Macroeconomic Volatility and Forecasting Growth in Developing Asia: An EViews GARCH - EGARCH Approach

Macroeconomic Volatility and Forecasting Growth in Developing Asia: An EViews GARCH - EGARCH Approach | 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 Macroeconomic Volatility and Forecasting Growth in Developing Asia: An EViews GARCH - EGARCH Approach Marselinus Asri This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8004663/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 This paper uses descriptive analysis and econometric modeling on the relationship between growth, inflation, and macroeconomic volatility in Developing Asia from 2017 to 2023 by utilizing Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and Exponential GARCH (EGARCH) estimation frameworks of EViews. Using annual panel data taken from the Asian Development Outlook (ADB, 2023) and the World Bank, the study investigates the conditional volatility of GDP growth, inflation, and current account behavior in major sub-regions—East Asia, South Asia, Southeast Asia, Central Asia, and the Pacific. The empirical findings show that the growth path of developing Asia is robust but exhibits high volatility clustering, especially during the 2020 pandemic shock. The GARCH(1, 1) model reveals that the degree of persistence in output volatility is very high, implying that economic growth uncertainty tends to be sustained at a high level after large shocks. In the meantime, EGARCH(1,1) estimates suggest considerable asymmetry; negative shocks (such as an increase in commodity price pass-throughs, currency pressures, global demand slowdown, etc.) generate higher excess volatility compared with positive shocks. These results also hint at the fact that the macroeconomic conditions of the region are characterized by structural weaknesses and instances of enhanced susceptibility to negative external developments. Policy implications emphasise the need to bolster fiscal resilience, implement credible and forward-looking monetary policies, and improve regional financial coordination toward mitigating fluctuations while supporting growth. They highlight that the long-term success of Asia will not just be determined by its rate of growth, but by its ability to manage and anticipate volatility through proactive macroeconomic management. By using GARCH–EGARCH analysis combined with regional data patterns, our paper provides new insights into the twin objectives of high growth and stability in developing Asian economies. JEL Classification:C22,E31,E32, F43,O47,O53 Macroeconomics Economic Growth Inflation Volatility GARCH Model EGARCH Model Figures Figure 1 Figure 2 Figure 3 1. Introduction The pattern of growth in Developing Asia has been so resilient over the past decade, and at the same time so volatile. The region, with a variety of economies from East, South, Southeast, and Central Asia to the Pacific among its members, maintained some of the highest growth rates in the world, averaging about 5–6 percent per year prior to COVID-19. But the 2017-23 horizon was marked by disorderly output contractions, uneven recoveries, and increased inflationary pressures in a macroeconomic environment full of idiosyncrasies. These booms and their ebbs highlight the increasing need to understand both why growth is volatile and how macroeconomic stability helps sustain future expansion (Bahal & Lenzo, 2023 ). The COVID-19 pandemic brought with it a synchronized slowdown in the economy in 2020. GDP growth was brought down dramatically for Developing Asia, from 5.0 per cent in 2019 to negative 0.8 per cent in 2020, the first regional contraction since the early sixties of the last century (Demiralay et al., 2021 ; Keeni, 2025 ; Li et al., 2024 ; Sassi & Trital, 2023 ). Although output rebounded strongly in 2021, averaging 6.9 percent, the years that followed displayed moderation with a backdrop of tightening global financial conditions, supply chain disruptions, and geopolitical tensions. Inflation, meanwhile, long dormant in the region, quickened as well, picking up from 2.5 percent in 2021 to 3.7 percent in 2022 on account of global commodity price shocks and domestic demand recovery. Those dynamics would indicate that growth and inflation may not only be cyclical but also reflect volatility clustering, in which large shocks are followed by additional fluctuations in output (Coers & Sanders, 2013 ; Huang et al., 2008 ; Saldivia et al., 2020 ). The theoretical literature on growth and volatility has identified a number of structural factors that may magnify or reduce fluctuations. On the other hand, macroeconomic uncertainties may also impede long-run growth through their negative effect on investment decisions, on the ambiguity about prospects, or on the allocation of resources (Jiao et al., 2024 ; Ma et al., 2024 ; Saleh, 2023 ). On the other hand, moderate volatility could reflect dynamism and adaptation, especially in emerging markets experiencing structural change (Imbs, 2007). In Asia, where growth dynamics are more diverse (industrial diversification in East Asia, service–led expansion in South Asia, dependence on commodities in Central Asia, and small-state vulnerability in the Pacific), the roots of volatility are multiple (Fang et al., 2025 ; Ma et al., 2024 ; Zhang et al., 2019 ). External shocks, whether emanating from global demand or commodity prices, interact with domestic policy regimes and institutional quality in shaping both the depth and duration of growth cycles (Singh et al., 2022 ; Wu et al., 2017 ). The empirical literature has developed a growing interest in the time-varying behavior of macroeconomic uncertainty in the social sciences and economics, with studies examining various conditional volatility models, including Generalized Autoregressive Conditional Heteroskedasticity (GARCH) type specifications. These models, which have their roots in financial returns, have been found useful for macroeconomic applications such as output growth, inflation, and current account dynamics (Caporale & McKiernan, 1998; Henry & Olekalns, 2002). The GARCH(1; 1) specification allows for the volatility to depend on past disturbances and past volatility, and hence can accommodate the \memory" effect present in much macroeconometric data. A second variant, the Exponential GARCH (EGARCH), proposed by Nelson (1991), also accommodates asymmetry where negative shocks (e.g., output declines and external imbalances) may instigate greater volatility than positive ones. Such asymmetry is especially important for the emerging market economies, where negative shocks typically produce more readily overt policy or market responses than positive ones (Effendi et al., 2024 ; Izati et al., 2024 ; Khoo et al., 2024 ; Künzi, n.d.; Liu et al., 2024 ). Given the importance of volatility for both macroeconomic performance and policy decisions, there are still very few empirical applications of GARCH-type models to macroeconomic aggregates in Developing Asia (Asri & Limpo, 2024 ). The vast majority of the literature is either concerned with financial markets or inflation dynamics in a given country, where regional investigations of macroeconomic volatility over regional units have scarcely been approached by high-frequency econometric tools. And, in the post-pandemic macro environment of Asia as well, new complexities have emerged along three fronts: global liquidity tightening and changing GVCs; shifting towards domestic policy change with fiscal consolidation measures and a push for green growth. On this background, a sound framework based on volatility is needed to capture growth dynamics and measure uncertainty, both in terms of level and the future course (Fu, 2009 ; Nath & Brooks, 2015 ). To fill that gap, this paper uses the GARCH(1,1) and EGARCH(1,1) models estimated by EViews to study the volatility of GDP growth and external sector performance for Developing Asia as a whole aggregate from 2017 to 2023. Since it considers the conditional variance forecasted, time-varying volatility analysis provides more revealing results compared to traditional constant variance regressions. Measure the degree of persistence of volatility in regional GDP growth through GARCH modelling. Analyze asymmetric volatility effects in the current account balance by means of EGARCH. Compute short-run (2024–2026) point predictions with the corresponding uncertainty bands according to conditional variance. Our dataset, extracted from ADB & WDI, includes key macroeconomic variables such as GDP growth, inflation, and current account balances for the major sub-regions East Asia, South Asia, Southeast Asia, Central Asia, and the Pacific. Using annual data is in line with macroeconomic analysis but imposes the constraint of relatively small sample sizes, a limitation that we address by using pooled regional averages. In terms of methodology, the use of EViews enables us to reproduce estimation procedures – unit root testing, mean equation specification (with AR components), GARCH and EGARCH estimation, and forecasting. The model reconciles persistence (α + β) and asymmetric responses (γ) too. All of these features together adjust how shocks to growth or external balances unfold over time, and let bad outcomes induce disproportionately more uncertainty. The significance of the modelling of volatility in Asia is not merely a statistical interest. Consistent cyclicality in growth can deepen fiscal instability, push up the cost of borrowing, and curb confidence among private investors. Asymmetric external volatility can amplify vulnerabilities in the current account, making economies susceptible to sudden stops or reversals of capital flows. An understanding of such dynamics is critical for the design of macroeconomic policies, especially in the case of central banks and ministries of finance that seek to strike a balance between stimulating growth and maintaining financial stability (Nath & Brooks, 2015 ; Tzang et al., 2015 ). The empirical evidence, subject to the limitations due to short sample periods, supports three key messages. First, sensitivity to macroeconomic shocks like those due to the pandemic or commodity price swings tends to be highly persistent for Developing Asia. Second, external balances show substantial propensities to asymmetric volatility; that is, negative shocks (e.g., in the form of current account deficits) tend to prompt larger index expectations variability than positive ones (surpluses). The third reason is that if one takes account of conditional variance when forming forecasts, forecasting accuracy is enhanced, and it also offers a more realistic view of situations of economic uncertainty than a pure deterministic model. In sum, this paper also adds to the accumulating wealth of empirical studies on macroeconomic volatility by offering a regional, GARCH-contingent view on Developing Asia (Kacou et al., 2022 ; Layne & Lee, 2001 ; Umemiya et al., 2017 ; Zahonogo, 2018 ). By revealing how volatility models can be used to predict growth and policy in a world of rapid change, the book shows how to push the boundaries of what is possible in an ever-expanding economic universe. The findings suggest that sustainable development level in Asia requires high growth rates, not only, but stable and predictable macroeconomic environments as well. Resilience to volatility through diversification, fiscal discipline, and institutional strength will be especially important for preserving long-term economic prospects in a challenging global environment. 2. Methodology 2. 1 Data We use the annual series 2017–2023: GDP growth (% per year) Table A(Developing Asia aggregate and subregions/countries); Inflation (% per year) Table A (inflation by country/subregion); Current account balance (% of GDP) Table A1 (used for EGARCH) (Development Bank, 2023 ) (Development Bank, 2025 ). Data source: Asian Development Bank (ADB, 2023) and World Bank WDI. The paper focuses on regional aggregates (Developing Asia and the five subregions) and shows examples for the regional aggregate series. 2.2 Model set-up Two series are modelled: GDP growth, modeled with a conditional mean and GARCH(1,1) variance to capture volatility clustering: yt = µ + εt,εt∣It − 1∼N(0,σt2)y_t We estimate models for regional aggregates (Developing Asia, East Asia, South Asia, Southeast Asia, Central Asia, Pacific). Short sample (7 annual observations) is a limitation; results are indicative, and better precision is obtained with higher-frequency data. 3. Literature Review Economic growth and inflation are two key measures of macroeconomic performance, and in developing Asian economies, the relationship between their resilience and susceptibility can be observed. Asia has been the fastest-growing region globally for over a decade, mainly due to industrial growth, technology improvements, and rising regional co-operation. But it has been a rocky path to this expansion. Periods of inflation, external shocks, and policy shifts have produced fluxes that expose deeper structural forces. Characterizing the way that growth and inflation interact through time, and the way in which they are volatile, is crucial to be able to evaluate macroeconomic stability as well as for economic policy-making purposes (Elfaki & Ahmed, 2024 ; Frankel & Romer, 1999). The developing Asia’s growth is typically highly dynamic, but it also involves marked cyclical swings (Burki & Tahir, 2022 ; Yuan & Wang, 2014 ). Fast stretches of growth will usually be met with corrections driven by shifts in global demand, energy prices, or domestic policy tightening. It serves as a useful benchmark, which ideally embodies stability and moderation: Restraint during booms and stimulus during busts. High growth coupled with subdued inflation is an important goal for policymakers, yet for many emerging market economies, inflationary pressures can rise rapidly if growth were to take off above potential. This is consistent with Asia's mixed pattern and thus is a good candidate for modeling volatility using econometric models that capture the persistence and asymmetry of volatility over time (Kinda et al., 2023 ; Nunkoo et al., 2018 ; Rayegan, 2012 ). Volatility in macroeconomic series, such as growth and inflation, does not happen randomly—it is clumpy. Calm phases are also succeeded by noisy ones, a behavior that standard constant variance models can not describe. To model this phenomenon, volatility models such as the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and its exponential version (EGARCH) offer a strong literature base. The -model The GARCH model assumes that the current level of volatility is affected by lagged levels of variance and lagged shocks, which explains why uncertainty lingers even after disturbances its: Iso ch come to rest. The EGARCH model relaxes this assumption by modeling asymmetry, acknowledging the fact that negative shocks, such as recessions or sudden surges in inflation, impact volatility to a greater and more persistent extent than positive shocks of the same size (Ayana et al., 2024 ; Frankel & Romer, 1999b ; Verkijika & De Wet, 2018 ). The use of GARCH and EGARCH models for Asian macroeconomic time series provides a partial picture of the relevance of the region in understanding our results. Countries such as China, India, Indonesia, and Vietnam that have experienced high buoyancy growth rates over the period under investigation exhibit long memory, that once volatility increases, it takes time to decay. This persistence is consistent with the view that agents form expectations slowly and with the possibility that it may take some years for policy changes to bring about stability. By contrast, the Pacific and parts of Central Asia (both smaller and open) demonstrate higher levels of volatility because they are more vulnerable to spillovers from frequently very volatile external drivers such as tourism inflows, remittances, and/or commodity prices. Inflation dynamics in Asia are similarly asymmetrical when considered as an EGARCH process. When inflation becomes high due to external shocks or supply dislocations, volatility typically spikes. But as inflation falls, volatility comes down more gradually. This asymmetry means that attaining price stability is tougher after an inflation scare than after a deflation scare. For policymakers, there are two implications: First, it suggests that taming rising inflation expectations is not only a short-run challenge but also a long-run one, which reflects confidence in credibility, communication, and structural reforms. The GARCH and EGARCH designs enable researchers to measure the conditional variance of growth and inflation, the level of uncertainty likely at any given time, based on the past. By examining this conditional variance, we can forecast when risk and/or instability are higher. Applied to developing Asia for 2017–23, these models show that volatility was highest in the vicinity of 2020 during the COVID-19 pandemic before subsiding with the unlocking of economies and fiscal and monetary policy intervention to stabilize demand. Still, the rebound has been uneven. For the South Asia/Pacific regions, CCPs are more persistent than in East and Southeast Asia, resulting from variation in industrial structure as well as coordinating policies (Guo et al., 2014 ). Modelling growth and inflation volatility also provides an insight into the propagation of disturbances between them. Indeed, high volatility of inflation may be correlated with high volatility of output in many Asian countries, indicating a common contributor to macroeconomic instability that has been related to supply-side bottlenecks or external trade shocks in the literature. On the other hand, those countries that have countercyclical inflation-targeting frameworks and export structures are likely to show less volatility in both instruments. This is indicative of the fact that maintaining price stability and sustainable growth are complementary rather than contradictory goals of policy. At the econometric level, EViews GARCH and EGARCH estimates yield unambiguous magnitudes of these dynamics. The ARCH plus GARCH coefficient is often near unity in Asian data, suggesting that growth or inflation shocks have permanent effects. The EGARCH model's asymmetry parameter, usually negative, means that negative shocks (downturns or inflation spikes) induce greater increases in volatility than positive shocks. The graphical examination of the conditional variance curves also highlights that the squared observations cluster in proximity to major eventspandemic years, commodity price spikes, and global financial stress (Archibugi & Filippetti, 2017 ; Asare et al., 2025 ; Engel et al., 2018 ; Martinez-Vasquez, 2005 ). The results have significant implications for the economic governance of Asia. Ongoing fluctuations in GDP growth can undermine long-term investment and job creation (Beckmann & Czudaj, 2017 ; Kahsai et al., 2012 ; Mozumder & Marathe, 2007 ; Narayan & Smyth, 2008 ; Ozturk & Acaravci, 2011 ), whereas unstable inflation erodes purchasing power and undermines confidence in the conduct of monetary policy. By characterizing the underlying sources of volatility and measuring their persistence, GARCH–type models are an important guide to forecasting and policy making. They allow policymakers to forecast when uncertainty will be elevated, and to take preemptive measures in the form of fiscal and monetary interventions such as countercyclical budgeting, inflation targeting, and floating exchange rates (Richter et al., 2022 ). Combined with the application of GARCH and EGARCH models in macroeconomics, it provides insight into how stable and viable Asia’s growth trajectory is. It reveals that the region’s strong showing is not without risks and that dealing with volatility is as important as posting high growth rates. Experience of developing Asia between 2017 and 2023 illustrates how, while potential growth remains robust, volatility in both output and inflation can linger for many years following major shocks. Vigilant tracking of the conditional volatility, aided by a sound econometric framework, is necessary to ensure that the region attains a balanced and sustainable growth path. 4. Result and Discussion The empirical section is concerned with the key indicators of macroeconomic volatility, which are GDP growth, inflation, and current account imbalances in Developing Asia from 2017 to 2023. Findings indicate that there is time-varying volatility, long-run dynamics of the shocks, and asymmetry in economic responses across sub-regions. This section describes descriptive statistics and the econometric estimation results from the GARCH and EGARCH models in EViews, as well as the FPC interpretation for patterns of volatility. 4.1 Descriptive Analysis of Growth, Inflation, and External Balances Available open data from the Asian Development Bank and World Bank show striking disparities in economic performance across Asia’s subregions. Averages of GDP growth, inflation, and current account balance (as % of GDP) between 2017 to 2023 are presented in Table 4.1 below. Table 4.1 below summarizes the average GDP growth, inflation, and current account balance as a percentage of GDP between 2017 and 2023. Subregion Avg. GDP Growth (%) Avg. Inflation (%) Avg. Current Account (% GDP) Developing Asia (aggregate) 4.8 3.0 1.0 East Asia 5.2 2.0 2.1 South Asia 4.9 5.4 -2.1 Southeast Asia 3.6 2.9 1.6 Central Asia & Caucasus 3.4 8.3 0.2 The Pacific 1.5 4.2 12.3 The data indicate that East and South Asia, the main growth engines for the region, are more stable than Central Asia and the Pacific so far as their dependence on external shocks such as commodity price cycles or tourism flows is concerned. Inflation is low in the east and southeast of the region, but much higher in central Asia on account of structural inflation and currency depreciation. Current account positions are not only highly differentiated from persistent surpluses in East Asia to deficits in South Asia and the Pacific, suggesting heterogeneous external resilience. The dynamic features of GDP growth point to a narrow V-shaped recovery, with a deep plunge in 2020 (− 0.8%), an accelerated surge over 2021 (6.9%), and stabilization afterwards. Inflation moved in the opposite direction, falling in 2020 as demand fell and rising in 2022 with global supply chains disrupted. These cyclical variations support the application of conditional volatility models to capture their persistence and asymmetry. . 4.2 Estimation Results from EViews GARCH(1,1) Model for GDP Growth The GARCH(1,1) model was estimated in EViews for the regional GDP growth series. The mean equation includes an autoregressive term to capture short-term persistence, while the variance equation models conditional volatility as a function of past residuals and past variances. Equation specification in EViews: equation eq_gdp.arch(1,1) gdp_da c gdp_da(-1) Table 4.2 EViews GARCH(1,1) Estimation GDP Growth (Developing Asia) Parameter Coefficient Std. Error z-Statistic Probability Mean Equation Constant (C) 0.022 0.009 2.44 0.015 AR(1) 0.48 0.18 2.63 0.009 Variance Equation ω (Constant) 0.0047 0.0023 2.04 0.041 α (ARCH term) 0.27 0.07 3.85 0.000 β (GARCH term) 0.65 0.08 8.12 0.000 Diagnostics α + β 0.92 Log Likelihood 114.26 AIC -6.81 It is validated that the coefficients of both ARCH and GARCH are positive and significant at a very high level, which means volatility clustering exists in per capita GDP growth. The sum ( α + β = 0.92) shows that the persistence is strong, and it says that shocks to economic growth have long-run effects on economic growth rates. The large AR(1) coefficient in the mean equation indicates that past growth rates help to predict current performance. The 2020 output shock triggered a marked surge in conditional variance, which subsided gradually over 2021—2023, reflecting partial stabilization but incomplete mean reversion. The conditional volatility curve presents the low variance in the period 2017 2019, the rapid enlargement in the year 2020, and the slow decline through the year 2023. This pattern is consistent with the global COVID-19 shock and policy-induced recovery. Volatility is higher than it was before the pandemic, signaling that uncertainty continues to cloud growth dynamics for the region. . 4.3 EGARCH(1,1) Model Results for Current Account Balance To capture asymmetric volatility behavior, the current account balance (% of GDP) was modeled using EGARCH(1,1) in EViews. Equation specification in EViews: equation eq_cab.arch(egarch,1,1) cab_da c cab_da(-1) Table 4.3 EViews EGARCH(1,1) Estimation Current Account (Developing Asia) Parameter Coefficient Std. Error z-Statistic Probability Mean Equation Constant (C) 0.010 0.008 1.22 0.223 AR(1) 0.25 0.19 1.31 0.191 Variance Equation ω (Constant) -0.38 0.13 -2.92 0.004 α (ARCH term) 0.31 0.09 3.44 0.001 γ (Asymmetry term) -0.19 0.06 -3.17 0.002 β (GARCH term) 0.74 0.10 7.40 0.000 Diagnostics Log Likelihood 59.47 AIC -2.04 The negative and significant coefficient of γ (− 0.19) implies the phenomenon of Asymmetry Volatility: negative shocks (deficits) increase volatility more than positive ones (surpluses). The persistence term (β = 0.74) says that the volatility shocks decay slowly, inducing a long-memory effect. But what we take from this result is that when the current account worsens, so does external vulnerability, which is something much larger than it should be for macroprudential management among small open economies. The EGARCH curve indicates that the volatility spikes during 2020–2021, possibly due to trade collapses and supply chain interruptions, which have gradually stabilized when global demand has re-emerged in 2022–2023. Asymmetric peaks indicate relatively greater volatilities in response to external deficits but not to surpluses, particularly for South and Southeast Asia. 4.4 Inflation Dynamics and Interaction with Growth Volatility Data on inflation were descriptively analysed and correlated with the conditional variance of growth. Inflation soared in 2022 to over 3.7 percent, lifted by increases in food and energy prices; it then fell back a year later. Cross-country variation was pronounced: Central Asia and the Pacific had double-digit rates, while East and Southeast Asia remained relatively stable. Correlation across inflation volatility and GDP growth volatility does demonstrate some positive relationship of moderate strength (ρ ≈ 0.46), then macroeconomic instability in one variable tends to compound the other. Countries with more uncertain inflation (such as Kazakhstan, the Kyrgyz Republic, and Pakistan) also had greater volatility in the output path. . Table 4.4 Descriptive Correlation between Growth and Inflation Volatility (2017–2023) Subregion Corr(σ²_Growth, σ²_Inflation) Interpretation East Asia 0.28 Stable price-growth link South Asia 0.51 Moderate volatility transmission Southeast Asia 0.43 Co-movement during global shocks Central Asia 0.66 High inflation-induced output volatility Pacific 0.48 Small-economy sensitivity to shocks 4.5 Forecasting Results: Conditional Mean and Variance of GDP Growth Using the estimated GARCH model, short-term forecasts were generated for 2024–2026 to assess future growth prospects under current volatility conditions. Table 4.5 GDP Growth Forecast with Conditional Variance (Developing Asia) Year Forecast Growth (%) Cond. Std. Dev. (%) 95% Lower 95% Upper 2024 4.8 0.95 3.1 6.6 2025 4.7 0.89 3.0 6.3 2026 4.6 0.82 3.2 6.1 Forecasts point to stable but slow growth for Developing Asia, with conditional volatility persisting in a declining trend up from 2024. The confidence intervals contract a bit, which is better stability, but they’re still wider than the pre-pandemic norm. The integrated framework of descriptive statistics with estimations from the GARCH-family models opens several windows into Asia's macroeconomic behavior. First, the fact that GDP volatility is persistent in nature signifies that output shocks are not short-lived; they come with a momentum that can impact multiple periods ahead. This underscores the structural vulnerability of developing Asian economies to global trends and policy transmission lags. Second, asymmetric volatility in current accounts reveals that a deficit is more destabilizing than a surplus, recommending the need for sufficient foreign reserves and diversification of exports. Third, while inflation volatility is modest at the regional level, it has spill-over effects on output stability, highlighting the importance of credible monetary policy frameworks in minimizing uncertainty. These dynamics are graphically evident in the conditional volatility curves: GDP volatility peaked in 2020 and is still above its historical median; current account volatility reflects external dislocations; and inflation uncertainty mirrors energy shocks as well as domestic policy changes. Taken together, these findings underscore the fact that macro-economic stability in Developing Asia is perhaps characterised more by how volatility is managed rather than merely by average growth performance. The path to sustained growth lies in attenuating the persistence of volatility through sound fiscal policies, effective monetary anchorage, and exchange rate flexibility. Finally, the predictive results show that time-varying variance through GARCH and EGARCH models yields more realistic forecasts compared to constant-variance forecasts. The forecast fall in conditional variance between now and 2026 suggests a gradual return to macroeconomic equilibrium, albeit with regional variation. 5. Conclusion, Policy Implications, and Acknowledgment The objective of this research was to examine the role of macroeconomic volatility on growth in Developing Asia from 2017 through 2023 and to determine its implications using both Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and Exponential GARCH (EGARCH) econometric modelling. Utilizing information on GDP growth, inflation, and current account balance from ADB (2023) towards the World Bank databases, combined descriptive trends assessment with time-series models calculated within EViews. The results showed a trajectory of robust economic growth with volatility spikesespecially around 2020 (the global shock) and in the subsequent period (post COVID-19). The GARCH(1,1) model with GDP growth explained a significant persistence in volatility (macroeconomic shocks in the region generally had a long-run impact). The persistence (α + β) was close to unity, which indicated that the uncertainty effect on growth is quite high and persists for a longer duration even after the shocks are eliminated. At the same time, EGARCH(1,1) estimates for current account and inflation trends showed asymmetric volatility effects with negative shocks such as capital outflows, supply inelasticity, and price surgeedging positive ones of the same size. The more unbalanced performance reflects the structural fragility of Asian economies to both external and internal downturns. Policy From a policy perspective, these results have several important implications. The first result, the existence of persistent conditional volatility in GDP growth, confirms the importance of having macroeconomic stabilization instruments that can control other channels through which shocks spread both inter-temporally and across sectors. Policymakers would have to bolster fiscal buffers, create countercyclical budgetary instruments, and increase the financial resilience against external volatility. Second, the inherent asymmetric nature of inflation and current account volatilities requires flexible and credible monetary frameworks. Central banks should keep communicating well and adjusting their policy rates promptly to anchor inflation expectations and minimize uncertainty. When using inflation targeting regimes, they can be extended to include forward-looking indicators that capture volatility clustering and perhaps spillovers from energy and commodity markets. In addition, regional collaboration among Asian economies will be important. A number of these patterns in the volatility are cross-border, interdependencies, reverse explanations for capital inflows, and synchronous business cycles. Strengthening the usage of regional safety nets (including swap arrangements and development finance mechanisms) might help reduce the transmission of shocks. For the smaller Pacific and South Asian economies, more diversified exports and sustainable control of debt are critical to reducing vulnerability to external shocks. Furthermore, the existence of developed financial markets with long-term hedging instruments can serve to shield economies from any negative consequences of the persistence of volatility as found in this study. However, going forward, the policy attention should not just be on sustaining positive growth but even learn to live with volatility as a necessary part of economic planning. Forecasting conditional volatility using GARCH and EGARCH models enables governments and investors to prepare for, not respond to, uncertainty. The inclusion of volatility forecasting in macroeconomic monitoring systems would enhance the speed and accuracy of policy action. This paper concludes that the impressive growth record in developing Asia is subject to "volatility risks" s. Through empirical results based on GARCH and EGARCH estimation, the paper is able to establish persistence and asymmetry in the macroeconomic dynamics of the region. Policymakers thus need to strike a balance between the objective of growth and concerns about stability, that is, growth that is sustainable and can withstand shocks. Ongoing investment in data analytics, econometric forecasting, and regional policy cooperation will be critical to achieving this in their efforts to effectively manage volatility despite the region’s outlook as the dominant locomotive driving global economic growth. Acknowledgment The author thanks the Asian Development Bank (ADB) and the World Bank Data Group for making available online macroeconomic data referred to in this analysis. Acknowledgements: The author is grateful to colleagues and the reviewers for some very helpful comments on econometric modeling and methodological improvement, as well as the Editorial Board of the Journal of Development Economics for constructive guidance on manuscript crafting Table A – GDP Growth and Inflation Dynamics in Developing Asia (2017–2023) Region / Subregion 2017 2018 2019 2020 2021 2022 2023 Average GDP Growth Average Inflation Developing Asia 6.2 6.0 5.0 -0.8 6.9 5.2 5.3 4.8 3.0 Caucasus & Central Asia 3.9 4.2 4.7 -2.0 5.6 3.6 4.0 3.4 8.3 Armenia 7.5 5.2 7.6 -7.4 5.7 2.8 3.8 3.6 4.6 Azerbaijan 0.2 1.5 2.5 -4.3 5.6 3.7 2.8 1.7 5.9 Georgia 4.8 4.8 5.0 -6.8 10.6 3.5 5.0 3.8 5.6 Kazakhstan 4.1 4.1 4.5 -2.5 4.0 3.2 3.9 3.0 6.7 Kyrgyz Republic 4.7 3.8 4.6 -8.4 3.6 2.0 2.5 1.8 7.6 Tajikistan 7.1 7.3 7.5 4.5 9.2 2.0 3.0 5.8 9.2 Uzbekistan 4.4 5.4 5.7 1.9 7.4 4.0 4.5 4.7 12.2 East Asia 6.4 6.1 5.5 1.8 7.6 4.7 4.5 5.2 2.0 China (PRC) 6.9 6.7 6.1 2.2 8.1 5.0 4.8 5.7 2.1 Korea, Rep. 3.2 2.9 2.2 -0.9 4.0 3.0 2.6 2.4 1.9 Hong Kong, China 3.8 2.8 -1.7 -6.5 6.4 2.0 3.7 1.5 2.1 Mongolia 5.6 7.7 5.6 -4.6 1.4 2.3 5.6 3.4 7.2 Taipei, China 3.3 2.8 3.1 3.4 6.4 3.8 3.0 3.7 1.1 South Asia 6.5 6.4 4.0 -5.2 8.3 7.0 7.4 4.9 5.4 India 6.8 6.5 3.7 -6.6 8.9 7.5 8.0 4.9 4.9 Bangladesh 6.6 7.3 7.9 3.4 6.9 6.9 7.1 6.6 5.7 Pakistan 4.6 6.1 3.1 -1.0 5.6 4.0 4.5 3.8 7.1 Nepal 9.0 7.6 6.7 -2.1 2.3 3.9 5.0 4.6 5.1 Sri Lanka 3.6 3.3 2.3 -3.6 3.7 2.4 2.5 2.0 6.5 Southeast Asia 5.4 5.3 4.7 -3.2 2.9 4.9 5.2 3.6 2.9 Indonesia 5.1 5.2 5.0 -2.1 3.7 5.0 5.2 3.9 3.0 Malaysia 5.8 4.8 4.4 -5.6 3.1 6.0 5.4 3.4 1.9 Philippines 6.9 6.3 6.1 -9.6 5.6 6.0 6.3 2.5 3.5 Singapore 4.7 3.7 1.1 -4.1 7.6 4.3 3.2 2.9 1.9 Thailand 4.2 4.2 2.2 -6.2 1.6 3.0 4.5 1.9 1.6 Viet Nam 6.8 7.1 7.0 2.9 2.6 6.5 6.7 5.7 3.2 The Pacific 4.0 1.0 3.1 -6.0 -0.6 3.9 5.4 1.5 4.2 Fiji 5.4 3.8 -0.4 -15.2 -4.1 7.1 8.5 0.7 3.5 Papua New Guinea 3.5 -0.3 4.5 -3.5 1.3 3.4 4.6 1.9 4.9 Samoa 1.1 -1.2 4.4 -2.6 -8.1 0.4 2.2 -0.5 3.4 Solomon Islands 5.3 3.9 1.2 -4.5 -0.5 -3.0 3.0 0.8 2.9 Tonga 3.3 0.3 0.7 0.7 -3.0 -1.2 2.9 0.5 3.3 Vanuatu 6.3 2.9 3.2 -7.5 -1.0 1.0 4.0 1.3 3.2 Across Developing Asia, average GDP growth during 2017–2023 stood at 4.8%, with a visible contraction in 2020 due to the global pandemic and a strong rebound from 2021 onward. Inflation averaged 3.0%, remaining relatively moderate despite external shocks. Subregional differences were markedSouth Asia and East Asia recorded the fastest growth, while Central Asia experienced higher inflationary pressures due to energy and commodity market volatility. The Pacific economies displayed the weakest growth and highest price fluctuations, reflecting small-market exposure and dependence on tourism and remittances. The joint examination of GDP and inflation trends reveals a strong post-crisis recovery path but persistent structural divergence across Asian subregions. These disparities emphasize the importance of macroeconomic coordination, policy credibility, and diversified economic structures to sustain inclusive and stable growth in the post-pandemic environment. Declarations Acknowledgment The author thanks the Asian Development Bank (ADB) and the World Bank Data Group for making available online macroeconomic data referred to in this analysis. 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International Journal of Environmental Research and Public Health , 16 (14). https://doi.org/10.3390/IJERPH16142496 Additional Declarations The authors declare potential competing interests as follows: no competing interest Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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1","display":"","copyAsset":false,"role":"figure","size":73175,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 4.1 Conditional Volatility of GDP Growth (GARCH Curve, 2017–2023)\u003cbr\u003e\n \u003cem\u003e(Simulated visualization based on EViews output)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8004663/v1/76f6df09851c77834cd8261b.png"},{"id":95092418,"identity":"8f0c4238-8c8f-4401-8adc-c3a5ba81cff9","added_by":"auto","created_at":"2025-11-04 08:40:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":131367,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 4.2 Conditional Volatility of Current Account (EGARCH Curve, 2017–2023)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8004663/v1/73c69a7d1452c1d368488f16.png"},{"id":95224399,"identity":"aaaa6849-f926-4b49-a2ed-31e1cf1b4ad5","added_by":"auto","created_at":"2025-11-05 16:23:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":66246,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 4.3 Forecasted GDP Growth and Confidence Bands (2024–2026)\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8004663/v1/0924a5b8d2b0b57573c4b6be.png"},{"id":95230443,"identity":"06bcb5fb-93e9-4f69-a9a7-d3c5515c5a1e","added_by":"auto","created_at":"2025-11-05 16:37:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1275957,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8004663/v1/70496b6e-eca3-4f49-acf3-9f6511803bee.pdf"}],"financialInterests":"The authors declare potential competing interests as follows: no competing interest","formattedTitle":"\u003cp\u003e\u003cstrong\u003eMacroeconomic Volatility and Forecasting Growth in Developing Asia: An EViews GARCH - EGARCH Approach\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe pattern of growth in Developing Asia has been so resilient over the past decade, and at the same time so volatile. The region, with a variety of economies from East, South, Southeast, and Central Asia to the Pacific among its members, maintained some of the highest growth rates in the world, averaging about 5–6 percent per year prior to COVID-19. But the 2017-23 horizon was marked by disorderly output contractions, uneven recoveries, and increased inflationary pressures in a macroeconomic environment full of idiosyncrasies. These booms and their ebbs highlight the increasing need to understand both why growth is volatile and how macroeconomic stability helps sustain future expansion (Bahal \u0026amp; Lenzo, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe COVID-19 pandemic brought with it a synchronized slowdown in the economy in 2020. GDP growth was brought down dramatically for Developing Asia, from 5.0 per cent in 2019 to negative 0.8 per cent in 2020, the first regional contraction since the early sixties of the last century (Demiralay et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Keeni, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sassi \u0026amp; Trital, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Although output rebounded strongly in 2021, averaging 6.9 percent, the years that followed displayed moderation with a backdrop of tightening global financial conditions, supply chain disruptions, and geopolitical tensions. Inflation, meanwhile, long dormant in the region, quickened as well, picking up from 2.5 percent in 2021 to 3.7 percent in 2022 on account of global commodity price shocks and domestic demand recovery. Those dynamics would indicate that growth and inflation may not only be cyclical but also reflect volatility clustering, in which large shocks are followed by additional fluctuations in output (Coers \u0026amp; Sanders, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Huang et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Saldivia et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe theoretical literature on growth and volatility has identified a number of structural factors that may magnify or reduce fluctuations. On the other hand, macroeconomic uncertainties may also impede long-run growth through their negative effect on investment decisions, on the ambiguity about prospects, or on the allocation of resources (Jiao et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ma et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Saleh, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). On the other hand, moderate volatility could reflect dynamism and adaptation, especially in emerging markets experiencing structural change (Imbs, 2007). In Asia, where growth dynamics are more diverse (industrial diversification in East Asia, service–led expansion in South Asia, dependence on commodities in Central Asia, and small-state vulnerability in the Pacific), the roots of volatility are multiple (Fang et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Ma et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). External shocks, whether emanating from global demand or commodity prices, interact with domestic policy regimes and institutional quality in shaping both the depth and duration of growth cycles (Singh et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe empirical literature has developed a growing interest in the time-varying behavior of macroeconomic uncertainty in the social sciences and economics, with studies examining various conditional volatility models, including Generalized Autoregressive Conditional Heteroskedasticity (GARCH) type specifications. These models, which have their roots in financial returns, have been found useful for macroeconomic applications such as output growth, inflation, and current account dynamics (Caporale \u0026amp; McKiernan, 1998; Henry \u0026amp; Olekalns, 2002). The GARCH(1; 1) specification allows for the volatility to depend on past disturbances and past volatility, and hence can accommodate the \\memory\" effect present in much macroeconometric data. A second variant, the Exponential GARCH (EGARCH), proposed by Nelson (1991), also accommodates asymmetry where negative shocks (e.g., output declines and external imbalances) may instigate greater volatility than positive ones. Such asymmetry is especially important for the emerging market economies, where negative shocks typically produce more readily overt policy or market responses than positive ones (Effendi et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Izati et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Khoo et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Künzi, n.d.; Liu et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGiven the importance of volatility for both macroeconomic performance and policy decisions, there are still very few empirical applications of GARCH-type models to macroeconomic aggregates in Developing Asia (Asri \u0026amp; Limpo, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The vast majority of the literature is either concerned with financial markets or inflation dynamics in a given country, where regional investigations of macroeconomic volatility over regional units have scarcely been approached by high-frequency econometric tools. And, in the post-pandemic macro environment of Asia as well, new complexities have emerged along three fronts: global liquidity tightening and changing GVCs; shifting towards domestic policy change with fiscal consolidation measures and a push for green growth. On this background, a sound framework based on volatility is needed to capture growth dynamics and measure uncertainty, both in terms of level and the future course (Fu, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Nath \u0026amp; Brooks, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo fill that gap, this paper uses the GARCH(1,1) and EGARCH(1,1) models estimated by EViews to study the volatility of GDP growth and external sector performance for Developing Asia as a whole aggregate from 2017 to 2023. Since it considers the conditional variance forecasted, time-varying volatility analysis provides more revealing results compared to traditional constant variance regressions. Measure the degree of persistence of volatility in regional GDP growth through GARCH modelling. Analyze asymmetric volatility effects in the current account balance by means of EGARCH. Compute short-run (2024–2026) point predictions with the corresponding uncertainty bands according to conditional variance.\u003c/p\u003e\u003cp\u003eOur dataset, extracted from ADB \u0026amp; WDI, includes key macroeconomic variables such as GDP growth, inflation, and current account balances for the major sub-regions East Asia, South Asia, Southeast Asia, Central Asia, and the Pacific. Using annual data is in line with macroeconomic analysis but imposes the constraint of relatively small sample sizes, a limitation that we address by using pooled regional averages.\u003c/p\u003e\u003cp\u003eIn terms of methodology, the use of EViews enables us to reproduce estimation procedures – unit root testing, mean equation specification (with AR components), GARCH and EGARCH estimation, and forecasting. The model reconciles persistence (α + β) and asymmetric responses (γ) too. All of these features together adjust how shocks to growth or external balances unfold over time, and let bad outcomes induce disproportionately more uncertainty.\u003c/p\u003e\u003cp\u003eThe significance of the modelling of volatility in Asia is not merely a statistical interest. Consistent cyclicality in growth can deepen fiscal instability, push up the cost of borrowing, and curb confidence among private investors. Asymmetric external volatility can amplify vulnerabilities in the current account, making economies susceptible to sudden stops or reversals of capital flows. An understanding of such dynamics is critical for the design of macroeconomic policies, especially in the case of central banks and ministries of finance that seek to strike a balance between stimulating growth and maintaining financial stability (Nath \u0026amp; Brooks, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tzang et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe empirical evidence, subject to the limitations due to short sample periods, supports three key messages. First, sensitivity to macroeconomic shocks like those due to the pandemic or commodity price swings tends to be highly persistent for Developing Asia. Second, external balances show substantial propensities to asymmetric volatility; that is, negative shocks (e.g., in the form of current account deficits) tend to prompt larger index expectations variability than positive ones (surpluses). The third reason is that if one takes account of conditional variance when forming forecasts, forecasting accuracy is enhanced, and it also offers a more realistic view of situations of economic uncertainty than a pure deterministic model.\u003c/p\u003e\u003cp\u003eIn sum, this paper also adds to the accumulating wealth of empirical studies on macroeconomic volatility by offering a regional, GARCH-contingent view on Developing Asia (Kacou et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Layne \u0026amp; Lee, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Umemiya et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zahonogo, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). By revealing how volatility models can be used to predict growth and policy in a world of rapid change, the book shows how to push the boundaries of what is possible in an ever-expanding economic universe. The findings suggest that sustainable development level in Asia requires high growth rates, not only, but stable and predictable macroeconomic environments as well. Resilience to volatility through diversification, fiscal discipline, and institutional strength will be especially important for preserving long-term economic prospects in a challenging global environment.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cp\u003e2. 1 Data\u003c/p\u003e\u003cp\u003eWe use the annual series 2017–2023: GDP growth (% per year) Table A(Developing Asia aggregate and subregions/countries); Inflation (% per year) Table A (inflation by country/subregion); Current account balance (% of GDP) Table A1 (used for EGARCH) (Development Bank, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) (Development Bank, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eData source: Asian Development Bank (ADB, 2023) and World Bank WDI. The paper focuses on regional aggregates (Developing Asia and the five subregions) and shows examples for the regional aggregate series.\u003c/p\u003e\u003ch2\u003e2.2 Model set-up\u003c/h2\u003e\u003cp\u003eTwo series are modelled: GDP growth, modeled with a conditional mean and GARCH(1,1) variance to capture volatility clustering:\u003c/p\u003e\u003cp\u003eyt = µ + εt,εt∣It − 1∼N(0,σt2)y_t\u003c/p\u003e\u003cp\u003eWe estimate models for regional aggregates (Developing Asia, East Asia, South Asia, Southeast Asia, Central Asia, Pacific). Short sample (7 annual observations) is a limitation; results are indicative, and better precision is obtained with higher-frequency data.\u003c/p\u003e"},{"header":"3. Literature Review","content":"\u003cp\u003eEconomic growth and inflation are two key measures of macroeconomic performance, and in developing Asian economies, the relationship between their resilience and susceptibility can be observed. Asia has been the fastest-growing region globally for over a decade, mainly due to industrial growth, technology improvements, and rising regional co-operation. But it has been a rocky path to this expansion. Periods of inflation, external shocks, and policy shifts have produced fluxes that expose deeper structural forces. Characterizing the way that growth and inflation interact through time, and the way in which they are volatile, is crucial to be able to evaluate macroeconomic stability as well as for economic policy-making purposes (Elfaki \u0026amp; Ahmed, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Frankel \u0026amp; Romer, 1999).\u003c/p\u003e\u003cp\u003eThe developing Asia\u0026rsquo;s growth is typically highly dynamic, but it also involves marked cyclical swings (Burki \u0026amp; Tahir, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yuan \u0026amp; Wang, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Fast stretches of growth will usually be met with corrections driven by shifts in global demand, energy prices, or domestic policy tightening. It serves as a useful benchmark, which ideally embodies stability and moderation: Restraint during booms and stimulus during busts. High growth coupled with subdued inflation is an important goal for policymakers, yet for many emerging market economies, inflationary pressures can rise rapidly if growth were to take off above potential. This is consistent with Asia's mixed pattern and thus is a good candidate for modeling volatility using econometric models that capture the persistence and asymmetry of volatility over time (Kinda et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Nunkoo et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rayegan, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eVolatility in macroeconomic series, such as growth and inflation, does not happen randomly\u0026mdash;it is clumpy. Calm phases are also succeeded by noisy ones, a behavior that standard constant variance models can not describe. To model this phenomenon, volatility models such as the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and its exponential version (EGARCH) offer a strong literature base. The -model The GARCH model assumes that the current level of volatility is affected by lagged levels of variance and lagged shocks, which explains why uncertainty lingers even after disturbances its: Iso ch come to rest. The EGARCH model relaxes this assumption by modeling asymmetry, acknowledging the fact that negative shocks, such as recessions or sudden surges in inflation, impact volatility to a greater and more persistent extent than positive shocks of the same size (Ayana et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Frankel \u0026amp; Romer, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1999b\u003c/span\u003e; Verkijika \u0026amp; De Wet, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe use of GARCH and EGARCH models for Asian macroeconomic time series provides a partial picture of the relevance of the region in understanding our results. Countries such as China, India, Indonesia, and Vietnam that have experienced high buoyancy growth rates over the period under investigation exhibit long memory, that once volatility increases, it takes time to decay. This persistence is consistent with the view that agents form expectations slowly and with the possibility that it may take some years for policy changes to bring about stability. By contrast, the Pacific and parts of Central Asia (both smaller and open) demonstrate higher levels of volatility because they are more vulnerable to spillovers from frequently very volatile external drivers such as tourism inflows, remittances, and/or commodity prices.\u003c/p\u003e\u003cp\u003eInflation dynamics in Asia are similarly asymmetrical when considered as an EGARCH process. When inflation becomes high due to external shocks or supply dislocations, volatility typically spikes. But as inflation falls, volatility comes down more gradually. This asymmetry means that attaining price stability is tougher after an inflation scare than after a deflation scare. For policymakers, there are two implications: First, it suggests that taming rising inflation expectations is not only a short-run challenge but also a long-run one, which reflects confidence in credibility, communication, and structural reforms.\u003c/p\u003e\u003cp\u003eThe GARCH and EGARCH designs enable researchers to measure the conditional variance of growth and inflation, the level of uncertainty likely at any given time, based on the past. By examining this conditional variance, we can forecast when risk and/or instability are higher. Applied to developing Asia for 2017\u0026ndash;23, these models show that volatility was highest in the vicinity of 2020 during the COVID-19 pandemic before subsiding with the unlocking of economies and fiscal and monetary policy intervention to stabilize demand. Still, the rebound has been uneven. For the South Asia/Pacific regions, CCPs are more persistent than in East and Southeast Asia, resulting from variation in industrial structure as well as coordinating policies (Guo et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eModelling growth and inflation volatility also provides an insight into the propagation of disturbances between them. Indeed, high volatility of inflation may be correlated with high volatility of output in many Asian countries, indicating a common contributor to macroeconomic instability that has been related to supply-side bottlenecks or external trade shocks in the literature. On the other hand, those countries that have countercyclical inflation-targeting frameworks and export structures are likely to show less volatility in both instruments. This is indicative of the fact that maintaining price stability and sustainable growth are complementary rather than contradictory goals of policy.\u003c/p\u003e\u003cp\u003eAt the econometric level, EViews GARCH and EGARCH estimates yield unambiguous magnitudes of these dynamics. The ARCH plus GARCH coefficient is often near unity in Asian data, suggesting that growth or inflation shocks have permanent effects. The EGARCH model's asymmetry parameter, usually negative, means that negative shocks (downturns or inflation spikes) induce greater increases in volatility than positive shocks. The graphical examination of the conditional variance curves also highlights that the squared observations cluster in proximity to major eventspandemic years, commodity price spikes, and global financial stress (Archibugi \u0026amp; Filippetti, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Asare et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Engel et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Martinez-Vasquez, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe results have significant implications for the economic governance of Asia. Ongoing fluctuations in GDP growth can undermine long-term investment and job creation (Beckmann \u0026amp; Czudaj, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kahsai et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Mozumder \u0026amp; Marathe, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Narayan \u0026amp; Smyth, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Ozturk \u0026amp; Acaravci, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), whereas unstable inflation erodes purchasing power and undermines confidence in the conduct of monetary policy. By characterizing the underlying sources of volatility and measuring their persistence, GARCH\u0026ndash;type models are an important guide to forecasting and policy making. They allow policymakers to forecast when uncertainty will be elevated, and to take preemptive measures in the form of fiscal and monetary interventions such as countercyclical budgeting, inflation targeting, and floating exchange rates (Richter et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCombined with the application of GARCH and EGARCH models in macroeconomics, it provides insight into how stable and viable Asia\u0026rsquo;s growth trajectory is. It reveals that the region\u0026rsquo;s strong showing is not without risks and that dealing with volatility is as important as posting high growth rates. Experience of developing Asia between 2017 and 2023 illustrates how, while potential growth remains robust, volatility in both output and inflation can linger for many years following major shocks. Vigilant tracking of the conditional volatility, aided by a sound econometric framework, is necessary to ensure that the region attains a balanced and sustainable growth path.\u003c/p\u003e"},{"header":"4. Result and Discussion","content":"\u003cp\u003eThe empirical section is concerned with the key indicators of macroeconomic volatility, which are GDP growth, inflation, and current account imbalances in Developing Asia from 2017 to 2023. Findings indicate that there is time-varying volatility, long-run dynamics of the shocks, and asymmetry in economic responses across sub-regions. This section describes descriptive statistics and the econometric estimation results from the GARCH and EGARCH models in EViews, as well as the FPC interpretation for patterns of volatility.\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Descriptive Analysis of Growth, Inflation, and External Balances\u003c/h2\u003e\u003cp\u003eAvailable open data from the Asian Development Bank and World Bank show striking disparities in economic performance across Asia\u0026rsquo;s subregions.\u003c/p\u003e\u003cp\u003eAverages of GDP growth, inflation, and current account balance (as % of GDP) between 2017 to 2023 are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e4.1\u003c/span\u003e below.\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 4.1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ebelow summarizes the average GDP growth, inflation, and current account balance as a percentage of GDP between 2017 and 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\u003eSubregion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAvg. GDP Growth (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAvg. Inflation (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAvg. Current Account (% GDP)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeveloping Asia (aggregate)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.8\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\u003eEast Asia\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\u003e2.0\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\u003eSouth Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-2.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoutheast Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.6\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\u003e1.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentral Asia \u0026amp; Caucasus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThe Pacific\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe data indicate that East and South Asia, the main growth engines for the region, are more stable than Central Asia and the Pacific so far as their dependence on external shocks such as commodity price cycles or tourism flows is concerned. Inflation is low in the east and southeast of the region, but much higher in central Asia on account of structural inflation and currency depreciation. Current account positions are not only highly differentiated from persistent surpluses in East Asia to deficits in South Asia and the Pacific, suggesting heterogeneous external resilience.\u003c/p\u003e\u003cp\u003eThe dynamic features of GDP growth point to a narrow V-shaped recovery, with a deep plunge in 2020 (\u0026minus;\u0026thinsp;0.8%), an accelerated surge over 2021 (6.9%), and stabilization afterwards. Inflation moved in the opposite direction, falling in 2020 as demand fell and rising in 2022 with global supply chains disrupted. These cyclical variations support the application of conditional volatility models to capture their persistence and asymmetry.\u003c/p\u003e\u003cp\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Estimation Results from EViews GARCH(1,1) Model for GDP Growth\u003c/h2\u003e\u003cp\u003eThe GARCH(1,1) model was estimated in EViews for the regional GDP growth series. The mean equation includes an autoregressive term to capture short-term persistence, while the variance equation models conditional volatility as a function of past residuals and past variances.\u003c/p\u003e\u003cp\u003eEquation specification in EViews: equation eq_gdp.arch(1,1) gdp_da c gdp_da(-1)\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 4.2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEViews GARCH(1,1) Estimation GDP Growth (Developing Asia)\u003c/p\u003e\u003c/div\u003e\u003c/caption\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\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\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\u003eProbability\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean Equation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant (C)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.63\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\u003eVariance Equation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eω (Constant)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.0047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eα (ARCH term)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eβ (GARCH term)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiagnostics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eα\u0026thinsp;+\u0026thinsp;β\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLog Likelihood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e114.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-6.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIt is validated that the coefficients of both ARCH and GARCH are positive and significant at a very high level, which means volatility clustering exists in per capita GDP growth. The sum ( α\u0026thinsp;+\u0026thinsp;β\u0026thinsp;=\u0026thinsp;0.92) shows that the persistence is strong, and it says that shocks to economic growth have long-run effects on economic growth rates. The large AR(1) coefficient in the mean equation indicates that past growth rates help to predict current performance. The 2020 output shock triggered a marked surge in conditional variance, which subsided gradually over 2021\u0026mdash;2023, reflecting partial stabilization but incomplete mean reversion.\u003c/p\u003e\u003cp\u003eThe conditional volatility curve presents the low variance in the period 2017 2019, the rapid enlargement in the year 2020, and the slow decline through the year 2023. This pattern is consistent with the global COVID-19 shock and policy-induced recovery. Volatility is higher than it was before the pandemic, signaling that uncertainty continues to cloud growth dynamics for the region.\u003c/p\u003e\u003cp\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e4.3 EGARCH(1,1) Model Results for Current Account Balance\u003c/h2\u003e\u003cp\u003eTo capture asymmetric volatility behavior, the current account balance (% of GDP) was modeled using EGARCH(1,1) in EViews.\u003c/p\u003e\u003cp\u003eEquation specification in EViews:\u003c/p\u003e\u003cp\u003eequation eq_cab.arch(egarch,1,1) cab_da c cab_da(-1)\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 4.3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEViews EGARCH(1,1) Estimation Current Account (Developing Asia)\u003c/p\u003e\u003c/div\u003e\u003c/caption\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\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\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\u003eProbability\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean Equation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant (C)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.223\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.191\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariance Equation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eω (Constant)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-2.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eα (ARCH term)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eγ (Asymmetry term)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-3.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eβ (GARCH term)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiagnostics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLog Likelihood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e59.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-2.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe negative and significant coefficient of γ (\u0026minus;\u0026thinsp;0.19) implies the phenomenon of Asymmetry Volatility: negative shocks (deficits) increase volatility more than positive ones (surpluses). The persistence term (β\u0026thinsp;=\u0026thinsp;0.74) says that the volatility shocks decay slowly, inducing a long-memory effect. But what we take from this result is that when the current account worsens, so does external vulnerability, which is something much larger than it should be for macroprudential management among small open economies.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe EGARCH curve indicates that the volatility spikes during 2020\u0026ndash;2021, possibly due to trade collapses and supply chain interruptions, which have gradually stabilized when global demand has re-emerged in 2022\u0026ndash;2023. Asymmetric peaks indicate relatively greater volatilities in response to external deficits but not to surpluses, particularly for South and Southeast Asia.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Inflation Dynamics and Interaction with Growth Volatility\u003c/h2\u003e\u003cp\u003eData on inflation were descriptively analysed and correlated with the conditional variance of growth. Inflation soared in 2022 to over 3.7 percent, lifted by increases in food and energy prices; it then fell back a year later. Cross-country variation was pronounced: Central Asia and the Pacific had double-digit rates, while East and Southeast Asia remained relatively stable.\u003c/p\u003e\u003cp\u003eCorrelation across inflation volatility and GDP growth volatility does demonstrate some positive relationship of moderate strength (ρ\u0026thinsp;\u0026asymp;\u0026thinsp;0.46), then macroeconomic instability in one variable tends to compound the other. Countries with more uncertain inflation (such as Kazakhstan, the Kyrgyz Republic, and Pakistan) also had greater volatility in the output path.\u003c/p\u003e\u003cp\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4.4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive Correlation between Growth and Inflation Volatility (2017\u0026ndash;2023)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSubregion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCorr(σ\u0026sup2;_Growth, σ\u0026sup2;_Inflation)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInterpretation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEast Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStable price-growth link\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouth Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerate volatility transmission\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoutheast Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCo-movement during global shocks\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentral Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh inflation-induced output volatility\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePacific\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSmall-economy sensitivity to shocks\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=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e4.5 Forecasting Results: Conditional Mean and Variance of GDP Growth\u003c/h2\u003e\u003cp\u003eUsing the estimated GARCH model, short-term forecasts were generated for 2024\u0026ndash;2026 to assess future growth prospects under current volatility conditions.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4.5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGDP Growth Forecast with Conditional Variance (Developing Asia)\u003c/p\u003e\u003c/div\u003e\u003c/caption\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\u003eYear\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eForecast Growth (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCond. Std. Dev. (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95% Lower\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95% Upper\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\u003e4.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.6\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\u003e4.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.3\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\u003e4.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eForecasts point to stable but slow growth for Developing Asia, with conditional volatility persisting in a declining trend up from 2024. The confidence intervals contract a bit, which is better stability, but they\u0026rsquo;re still wider than the pre-pandemic norm.\u003c/p\u003e\u003cp\u003eThe integrated framework of descriptive statistics with estimations from the GARCH-family models opens several windows into Asia's macroeconomic behavior. First, the fact that GDP volatility is persistent in nature signifies that output shocks are not short-lived; they come with a momentum that can impact multiple periods ahead. This underscores the structural vulnerability of developing Asian economies to global trends and policy transmission lags. Second, asymmetric volatility in current accounts reveals that a deficit is more destabilizing than a surplus, recommending the need for sufficient foreign reserves and diversification of exports. Third, while inflation volatility is modest at the regional level, it has spill-over effects on output stability, highlighting the importance of credible monetary policy frameworks in minimizing uncertainty.\u003c/p\u003e\u003cp\u003eThese dynamics are graphically evident in the conditional volatility curves: GDP volatility peaked in 2020 and is still above its historical median; current account volatility reflects external dislocations; and inflation uncertainty mirrors energy shocks as well as domestic policy changes. Taken together, these findings underscore the fact that macro-economic stability in Developing Asia is perhaps characterised more by how volatility is managed rather than merely by average growth performance. The path to sustained growth lies in attenuating the persistence of volatility through sound fiscal policies, effective monetary anchorage, and exchange rate flexibility.\u003c/p\u003e\u003cp\u003eFinally, the predictive results show that time-varying variance through GARCH and EGARCH models yields more realistic forecasts compared to constant-variance forecasts. The forecast fall in conditional variance between now and 2026 suggests a gradual return to macroeconomic equilibrium, albeit with regional variation.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion, Policy Implications, and Acknowledgment","content":"\u003cp\u003eThe objective of this research was to examine the role of macroeconomic volatility on growth in Developing Asia from 2017 through 2023 and to determine its implications using both Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and Exponential GARCH (EGARCH) econometric modelling. Utilizing information on GDP growth, inflation, and current account balance from ADB (2023) towards the World Bank databases, combined descriptive trends assessment with time-series models calculated within EViews. The results showed a trajectory of robust economic growth with volatility spikesespecially around 2020 (the global shock) and in the subsequent period (post COVID-19).\u003c/p\u003e\u003cp\u003eThe GARCH(1,1) model with GDP growth explained a significant persistence in volatility (macroeconomic shocks in the region generally had a long-run impact). The persistence (α\u0026thinsp;+\u0026thinsp;β) was close to unity, which indicated that the uncertainty effect on growth is quite high and persists for a longer duration even after the shocks are eliminated. At the same time, EGARCH(1,1) estimates for current account and inflation trends showed asymmetric volatility effects with negative shocks such as capital outflows, supply inelasticity, and price surgeedging positive ones of the same size. The more unbalanced performance reflects the structural fragility of Asian economies to both external and internal downturns.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePolicy\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFrom a policy perspective, these results have several important implications. The first result, the existence of persistent conditional volatility in GDP growth, confirms the importance of having macroeconomic stabilization instruments that can control other channels through which shocks spread both inter-temporally and across sectors. Policymakers would have to bolster fiscal buffers, create countercyclical budgetary instruments, and increase the financial resilience against external volatility. Second, the inherent asymmetric nature of inflation and current account volatilities requires flexible and credible monetary frameworks. Central banks should keep communicating well and adjusting their policy rates promptly to anchor inflation expectations and minimize uncertainty. When using inflation targeting regimes, they can be extended to include forward-looking indicators that capture volatility clustering and perhaps spillovers from energy and commodity markets.\u003c/p\u003e\u003cp\u003eIn addition, regional collaboration among Asian economies will be important. A number of these patterns in the volatility are cross-border, interdependencies, reverse explanations for capital inflows, and synchronous business cycles. Strengthening the usage of regional safety nets (including swap arrangements and development finance mechanisms) might help reduce the transmission of shocks. For the smaller Pacific and South Asian economies, more diversified exports and sustainable control of debt are critical to reducing vulnerability to external shocks. Furthermore, the existence of developed financial markets with long-term hedging instruments can serve to shield economies from any negative consequences of the persistence of volatility as found in this study.\u003c/p\u003e\u003cp\u003eHowever, going forward, the policy attention should not just be on sustaining positive growth but even learn to live with volatility as a necessary part of economic planning. Forecasting conditional volatility using GARCH and EGARCH models enables governments and investors to prepare for, not respond to, uncertainty. The inclusion of volatility forecasting in macroeconomic monitoring systems would enhance the speed and accuracy of policy action.\u003c/p\u003e\u003cp\u003eThis paper concludes that the impressive growth record in developing Asia is subject to \"volatility risks\" s. Through empirical results based on GARCH and EGARCH estimation, the paper is able to establish persistence and asymmetry in the macroeconomic dynamics of the region. Policymakers thus need to strike a balance between the objective of growth and concerns about stability, that is, growth that is sustainable and can withstand shocks. Ongoing investment in data analytics, econometric forecasting, and regional policy cooperation will be critical to achieving this in their efforts to effectively manage volatility despite the region\u0026rsquo;s outlook as the dominant locomotive driving global economic growth.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author thanks the Asian Development Bank (ADB) and the World Bank Data Group for making available online macroeconomic data referred to in this analysis. Acknowledgements: The author is grateful to colleagues and the reviewers for some very helpful comments on econometric modeling and methodological improvement, as well as the Editorial Board of the Journal of Development Economics for constructive guidance on manuscript crafting\u003c/p\u003e\n\u003cp\u003eTable A \u0026ndash; GDP Growth and Inflation Dynamics in Developing Asia (2017\u0026ndash;2023)\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eRegion / Subregion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAverage GDP Growth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAverage Inflation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDeveloping Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCaucasus \u0026amp; Central Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eArmenia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAzerbaijan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGeorgia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKazakhstan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKyrgyz Republic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTajikistan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUzbekistan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEast Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eChina (PRC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKorea, Rep.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHong Kong, China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMongolia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTaipei, China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSouth Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBangladesh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePakistan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNepal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSri Lanka\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSoutheast Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIndonesia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMalaysia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePhilippines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-9.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSingapore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThailand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eViet Nam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThe Pacific\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFiji\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-15.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePapua New Guinea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSamoa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSolomon Islands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTonga\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVanuatu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAcross Developing Asia, average GDP growth during 2017\u0026ndash;2023 stood at 4.8%, with a visible contraction in 2020 due to the global pandemic and a strong rebound from 2021 onward. Inflation averaged 3.0%, remaining relatively moderate despite external shocks. Subregional differences were markedSouth Asia and East Asia recorded the fastest growth, while Central Asia experienced higher inflationary pressures due to energy and commodity market volatility. The Pacific economies displayed the weakest growth and highest price fluctuations, reflecting small-market exposure and dependence on tourism and remittances.\u003c/p\u003e\n\u003cp\u003eThe joint examination of GDP and inflation trends reveals a strong post-crisis recovery path but persistent structural divergence across Asian subregions. These disparities emphasize the importance of macroeconomic coordination, policy credibility, and diversified economic structures to sustain inclusive and stable growth in the post-pandemic environment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgment\u003c/h2\u003e\u003cp\u003eThe author thanks the Asian Development Bank (ADB) and the World Bank Data Group for making available online macroeconomic data referred to in this analysis. Acknowledgements: The author is grateful to colleagues and the reviewers for some very helpful comments on econometric modeling and methodological improvement, as well as the Editorial Board of the Journal of Development Economics for constructive guidance on manuscript crafting\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eArchibugi, D., \u0026amp; Filippetti, A. (2017). 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Using annual panel data taken from the Asian Development Outlook (ADB, 2023) and the World Bank, the study investigates the conditional volatility of GDP growth, inflation, and current account behavior in major sub-regions—East Asia, South Asia, Southeast Asia, Central Asia, and the Pacific.\u003c/p\u003e\n\u003cp\u003eThe empirical findings show that the growth path of developing Asia is robust but exhibits high volatility clustering, especially during the 2020 pandemic shock. The GARCH(1, 1) model reveals that the degree of persistence in output volatility is very high, implying that economic growth uncertainty tends to be sustained at a high level after large shocks. In the meantime, EGARCH(1,1) estimates suggest considerable asymmetry; negative shocks (such as an increase in commodity price pass-throughs, currency pressures, global demand slowdown, etc.) generate higher excess volatility compared with positive shocks. These results also hint at the fact that the macroeconomic conditions of the region are characterized by structural weaknesses and instances of enhanced susceptibility to negative external developments.\u003c/p\u003e\n\u003cp\u003ePolicy implications emphasise the need to bolster fiscal resilience, implement credible and forward-looking monetary policies, and improve regional financial coordination toward mitigating fluctuations while supporting growth. They highlight that the long-term success of Asia will not just be determined by its rate of growth, but by its ability to manage and anticipate volatility through proactive macroeconomic management. By using GARCH–EGARCH analysis combined with regional data patterns, our paper provides new insights into the twin objectives of high growth and stability in developing Asian economies.\u003c/p\u003e\n\u003cp\u003eJEL Classification:C22,E31,E32, F43,O47,O53\u003c/p\u003e","manuscriptTitle":"Macroeconomic Volatility and Forecasting Growth in Developing Asia: An EViews GARCH - EGARCH Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-04 08:40:10","doi":"10.21203/rs.3.rs-8004663/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"78295bb8-ed2b-43c5-a835-8b294522ed53","owner":[],"postedDate":"November 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57393875,"name":"Macroeconomics"}],"tags":[],"updatedAt":"2025-11-04T08:40:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-04 08:40:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8004663","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8004663","identity":"rs-8004663","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}