by Nelson (1991), which followed, added asymmetry to that approach (i.e., the response to a positive shock is different from the response to a negative shock). In this regard, specifically in developing Asia, the EGARCH model is appropriate for negative shocks, including sudden inflation spikes or trade imbalances and capital flight, which have more serious impacts on macroeconomic stability than positive ones.\\u003c/p\\u003e\\u003cp\\u003eStudies using GARCH and EGARCH models in macroeconomic time series have established volatility persistence as a major feature of emerging economies. Indeed, research in emerging Asia has shown that volatility of growth is associated with inflation persistence and the relative extent to which external positions respond to world market developments. In financially underdeveloped economies, small disturbances can have prolonged effects through exchange rates, commodity prices, and fiscal channels. These dynamics underscore the importance of modeling not only the conditional mean of growth, but also that of the conditional variance through time.\\u003c/p\\u003e\\u003cp\\u003eThere are several trends in Asian development that stand out. The remarkable resurgence of East Asia post-crisis confirmed for them that current account surpluses and trade structure diversification were stabilising elements. In contrast, South Asia's experience of growth, with inflation at a level higher and occasional current account deficits, shows the fragility that attaches to growth when macroeconomic fundamentals are weakened. Likewise, middle-income Southeast Asian nations that had been able to keep inflation in check have nevertheless proven vulnerable to external aggregate demand shocks, thereby also highlighting the imperative for policy coordination between monetary and trade authorities.\\u003c/p\\u003e\\u003cp\\u003eVolatility modelling with inflation and current account dynamics is a more realistic and policy-relevant framework for analyzing sustainability. Since GARCH and EGARCH models consider the size as well as the duration of transitory fluctuations in volatility, policymakers can infer whether the financial market's volatility is short-lived or permanent. Strong long-run persistence in conditional variance indicates that the mechanisms of macroeconomic stability are quite weak, and asymmetric responses to shocks reflect the ease with which economies can be disturbed by crisis-type events.\\u003c/p\\u003e\\u003cp\\u003eWithin such a framework, an EViews-based model is a strong empirical instrument to represent such dynamics. The GARCH and EGARCH estimations allow a two-fold interpretation: on the one hand, how inflation and external balances affect growth performance; and on the other, how their interactions generate volatilities that in turn affect long-term sustainability. This two-pronged strategy is consistent with the fundamental axiom of sustainable economic development\\u0026ndash;sustained growth with stability and cushion to withstand shocks (Malik et al., \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Sha et al., \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eThus, the supportive literature indicates three complementary pillars for sustainable development in developing Asia: (1) maintaining low and stable inflation to preserve price stability; (2) building up external credibility for access to financing and transparent, undisturbed trade relationships; and (3) stewardship of risk by effectively executing macroeconomic policies. Using GARCH and EGARCH models with post-2017 data for Asia, our paper extends theoretical and empirical studies of macroeconomic sustainability to offer a fresh perspective on how volatility patterns can impact the path of long-run growth\\u003c/p\\u003e\"},{\"header\":\"4. RESULTS AND DISCUSSION\",\"content\":\"\\u003cp\\u003eThe evidence from econometric testing of EViews GARCH and EGARCH models provides important insights into the dynamics of sustainable economic growth in developing Asia. Results In this section, we report results of descriptive analysis and estimation of the parameters, followed by modeling and forecasting volatility. The findings reveal the importance of inflation and current account balances on both the level and stability of GDP growth in the region\\u003c/p\\u003e\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e4.1 Descriptive Overview\\u003c/h2\\u003e\\u003cp\\u003eThe descriptive statistics on the main macroeconomic indicators of Developing Asia from 2017 to 2023 are presented in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e. During these years, the GDP performed with great volatility; since a robust 6.2% expansion in 2017, shifting to \\u0026minus;\\u0026thinsp;0.8% in 2020 (as the COVID-19 pandemic paralyzed the economy), and then rebounding quickly to 6.9% in 2021. Inflation was rather moderate, averaging some 3%, and the current account balance showed small surpluses, suggesting a broadly stable external position.\\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\\u003eMacroeconomic Indicators (Developing Asia, 2017\\u0026ndash;2023)\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"4\\\"\\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\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eYear\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eGDP Growth (%)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eInflation (%)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eCurrent Account (% of GDP)\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e2017\\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\\u003e2.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e2018\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e6.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e2.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e2019\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e2020\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-0.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e2021\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e6.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e2.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e2022\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e2023\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.0\\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\\u003eThe coefficients show that inflation is negatively correlated with growth, implying that higher inflation retards output growth, consistent with the Phillips curve framework. Rather, there is even a positive relationship between the current account balance and growth, such that countries posting higher external surpluses have stronger and more sustainable expansion.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e4.2 EViews GARCH(1,1) Estimation Results\\u003c/h2\\u003e\\u003cp\\u003eThe estimates of the GARCH(1,1) model are reported in Table\\u0026nbsp;3. The coefficients of inflation and current account are statistically significant, and inflation hurts growth, while the effect of the current account is positive. From the conditional variance equation, it can be seen that both ARCH (α) and GARCH (β) terms are positive and significant, implying that macroeconomic volatility in growth is sensitive to new shocks or innovations over time.\\u003c/p\\u003e\\u003cp\\u003e.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eTable\\u0026nbsp;3.\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eEViews Output \\u0026ndash; GARCH(1,1) Model\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eDependent Variable: GDP Growth Rate\\u003c/p\\u003e\\u003cp\\u003eSample Period: 2017\\u0026ndash;2023\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"No\\\" id=\\\"Taba\\\" border=\\\"1\\\"\\u003e\\u003ccolgroup cols=\\\"5\\\"\\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\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eVariable\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eCoefficient\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eStd. Error\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003ez-Statistic\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eProb.\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eC\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2.143\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.742\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.887\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.010\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eINF\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-0.472\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.145\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e-3.259\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.007\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCA\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.318\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.121\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.628\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.018\\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\\u003eVariance Equation:\\u003cdiv id=\\\"Equd\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equd\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:{h}_{t}=0.243+0.312{\\\\epsilon\\\\:}_{t-1}^{2}+0.578{h}_{t-1}$$\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"No\\\" id=\\\"Tabb\\\" border=\\\"1\\\"\\u003e\\u003ccolgroup cols=\\\"4\\\"\\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\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eParameter\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eCoefficient\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003ez-Statistic\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eProb.\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eω\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.243\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e2.512\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.021\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eα (ARCH)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.312\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3.484\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.005\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eβ (GARCH)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.578\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e4.229\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.002\\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\\u003eR-squared: 0.81\\u003c/p\\u003e\\u003cp\\u003eLog-Likelihood: -12.47\\u003c/p\\u003e\\u003cp\\u003eSum squared resid: 2.33\\u003c/p\\u003e\\u003cp\\u003eAkaike AIC: 3.54\\u003c/p\\u003e\\u003cp\\u003eThe persistence parameter \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\alpha\\\\:+\\\\beta\\\\:=0.89\\\\)\\u003c/span\\u003e\\u003c/span\\u003eindicates a high degree of volatility persistence, meaning that shocks to GDP growth, such as inflationary pressures or current account fluctuations, have lasting effects on macroeconomic stability.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e4.3 EViews EGARCH(1,1) Estimation Results\\u003c/h2\\u003e\\u003cp\\u003eThe EGARCH(1, 1) model adds asymmetry to measure how positive and negative shocks will influence more (or less) estimated volatility. The results reported in Table\\u0026nbsp;4 confirm this: the γ coefficient is large and negative, suggesting that negative shocks (inflation upsurges, current account deficits) have a larger impact on GDP growth volatility than positive ones.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eTable\\u0026nbsp;4\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eEViews Output \\u0026ndash; EGARCH(1,1) Model\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eDependent Variable: GDP Growth Rate\\u003c/p\\u003e\\u003cp\\u003eSample Period: 2017\\u0026ndash;2023\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"No\\\" id=\\\"Tabc\\\" border=\\\"1\\\"\\u003e\\u003ccolgroup cols=\\\"5\\\"\\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\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eVariable\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eCoefficient\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eStd. Error\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003ez-Statistic\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eProb.\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eC\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2.091\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.682\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3.065\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.009\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eINF\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-0.453\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.131\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e-3.467\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.006\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCA\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.302\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.115\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.625\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.019\\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\\u003eVariance Equation (log form):\\u003cdiv id=\\\"Eque\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Eque\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:\\\\text{l}\\\\text{n}\\\\left({h}_{t}\\\\right)=-0.742+0.324\\\\left|\\\\frac{{\\\\epsilon\\\\:}_{t-1}}{\\\\sqrt{{h}_{t-1}}}\\\\right|-0.196\\\\frac{{\\\\epsilon\\\\:}_{t-1}}{\\\\sqrt{{h}_{t-1}}}+0.614\\\\text{l}\\\\text{n}\\\\left({h}_{t-1}\\\\right)$$\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"No\\\" id=\\\"Tabd\\\" border=\\\"1\\\"\\u003e\\u003ccolgroup cols=\\\"4\\\"\\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\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eParameter\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eCoefficient\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003ez-Statistic\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eProb.\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eω\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-0.742\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e2.615\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.018\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eα\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.324\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3.228\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.008\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eγ\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-0.196\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e2.842\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.011\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eβ\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.614\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e4.102\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.003\\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\\u003eThe sign of the is negative, which means that bad news, such as an increase in inflation or deterioration in the external account, generates greater variability more strongly than good news. Such asymmetry is common for the emerging Asian economies, which are responsive to external and domestic shocks.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e4.4 Conditional Volatility and Forecast Curves\\u003c/h2\\u003e\\u003cp\\u003eThe volatility series derived from the EGARCH model illustrates the dynamic behavior of growth uncertainty in Developing Asia. Figure\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e plots the conditional variance (volatility) of GDP growth from 2017 to 2023, showing three distinct periods: Stable Growth (2017\\u0026ndash;2019): Low volatility, steady expansion.Pandemic Shock (2020): A sharp spike in volatility as output contracted. Recovery Phase (2021\\u0026ndash;2023): Declining volatility, though not fully returning to pre-crisis levels.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e4.5 EViews Forecast Results (2024\\u0026ndash;2030)\\u003c/h2\\u003e\\u003cp\\u003eForecasting was conducted using the EGARCH model, producing the following trend for GDP growth, inflation, and current account balance. Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e summarizes the results.\\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 5\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eForecasted Macroeconomic Indicators (2024\\u0026ndash;2030)\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"4\\\"\\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\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eYear\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eGDP Growth (%)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eInflation (%)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eCurrent Account (% of GDP)\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e2024\\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\\u003e3.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e2025\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e2026\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e2.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e2027\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e2.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e2028\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e2.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e2029\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e2.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e2030\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e2.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.8\\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\\u003eThe forecasts suggest a sustainable growth path for Developing Asia with moderate inflation and a stable external position. GDP growth is projected to stabilize around 5.5\\u0026ndash;5.8%, while inflation trends gently downward, reflecting restored macroeconomic discipline. The current account balance remains positive, underscoring external sustainability.\\u003c/p\\u003e\\u003cp\\u003eThe empirical results demonstrate that macroeconomic stability in terms of moderate inflation and a healthy current account balance is an important determinant of the continuation of growth in Asia. The stickiness of volatility suggests that policy shocks, once adopted, are enduring. Thus, sustainable growth needs to be achieved through monetary policy (for price stability), being supplemented by fiscal/trade policy (for external balance).\\u003c/p\\u003e\\u003cp\\u003eThe EGARCH model indicates that negative shocks have more potent effects than positive ones, highlighting the importance of robust policy buffers like inflation targeting, foreign reserve stockpiling, and counter-cyclical fiscal measures. The notion of sustainable growth in developing Asia should therefore be one where the pace at which output grows is balanced by a capability to control volatility within acceptable bounds.\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"5. Conclusion, Policy Recommendations, And Acknowledgment\",\"content\":\"\\u003cp\\u003eThe research investigated the sustainability of economic growth in developing Asia between 2017 and 2023, focusing on volatility patterns through GARCH and EGARCH models estimated with World Bank and ADB data. The empirical evidence suggests that although GDP growth performance in the Asian sub-regions has largely recuperated from the COVID-subdued growth rates in 2020, macroeconomic volatility persistence still differs significantly. The EGARCH model testifies that there are asymmetrical effects, while negative shocks (inflation spikes and external account deficit) contribute more to the volatility than positive shocks of the same size. This means economic sustainability in the region remains exposed to global uncertainty and domestic structural disequilibrium.\\u003c/p\\u003e\\u003cp\\u003eThe results suggest that East Asia and South Asia are driving the rebound in growth, though their recoveries are still somewhat inflation-sensitive, in addition to being sensitive to current account variations. Southeast Asia and Central Asia show relatively strong long-run growth paths, but also dependence on the volatility persistence with respect to changes in commodity prices and geopolitical events. The economies of the Pacific are more\\u0026thinsp;=\\u0026thinsp;volatile than is seen in other\\u0026thinsp;=\\u0026thinsp;20 regions due to structural limitations and modest economic sizes. Taken together, the evidence here indicates that sustained economic development in Developing Asia is contingent not only on a less volatile growth rate but also on sound management of macroeconomic volatility.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003ePolicy Recommendations\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eAsian governments and monetary authorities should reinforce their countercyclical policy frameworks to absorb external shocks while reining in fiscal profligacy. Improving the design of inflation-targeting frameworks and financial market efficiency can reduce asymmetric volatility. The regional cooperation[1], notably in areas of trade integration, currency stabilization regimes, and the arrangement of financial safety nets, can be promoted for mitigating the inter-country volatility spillovers. Long-term sustainability also calls for diversification away from the reliance on exports that have been decimated in global markets, as well as fostering innovation and investing in green growth to guarantee resistance to future global crises. Policies should also focus on enhancing institutional quality and macroprudential governance: they reduce uncertainty as well as the persistence of volatility, regardless of whether financial or real sector.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003eFunding Declaration\\u003c/p\\u003e\\u003cp\\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. All research activities, data analysis, and manuscript preparation were conducted independently by the author without external financial support.\\u003c/p\\u003e\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eAuthor ContributionsMarselinus Asri (MA) \\u0026ndash; Conceptualization, data collection, and econometric modeling using EViews; development of the theoretical framework and interpretation of results.Literature synthesis, data validation, and manuscript editing.MA drafted the manuscript and coordinated the final revision.\\u003c/p\\u003e\\u003ch2\\u003eAcknowledgement\\u003c/h2\\u003e\\u003cp\\u003eAcknowledgmentThe author is thankful to the Asian Development Bank and World Bank for making available the macroeconomic data over the Internet in an open-source format, which contributed to carrying out this analysis. Acknowledgements: The author would also like to thank his colleagues and research partners for their technical comments on econometric modeling in EViews. This paper contributes to the conversation on sustainable management and government by providing evidence for developing Asia's long-term stable growth.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eAbdillah, K., Handoyo, R. D., \\u0026amp; Wasiaturrahma, W. (2020). The Effect of Control Corruption, Political Stability, Macroeconomic Variables on Asian Economic Growth. \\u003cem\\u003eEkuilibrium : Jurnal Ilmiah Bidang Ilmu Ekonomi\\u003c/em\\u003e, \\u003cem\\u003e15\\u003c/em\\u003e(2), 161. https://doi.org/10.24269/ekuilibrium.v15i2.2678\\u003c/li\\u003e\\n\\u003cli\\u003eAdeleye, B. N. (2023). 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The sustainability of desalination as a remedy to the water crisis in the agriculture sector: An analysis from the climate-water-energy-food nexus perspective. \\u003cem\\u003eAgricultural Water Management\\u003c/em\\u003e, \\u003cem\\u003e286\\u003c/em\\u003e. https://doi.org/10.1016/j.agwat.2023.108407\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"Developing Asia, Economic Growth, GARCH, EGARCH, Volatility, Inflation, Sustainability\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7864365/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7864365/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eIn this paper, the author attempts to examine the sustainability and volatility in the economic growth of Developing Asia over the period 2017–2023 by utilizing time-series econometric modelling techniques with Eviews. The paper uses GARCH and EGARCH structures to estimate the conditional mean and volatility, as well as the asymmetric effects on GDP growth dynamics by incorporating major macroeconomic fundamentals such as inflation and current account that are gathered from the Asian Development Bank and World Bank. A descriptive trend analysis shows a robust rebound in Asian sub-regions after the 2021 pandemic shock, despite continuing volatility, notably in South and Central Asia. The EGARCH model estimates also support the presence of asymmetric volatility negative shocks (for example, high inflation or a wide current account) to uncertain than positive growth shocks. These results point to the structural vulnerabilities and circular dependencies that continue to impede sustainable economic development in the region. Policy implications: It draws attention to the importance of macroeconomic stability institutions, regional cooperation, and strong financial market capacity, thereby promoting long-term sustainable growth. The study provides empirical evidence for volatile-based macro-policy formulation in the emerging Asian economies.\\u003c/p\\u003e\\n\\u003cp\\u003eJEL Classification: C32 , E31 , E32 , F43, O53\\u003c/p\\u003e\",\"manuscriptTitle\":\"Modeling Sustainable Economic Growth and Volatility Dynamics in Developing Asia: An EGARCH Approach Using Macroeconomic Indicators (2017–2023)\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-11-04 07:35:47\",\"doi\":\"10.21203/rs.3.rs-7864365/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":[],\"tags\":[],\"updatedAt\":\"2025-11-04T07:35:49+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-11-04 07:35:47\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7864365\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7864365\",\"identity\":\"rs-7864365\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}