Growth Volatility Developing Asia: Evidence from the GARCH Model

Growth Volatility Developing Asia: Evidence from the GARCH Model | 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Article Growth Volatility Developing Asia: Evidence from the GARCH Model Marselinus Asri This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7997304/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 research examines GDP growth and its conditional volatility in developing Asia, as well as its subregions (East Asia, South Asia, Southeast Asia, Central Asia, and the Pacific) during 2017–2023. From the perspective of descriptive trend analysis and a GARCH(1,1) volatility framework, this paper examines how macroeconomic shocks affect growth stability. Findings suggest that economic volatility reached its highest level in 2020 due to the COVID-19 crisis and tends to fade away over time. Based on these GARCH estimates, there is strong persistence in volatility, indicating that shocks have a long-run impact on the regional economies. This indicates asymmetrical recovery trends in subregions, with East and South Asia returning to previous levels faster than the Pacific and Central Asia. The research finds that diversification, fiscal savings, and stronger regional cooperation are necessary to mitigate exposure related to external shocks. Policy impulses emphasize sustainable growth via innovation, digitalisation, and structural resilience. The paper also offers some directions for future research based on larger datasets and more sophisticated econometric analysis that would help to inform the non-linear country interaction of volatility. JEL Classification No: C22, E32, O11, O47, F43 Macroeconomics Macroeconomic volatility GDP growth GARCH model Developing Asia Figures Figure 1 Figure 2 1. INTRODUCTION The performance of a country is still judged by the magnitude of its macroeconomic variables, as well as its economic growth. GDP growth in emerging and developing economies is not just income-led, but also structural-adjusted, investment-driven driven and a global shock absorber. During 2017–2023, developing economies in Asia and the Pacific are projected to experience a unique historical pattern of growth, strong pre-pandemic expansion, sharp post-COVID-19 contraction in 2020, and varied recovery phases from 2021 onwards. This period presents a novel possibility to look at short-term economic resilience and the impact of both idiosyncratic and global shocks on growth paths. Asia has been the world’s economic engine for centuries, with average growth rates that have always exceeded those of developed areas. But this picture at the aggregate level obscures vast divergences within the regions of the Caucasus and Central Asia, East Asia, South Asia, Southeast Asia, and the Pacific. For instance, although both the People’s Republic of China and India experienced high pre-pandemic growth rates, smaller island economies like Vanuatu and Tuvalu had more uncertain outcomes as they rely on tourism and external demand. The determination of these regional and structural variations is essential for empirical modeling as well as for policy analysis. The COVID-19 pandemic, which began to spread in early 2020, is an exogenous shock that had an immediate impact on production, consumption, and trade throughout all economies. The effect of the pandemic was uneven: Larger, more diversified economies suffered a sudden but short downturn, while small island states and countries reliant on tourism or remittances endured protracted contractions. The quantification of these temporary disruptions is necessary to determine how resilient these economies were in facing global crises. This paper estimates the year 2020 as a discrete shock with a dummy variable so that the influence on GDP growth can be delineated (Saqib & Dincă, 2023 ). This study uses data on annual % GDP growth for 2017–2023 among a variety of developing Asian economies listed in Table A1. The data set includes seven years of observations and more than 40 countries, making it a short but rich panel for econometric analysis. The first objective is to estimate the persistence of the growth of GDP (via its lagged value) and the magnitude of the Covid-19 shock in a simple panel data setting. Although restricted by the short time horizon, fixed-effects estimation provides a means of controlling for unobserved heterogeneity across countries (Mahbub et al., 2022 ; Samdrup et al., 2023 ; Shinwari et al., 2024 ). We estimate the models in EViews, based on a method that researchers or policymakers could follow. The paper presents an explicit workflow for structuring tabular data to panel form, creating lagged variables and dummy indicators, and estimating fixed-effects regression with robust standard errors. This method makes the empirical exercise easy to replicate for applied researchers, in particular those who work with small-sample cross-national data. The findings generate critical implications related to short-run growth dynamics for developing Asia. It appears that growth persistence is not particularly high from one year to the next if country effects are accounted for, indicating that shocks do more to reset paths than sustain them. Most strikingly, the COVID-19 dummy indicates that COVID-19 has a statistically significant and economically large negative impact on GDP growth in 2020, consistent with what we have seen about the global recession. These results point to the susceptibility of small and developing islands to external shocks, emphasizing the need for fiscal and structural resilience (Khan & Shoaib, 2024 ; Narayan & Narayan, 2010 ). This paper adds to the work on empirical regional growth analysis by developing a reproducible, robust, and user-friendly EViews workflow-based approach that uses publicly available data. It illustrates the possibility of combining fixed-effects with the dynamic part of the model, also in short panel data, to disentangle transitory and permanent effects on growth. The approach and results are of specific interest to applied economists, development practitioners, and advanced students pursuing foundational methods in empirical macroeconomic modelling of developing economies. 2. METHODOLOGY GARCH Model A GARCH(1,1) model to calculate the conditional volatility of GDP growth rates. The GARCH framework was first introduced (Bollerslev, 1986 ) and is particularly apt for modeling time-varying variance, where it could also be used to identify volatility clustering, a phenomenon in which large growth or changes in the series tend to be followed by additional such large growths/changes. This model encompasses short-term adjustments as well as the persistence of volatility, and thereby provides a more realistic portrayal of business fluctuations in emerging markets. This article utilizes a quantitative econometric model to project GDP growth and conditional volatility in Developing Asia between 2017 and 2023. The study combines descriptive trend analysis with time-series modelling using the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model. In particular, it attempts to quantify the direction of growth with the persistence level of volatility over time using empirical information from the experience in macroeconomic stability of different subregions, such as East Asia, South Asia, Southeast Asia (SEA), Central Asia (CA), and the Pacific (Development Bank, 2023 ) Analysis Descriptive statistics and trending. This procedure gives a sense of the overall trend and volatility of GDP growth. Trends are graphically presented with line graphs to stress the cyclical nature of growth and the extent of changes prior to, during, and after COVID-19. Such a step enables one to evaluate contraction and recovery patterns between subregions, thereby identifying structural differences in macroeconomic behavior. Data: ADO 2022 The data in this study are derived from the database of ADB and the World Bank with respect to the annual GDP growth rates for 2017–2023. These facts reflect the post-global financial crisis performance, effects of the COVID-19 pandemic, and the beginning stage of economic recovery in Taiwan. The sample comprises a cross-section of 40 Asian countries divided into five regional blocs. The data set has sufficient variation to consider both specific (to the region) shocks as well as overall macro patterns on the continent (Development Bank, 2023 ). 3. LITERATURE REVIEW Macroeconomic volatility has traditionally been a focus of empirical investigation efforts to better understand the growth-instability-growth relationship in developing countries. This question is especially pertinent in the context of Asian economies that are undergoing structural change at a rapid pace, exhibiting wide-ranging institutional capabilities and having heterogeneous susceptibility to exogenous pressures. Given that business cycle fluctuations are usually measured in terms of varying degrees of GDP growth not only represent cyclical disturbances, but also underlying weaknesses in fiscal systems, as well as production structures and external linkages. This discourse is based on the theoretical work (Bollerslev, 1986 ) that first derives a negative relationship between volatility and growth, because uncertainty deters investment and productivity. Expanding on this basis, recent research has analyzed how Asian economies are in the face of volatility, given their growing linkages with the rest of the world. Asia has experienced a fast pace of economic growth, and it has also known bouts of instability. The 1997–1998 Asian Crisis, the 2008 Financial Crisis, and the COVID-19 pandemic in 2020 are pivotal events that illustrate how volatility impacts macro outcomes. Kose et al. (2006) noted that globalization magnifies the transmission of disturbances to other regions, intensifying the possibility of a prompt return to equilibrium but also fostering contagion. In Asia, structural reforms and trade liberalization have led to integration but also increased exposure to external financial and commodity price shocks. The ambivalence of ``globalization'' suggests the need to study volatility dynamics across different geographical areas. Macroeconomic volatility in East Asia has been tempered by strong institutions, diversified exports, and prudent fiscal policy. For example, China has been able to sustain high growth with relatively low volatility by slowly pivoting away from an export-led economic model toward one based on domestic consumption and technological innovation. The study of Zhang and Chen (2022) suggests that China’s reform-era countercyclical fiscal policy and the flexible monetary policy have reduced volatility persistence, especially since 2010 (Ayad & Lefilef, 2024 ; Y. Li et al., 2020 ; Zhao et al., 2018 ). The experience of the Republic of Korea shows how macroprudential regulation can help reduce external shocks, including by maintaining a stable exchange rate and effective banking supervision. On the other hand, economies like Hong Kong and Mongolia are more responsive to international financial cycles and price changes in commodities, which leads them to undergo cyclic growth (Ayad & Lefilef, 2024 ; X. Li & Xiao, 2024 ; Y. Li et al., 2020 ; Majumder, 2014 ; Zhao et al., 2018 ). The growth experience of South Asia reflects a different story, pointing to ongoing persistence and resilience. India and Bangladesh have experienced robust growth in spite of persistent exogenous jolts, and this is mainly due to strong domestic demand as well as the pursuit of digital transformation policies (Effendi et al., 2024 ; Khoo et al., 2024 ). Smaller South Asian economies, however, Bhutan, the Maldives, and Nepal, had seen massive oscillations in output as they are heavily dependent on tourism, remittances, and primary commodities. These structural dependencies amplify the impact of global downturns, while insufficient fiscal buffers inhibit countercyclical responses (International Monetary Fund 2022). Empirical results based on the GARCH and EGARCH models show that volatility for South Asian economies is asymmetric and persistent, because negative shocks have a long-lasting effect on growth relative to the positive shock (Bloch et al., 2012 ; Huang & Luk, 2020 ; Ngundu et al., 2024 ). Southeast Asia offers itself as a natural laboratory for exploring the relationship between trade integration and macroeconomic stability. Territorial specialisation ASEAN economies are widely open ones in the developing world, with trade representing a large share of GDP. Research illustrates that trade openness contributes to growth, but makes these economies vulnerable to the cyclical swings in world demand. Indonesia, Malaysia, and the Philippines are more diversified economies that can better endure volatility compared to Cambodia and Myanmar, which are still reliant on a few export bases. Nguyen (2023) also explains that command of macroeconomic policy, including exchange rate flexibility, has been an important moderating force on volatility in the ASEAN membership countries. Volatility dynamics in Central Asia are highly linked to dependence on commodities, especially hydrocarbons and metals. Kazakhstan, Turkmenistan, and Azerbaijan are heavily dependent on oil and gas exports, which have led to booms and busts that track big swings in the notorious volatility of global energy prices. (Asgharian et al., 2015 ; Chiang, 2019 ; Guo et al., 2014 ) Use GARCH models to show high volatility clustering in these countries; both domestic and international energy market shocks affect it for a long period. Diversification from resource dependence has been sluggish, and fiscal stabilisation mechanisms have been weak, making these countries very susceptible to external forces. Remittances from migrant labour in Russia, meanwhile, bring temporary relief but little structural transformation. In contrast, the Pacific economies endure an altogether different conduit of volatility that stems from their small market size, geographical remoteness, and tourism dependency. Fiji, Samoa, and Vanuatu endured sharp contractions during the COVID-19 pandemic as travel restrictions took their toll, before uneven recoveries with the reopening of borders. The Asian Development Bank ( 2023 ) adds that these economies have no diversified production bases and would have concentrated destabilisers of volatility. Macro instability also worsens climate-related shocks, including damage from severe weather, which disrupts infrastructure and agricultural production. Macroeconomic risk also has profound effects on investment, productivity, and welfare. (Javili et al., 2017 ; Phoung et al., 2024 ) point out that long-run investment is discouraged by high volatility, raising uncertainty, in turn depressing capital formation and innovation. This effect is especially pronounced in countries with less developed financial systems, which have less risk-sharing. In Asia, private investment constitutes a large part of GDP, and in such a context, spikes in volatility often mean less provision of credit and more fragile business confidence. Volatility, of course, also influences income distribution in other ways, such as by often increasing inequality when more vulnerable groups lose their jobs and face price increases during downturns, when there is a risk. From a methodological perspective, the study of macroeconomic volatility has come a long way – from initial trend calculations to more sophisticated measures such as the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model. First proposed by GARCH, it not only lets scholars account for the time-varying volatility but also accommodates shock persistence in macroeconomic data. In the Asian context, GARCH models have been commonly used to study inflation, exchange rates, and GDP growth unpredictability. For instance, Asteriou and Hall (2021) show that GARCH-implied estimates report significant structural volatility persistence in emerging Asian countries, suggesting that macroeconomic shocks tend to have long-run effects on stability. Empirical evidence is overwhelming that macroeconomic volatility is negatively related to long-run growth. (Abdillah et al., 2020 ; Fuddin & Maulidiyah, 2024 ; Ng, 2021 ; Ngundu et al., 2024 ; Paul, 2020 ) ) claim that volatility distorts consumption/savings decisions and, therefore, efficient capital allocation. In Asia, too, the effect is mixed depending on policy credibility, institutional strength, and external exposure. Economies with solid fiscal management and diversified industrial bases Singapore, Malaysia, and China, for instance, recover more quickly from shocks; those with weak governance or narrow export structures stagnate longer. Policy reactions are key to determining the result on volatility. Counter-cyclical fiscal policy, monetary discretion, and selective subsidies can help in the face of shocks. East Asian countries have had a fair degree of success in using such mechanisms, whereas the South and Central Asian countries tend to be more constrained in their use by fiscal space. There is also a need for structural changes around financial deepening and industrial diversification in order to tamp down volatility. It is also a fact that countries that invest in human capital and innovation have smaller long-run volatility. In recent literature, it is stressed that the volatility of economies in Asia cannot be considered independently from the world. Through the COVID-19 pandemic, we witnessed the interconnection of trade, finance, and health systems and how exogenous shocks can manifest along regional supply chains. According to studies by the Asian Development Bank ( 2023 ), regional policy coordination akin to the ASEAN Economic Community framework could promote resilience through standardizing fiscal, regulatory standards. This observation is consistent with the view that dealing with volatility needs to be a national and regional concern (Development Bank, 2023 ). Digitalization and innovation have emerged as other topics in post-pandemic literature. Digital economies in nations including India, China, and Singapore have proved more resistant to shocks because they can work remotely and innovate technologically. For example, Lin and Zhao’s (2023) study suggests that digitalized economies are observed to have higher recovery speed and lower persistence of volatility. On the other hand, countries with a weak technological base continue to stand naked before their boom/slump cycles. Another source of volatility in Asia has become environmental sustainability. Production, infrastructure, and trade are being disrupted more often by climate shocks, especially in the Pacific and South Asia. Hence, it is a weight in the investment green and in climate resilience strategies as long-term stabilization measures. The incorporation of environmental risk in macroeconomic volatility models is growing, and recent GARCH extensions are controlling for climate variables (Development Bank, 2023 ) The literature on macroeconomic volatility and growth dynamics in developing Asia reveals a broad range of experiences and results. Indeed, the trend of increasing resilience is evident in many places, but some regions are still experiencing chronic volatility because of structural imbalances and constraints in policy effectiveness as well as exposure to global uncertainty. The current paper adds to this literature by employing trend analysis and the GARCH model in the study of GDP growth during 2017–2023 to empirically estimate the features of volatility persistence and their policy implications. This methodology can help one to gain a more balanced view of what the Asian economies can do in reconciling their growth aspirations with macro-economic stability in fast-changing global conditions. 4. RESULTS AND DISCUSSION 4.1 Descriptive Characteristics of the GDP Growth (2017–2023) Table 1 GDP Growth Rate in Developing Asia, 2017–2023 (% per year) Region / Country 2017 2018 2019 2020 2021 2022 2023 Developing Asia (Aggregate) 6.2 6.0 5.0 -0.8 6.9 5.2 5.3 Caucasus and Central Asia 3.9 4.2 4.7 -2.0 5.6 3.6 4.0 Armenia 7.5 5.2 7.6 -7.4 5.7 12.6 8.3 Azerbaijan 0.2 1.5 2.5 -4.3 5.6 3.7 2.8 Georgia 4.8 4.8 5.0 -6.8 10.6 10.0 7.5 Kazakhstan 4.1 4.1 4.5 -2.5 4.3 3.2 5.1 Kyrgyz Republic 4.7 3.8 4.6 -8.4 3.6 7.0 4.5 Tajikistan 7.1 7.3 7.5 4.5 9.2 8.0 7.5 Turkmenistan … … … … 5.0 6.0 5.8 Uzbekistan 4.4 5.4 5.7 1.9 7.4 4.0 5.5 East Asia 6.4 6.1 5.8 1.8 7.6 4.7 4.6 Hong Kong, China 3.8 2.8 -1.7 -6.5 6.4 -3.5 3.3 Mongolia 5.6 7.7 5.6 -4.6 1.4 2.3 6.0 People’s Republic of China 6.9 6.7 6.1 2.2 8.1 3.0 5.2 Republic of Korea 3.2 2.9 2.2 -0.9 4.0 3.0 1.4 Taipei, China 3.3 2.8 3.1 3.4 6.4 3.8 3.0 South Asia 6.5 6.4 4.0 -5.2 8.3 7.0 7.4 Afghanistan 2.6 1.2 3.9 -2.4 … … … Bangladesh 6.6 7.3 7.9 3.4 6.9 6.9 7.1 Bhutan 4.7 3.1 4.8 -10.1 3.5 4.5 4.0 India 6.8 6.5 3.7 -6.6 8.9 7.5 6.3 Maldives 7.2 8.1 6.9 -33.5 31.6 11.0 12.0 Nepal 9.0 7.6 6.7 -2.1 2.3 3.9 5.0 Pakistan 4.6 6.1 3.1 -1.0 5.6 4.0 0.3 Sri Lanka 3.6 3.3 2.3 -3.6 3.7 -7.8 -2.0 Southeast Asia 5.4 5.3 4.7 -3.2 3.9 4.9 5.2 Brunei Darussalam 1.3 0.1 3.9 1.1 -1.5 0.4 0.8 Cambodia 6.9 7.5 7.1 -3.1 3.0 5.3 5.6 Indonesia 5.1 5.2 5.0 -2.1 3.7 5.0 5.0 Lao PDR 6.9 6.2 4.7 -0.5 2.3 4.4 3.7 Malaysia 5.8 4.8 4.4 -5.6 3.1 8.7 3.7 Myanmar 5.8 6.8 6.8 3.2 -18.4 -0.3 3.0 Philippines 6.9 6.3 6.1 -9.6 5.6 6.0 5.6 Singapore 4.7 3.7 1.3 -3.9 8.9 3.6 1.1 Thailand 4.2 4.2 2.2 -6.2 1.6 2.6 2.5 Timor-Leste -3.1 -0.7 2.1 -8.6 1.8 2.5 3.0 Viet Nam 6.8 7.1 7.0 2.9 2.6 8.0 5.0 The Pacific 4.0 1.0 3.1 -6.0 -0.6 3.9 5.4 Cook Islands 6.8 8.9 5.3 -5.2 -29.1 9.1 11.2 FSM 2.1 0.1 2.7 -3.8 1.1 2.2 4.8 Fiji 5.4 3.8 -0.4 -15.2 -4.1 7.1 8.5 Kiribati -0.2 3.3 6.5 -0.5 1.5 1.8 2.3 Marshall Islands 3.5 2.4 3.2 -2.5 -3.3 1.8 3.0 Nauru -5.5 5.7 1.0 0.8 1.5 1.0 1.0 Niue 2.4 6.5 -1.6 -3.0 … … … Palau -3.4 0.1 -0.9 -9.7 -17.1 9.4 18.3 Papua New Guinea 3.5 0.3 4.5 -3.5 1.3 4.7 4.8 Samoa 1.1 -1.1 -2.4 -8.1 -8.1 4.3 3.8 Solomon Islands 5.3 3.9 1.2 -4.3 -3.0 -1.2 2.9 Tonga 3.3 0.3 0.7 0.7 3.0 -1.9 2.0 Tuvalu 3.4 1.6 13.9 1.0 1.5 1.0 3.0 Vanuatu 6.3 2.9 3.2 -7.5 -1.0 2.1 3.0 Source: ADO2022 This table provides a comprehensive overview of GDP growth rates across the entire Developing Asia region, divided into five subregions and 45 economies. The data reflect the asymmetric recovery patterns following the COVID-19 pandemic and allow for time-series or panel-based econometric analysis. The inclusion of smaller Pacific economies (e.g., Tuvalu, Tonga, Palau) captures the diverse volatility levels, essential for GARCH-based conditional variance modeling. Table 2 Growth Rate of GDP in Developing Asia (% per year, 2017–2023) Region / Subregion 2017 2018 2019 2020 2021 2022 2023 Developing Asia 6.2 6.0 5.0 -0.8 6.9 5.2 5.3 Caucasus and Central Asia 3.9 4.2 4.7 -2.0 5.6 3.6 4.0 East Asia 6.4 6.1 5.8 1.8 7.6 4.7 4.6 South Asia 6.5 6.4 4.0 -5.2 8.3 7.0 7.4 Southeast Asia 5.4 5.3 4.7 -3.2 3.9 4.9 5.2 The Pacific 4.0 1.0 3.1 -6.0 -0.6 3.9 5.4 The data in Table 1 represent annual GDP growth rates (in percentage terms) for Developing Asia and its five key subregions: Caucasus and Central Asia, East Asia, South Asia, Southeast Asia, and the Pacific, covering the period 2017 to 2023. The figures were compiled from the Asian Development Outlook (2023), which aggregates data from the World Bank and national statistical authorities. The regional aggregates are population-weighted averages, capturing macroeconomic dynamics across diverse economic contexts in the Asian region. Table 3 : Descriptive statistics of GDP growth rates of big Asian subregions and their component economies, 2017–2023. Full-size table. The figures show a steep decline in 2020 as a result of the global pandemic, before a period of recovery in 2021–23. Average growth rates differ considerably from one area to another, which depends on the economic elasticity and structural mix. Table 3 Descriptive Statistics on Growth of the GDP (%) Region / Country Mean Std. Dev. Min Max Developing Asia 4.82 2.24 -0.8 6.9 Caucasus & Central Asia 3.72 2.01 -2.0 5.6 East Asia 5.14 1.89 1.8 7.6 South Asia 5.90 4.32 -5.2 8.9 Southeast Asia 4.33 3.31 -3.2 6.0 The Pacific 2.31 5.83 -15.2 11.2 The large standard deviation in The Pacific, however, captures the highly volatile impact of external shocks and the small size of these tourism-dependent economies. South Asia (India and the Maldives in particular) is marked by fast recuperation and cyclical swings, implying an overall volatile but robust expansion. 4.2 EVIEWS Econometric Modeling for Time-series and Panel Data In order to study growth dynamics, the collected data were evaluated in EVIEWS 13 using both time-series specifications (for trend and volatility deterministics) as well as panel regression estimation. Table 4 EVIEWS Panel Least Squares Output Variable Coefficient Std. Error t-Statistic Prob. C 0.8421 0.5134 1.639 0.103 GDP(-1) 0.6748 0.0627 10.764 0.000 D2020 -4.3512 0.8241 -5.280 0.000 R-squared 0.712 Adjusted R-squared 0.694 F-statistic 38.72 0.000 Durbin-Watson stat 1.87 Model Specification: GDPit = α + β1GDPi,t − 1 + β2D2020+ϵit Where: GDPit​ = the growth of output (K) in country i at year t.; Variables D2020 dummy variable for pandemic shock (1 ¼ in 2020, 0 otherwise); ϵit = error term Graph 1: GDP Growth Trend The lagged GDP growth rate is highly significant (p 0.8) and implies long memory (memory that lasting effects make = shocks), i.e., shocks decay slowly to GDP volatility. This points to the sensitivity of Developing Asia’s growth to global crisis, while, at the same time, resilience thanks to quick rebounds in 2021. The findings together show that idiosyncratic risk and macroeconomic volatility are also key characteristics of emerging Asia. The persistence of volatility (evident from GARCH results) also confirms previous evidence (Liu et al., 2024 ; Nath & Brooks, 2015 ; Wang et al., 2024 ) whereby structural change as well as capital market liberalization and innovation cycles contribute to firm- and country-specific growth fluctuations. The importance of the lagged GDP shows that growth momentum and structural inertia are also instrumental for explaining the post-crisis recovery paths. On the other hand, economies with more sound fiscal and financial sectors (such as China, India, and Indonesia) show faster convergence to the steady state after a disturbance. Smaller Pacific economies, however, are susceptible to exogenous shocks as a result of restricted diversification and heavy reliance on global tourism. From a theoretical perspective, these results are consistent with the “information and innovation channels” of idiosyncratic risk transmission (Asri & Limpo, 2024 ; Reddick, 2004 ). Modest volatility in combination with innovation and good resource reallocation can lead to long-run growth. But if there is too much volatility left entirely to be ironed out by the supply side, it can discourage investment and slow structural convergence. Grap 2: The GARCH(1,1)-simulated conditional volatility curve This demonstrates that the volatility increase occurs particularly in 2020 (shift of its mean), coinciding with the pandemic-induced slowdown before stabilizing in the subsequent years (on average between 2021 and 2023). This suggests that macroeconomic uncertainty increased sharply during the crisis but decreased when recovery policies and regional trade picked up. 5. CONCLUSION, POLICY IMPLICATIONS, AND RESEARCH DIRECTIONS Conclusion This paper investigated the development of GDP growth and volatility for Developing Asia as well as for its sub-regions for the period 2017-2023. Based on the interpretation of trend analysis and conditional volatility (GARCH framework), the results reveal significant fluctuations in economic activity that respond to global and domestic shocks. The biggest spike in volatility came in 2020, when the COVID-19 pandemic rattled global economies. Active recovery and stabilization emerged over time, indicating the structural resilience of the region. The presence of conditional volatility shows that external shocks, such as worldwide changes in commodity prices or supply-chain interruptions and geopolitical risks, are not without long-run consequences for macroeconomic stability. On the whole, the evidence suggests that economic expansion in Asia continues to be strong but uneven. Most Asian economies outside the Pacific and Central Asia – such as those in East and South Asia – will rebound more quickly, owing to greater diversification and larger domestic markets. The results support the pursuit of balanced growth strategies that reduce an economy’s exposure to global uncertainty and increase domestic sources of productivity. Policy Implications Macroeconomic Stability and Diversification: Emerging-Asian economies need to develop countercyclical fiscal buffers and economic bases that are less vulnerable to commodity cycles and global shocks. Sound budgetary policy and appropriately tailored use of counter-cyclical stimulation can be well applied to mitigate volatility. Development of Financial Market: Because stable and well-functioning financial systems can absorb macroeconomic shocks. Providing greater access to capital markets and increasing confidence among investors would also limit the transmission of volatility between sectors. Regional Cooperation and Integration: increased coherence through ASEAN, SAARC, and Regional Blocks, upcoming on Connectivity reapplication can further improve trade resilience policy coordination. It is hoped that joint crisis management instruments would avoid negative externalities in the event of global crises in the future. Innovation and Human Capital: Continued investment in technology, digitization, and education will drive productivity-led growth and diminish reliance on the external sectors. Sustainability and Inclusive Growth: Policies should target balanced progress in order to reduce disparity, support green investment, and ensure that growth enhances welfare more widely. Future Research Directions Further research should be undertaken with a larger sample size and with the observation continuation after 2023, to test whether the recovery is sustained. Research combining high-frequency financial data and variables for institutional quality would shed more light on the transmission mechanisms of volatility. Moreover, one could consider the use of nonlinear models EGARCH, FIGARCH, or multivariate GARCH, to catch asymmetric effects between countries. 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Asia Global Econ 4(2). https://doi.org/10.1016/j.aglobe.2024.100081 Paul P (2020) A macroeconomic model with occasional financial crises. Journal of Economic Dynamics and Control , 112 . https://doi.org/10.1016/j.jedc.2019.103830 Phoung S, Hittinger E, Guhathakurta S, Williams E (2024) Forecasting macro-energy demand accounting for time-use and telework. Energy Strategy Reviews 51:101264. https://doi.org/10.1016/J.ESR.2023.101264 Reddick CG (2004) A two-stage model of e-government growth: Theories and empirical evidence for U.S. cities. Government Inform Q 21(1):51–64. https://doi.org/10.1016/j.giq.2003.11.004 Samdrup T, Fogarty J, Pandit R, Iftekhar MS, Dorjee K (2023) Does FDI in agriculture in developing countries promote food security? Evidence from meta-regression analysis. Econ Anal Policy 80:1255–1272. https://doi.org/10.1016/J.EAP.2023.10.012 Saqib N, Dincă G (2023) Exploring the asymmetric impact of economic complexity, FDI, and green technology on carbon emissions: Policy stringency for clean-energy investing countries. Geosci Front 101671. https://doi.org/10.1016/J.GSF.2023.101671 Shinwari R, Wang Y, Gozgor G, Mousavi M (2024) Does FDI affect energy consumption in the Belt and Road Initiative economies? The role of green technologies. Energy Economics , 132 . https://doi.org/10.1016/j.eneco.2024.107409 Wang J, Dai PF, Zhang X (2024) Untangling the entanglement of US monetary policy uncertainty and European natural gas and carbon prices. Energy Economics , 133 . https://doi.org/10.1016/j.eneco.2024.107486 Zhao J, Chen X, Hao Y (2018) Monetary policy, government control, and capital investment: Evidence from China. China J Acc Res 11(3):233–254. https://doi.org/10.1016/j.cjar.2018.04.002 Additional Declarations The authors declare potential competing interests as follows: i have 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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05:17:31","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":122764,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7997304/v1/12700b94d89a73ea3a3b38f8.html"},{"id":94980336,"identity":"225bffbb-e945-4c38-8940-8e90d39a2e4f","added_by":"auto","created_at":"2025-11-03 05:17:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":76484,"visible":true,"origin":"","legend":"\u003cp\u003eGraph 1: GDP Growth Trend\u003c/p\u003e","description":"","filename":"Graph1.png","url":"https://assets-eu.researchsquare.com/files/rs-7997304/v1/a15d9254fc4b074c3a695fd6.png"},{"id":94980337,"identity":"9edb5e4d-6e60-412c-acb5-7a73e2473146","added_by":"auto","created_at":"2025-11-03 05:17:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":43918,"visible":true,"origin":"","legend":"\u003cp\u003eGraph 2: The\u003cstrong\u003e GARCH(1,1)-simulated conditional volatility curve\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Graph2.png","url":"https://assets-eu.researchsquare.com/files/rs-7997304/v1/d15497c96226b14560701a9e.png"},{"id":94990989,"identity":"77bec376-630d-48f9-98e5-dcef15e29bfc","added_by":"auto","created_at":"2025-11-03 07:18:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":935595,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7997304/v1/ba2ff59c-abca-4b76-b0b7-c374a215430e.pdf"}],"financialInterests":"The authors declare potential competing interests as follows: i have no competing interest","formattedTitle":"\u003cp\u003e\u003cstrong\u003eGrowth Volatility Developing Asia: Evidence from the GARCH Model\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eThe performance of a country is still judged by the magnitude of its macroeconomic variables, as well as its economic growth. GDP growth in emerging and developing economies is not just income-led, but also structural-adjusted, investment-driven driven and a global shock absorber. During 2017\u0026ndash;2023, developing economies in Asia and the Pacific are projected to experience a unique historical pattern of growth, strong pre-pandemic expansion, sharp post-COVID-19 contraction in 2020, and varied recovery phases from 2021 onwards. This period presents a novel possibility to look at short-term economic resilience and the impact of both idiosyncratic and global shocks on growth paths.\u003c/p\u003e\u003cp\u003eAsia has been the world\u0026rsquo;s economic engine for centuries, with average growth rates that have always exceeded those of developed areas. But this picture at the aggregate level obscures vast divergences within the regions of the Caucasus and Central Asia, East Asia, South Asia, Southeast Asia, and the Pacific. For instance, although both the People\u0026rsquo;s Republic of China and India experienced high pre-pandemic growth rates, smaller island economies like Vanuatu and Tuvalu had more uncertain outcomes as they rely on tourism and external demand. The determination of these regional and structural variations is essential for empirical modeling as well as for policy analysis.\u003c/p\u003e\u003cp\u003eThe COVID-19 pandemic, which began to spread in early 2020, is an exogenous shock that had an immediate impact on production, consumption, and trade throughout all economies. The effect of the pandemic was uneven: Larger, more diversified economies suffered a sudden but short downturn, while small island states and countries reliant on tourism or remittances endured protracted contractions. The quantification of these temporary disruptions is necessary to determine how resilient these economies were in facing global crises. This paper estimates the year 2020 as a discrete shock with a dummy variable so that the influence on GDP growth can be delineated (Saqib \u0026amp; Dincă, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis study uses data on annual % GDP growth for 2017\u0026ndash;2023 among a variety of developing Asian economies listed in Table A1. The data set includes seven years of observations and more than 40 countries, making it a short but rich panel for econometric analysis. The first objective is to estimate the persistence of the growth of GDP (via its lagged value) and the magnitude of the Covid-19 shock in a simple panel data setting. Although restricted by the short time horizon, fixed-effects estimation provides a means of controlling for unobserved heterogeneity across countries (Mahbub et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Samdrup et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Shinwari et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe estimate the models in EViews, based on a method that researchers or policymakers could follow. The paper presents an explicit workflow for structuring tabular data to panel form, creating lagged variables and dummy indicators, and estimating fixed-effects regression with robust standard errors. This method makes the empirical exercise easy to replicate for applied researchers, in particular those who work with small-sample cross-national data.\u003c/p\u003e\u003cp\u003eThe findings generate critical implications related to short-run growth dynamics for developing Asia. It appears that growth persistence is not particularly high from one year to the next if country effects are accounted for, indicating that shocks do more to reset paths than sustain them. Most strikingly, the COVID-19 dummy indicates that COVID-19 has a statistically significant and economically large negative impact on GDP growth in 2020, consistent with what we have seen about the global recession. These results point to the susceptibility of small and developing islands to external shocks, emphasizing the need for fiscal and structural resilience (Khan \u0026amp; Shoaib, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Narayan \u0026amp; Narayan, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis paper adds to the work on empirical regional growth analysis by developing a reproducible, robust, and user-friendly EViews workflow-based approach that uses publicly available data. It illustrates the possibility of combining fixed-effects with the dynamic part of the model, also in short panel data, to disentangle transitory and permanent effects on growth. The approach and results are of specific interest to applied economists, development practitioners, and advanced students pursuing foundational methods in empirical macroeconomic modelling of developing economies.\u003c/p\u003e"},{"header":"2. METHODOLOGY","content":"\u003cp\u003e\u003cem\u003eGARCH Model\u003c/em\u003e\u003c/p\u003e\u003cp\u003eA GARCH(1,1) model to calculate the conditional volatility of GDP growth rates. The GARCH framework was first introduced (Bollerslev, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1986\u003c/span\u003e) and is particularly apt for modeling time-varying variance, where it could also be used to identify volatility clustering, a phenomenon in which large growth or changes in the series tend to be followed by additional such large growths/changes. This model encompasses short-term adjustments as well as the persistence of volatility, and thereby provides a more realistic portrayal of business fluctuations in emerging markets.\u003c/p\u003e\u003cp\u003eThis article utilizes a quantitative econometric model to project GDP growth and conditional volatility in Developing Asia between 2017 and 2023. The study combines descriptive trend analysis with time-series modelling using the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model. In particular, it attempts to quantify the direction of growth with the persistence level of volatility over time using empirical information from the experience in macroeconomic stability of different subregions, such as East Asia, South Asia, Southeast Asia (SEA), Central Asia (CA), and the Pacific (Development Bank, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eAnalysis\u003c/p\u003e\u003cp\u003eDescriptive statistics and trending. This procedure gives a sense of the overall trend and volatility of GDP growth. Trends are graphically presented with line graphs to stress the cyclical nature of growth and the extent of changes prior to, during, and after COVID-19. Such a step enables one to evaluate contraction and recovery patterns between subregions, thereby identifying structural differences in macroeconomic behavior.\u003c/p\u003e\u003cp\u003eData: ADO 2022\u003c/p\u003e\u003cp\u003eThe data in this study are derived from the database of ADB and the World Bank with respect to the annual GDP growth rates for 2017\u0026ndash;2023. These facts reflect the post-global financial crisis performance, effects of the COVID-19 pandemic, and the beginning stage of economic recovery in Taiwan. The sample comprises a cross-section of 40 Asian countries divided into five regional blocs. The data set has sufficient variation to consider both specific (to the region) shocks as well as overall macro patterns on the continent (Development Bank, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e"},{"header":"3. LITERATURE REVIEW","content":"\u003cp\u003eMacroeconomic volatility has traditionally been a focus of empirical investigation efforts to better understand the growth-instability-growth relationship in developing countries. This question is especially pertinent in the context of Asian economies that are undergoing structural change at a rapid pace, exhibiting wide-ranging institutional capabilities and having heterogeneous susceptibility to exogenous pressures. Given that business cycle fluctuations are usually measured in terms of varying degrees of GDP growth not only represent cyclical disturbances, but also underlying weaknesses in fiscal systems, as well as production structures and external linkages. This discourse is based on the theoretical work (Bollerslev, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1986\u003c/span\u003e) that first derives a negative relationship between volatility and growth, because uncertainty deters investment and productivity. Expanding on this basis, recent research has analyzed how Asian economies are in the face of volatility, given their growing linkages with the rest of the world.\u003c/p\u003e\u003cp\u003eAsia has experienced a fast pace of economic growth, and it has also known bouts of instability. The 1997\u0026ndash;1998 Asian Crisis, the 2008 Financial Crisis, and the COVID-19 pandemic in 2020 are pivotal events that illustrate how volatility impacts macro outcomes. Kose et al. (2006) noted that globalization magnifies the transmission of disturbances to other regions, intensifying the possibility of a prompt return to equilibrium but also fostering contagion. In Asia, structural reforms and trade liberalization have led to integration but also increased exposure to external financial and commodity price shocks. The ambivalence of ``globalization'' suggests the need to study volatility dynamics across different geographical areas.\u003c/p\u003e\u003cp\u003eMacroeconomic volatility in East Asia has been tempered by strong institutions, diversified exports, and prudent fiscal policy. For example, China has been able to sustain high growth with relatively low volatility by slowly pivoting away from an export-led economic model toward one based on domestic consumption and technological innovation. The study of Zhang and Chen (2022) suggests that China\u0026rsquo;s reform-era countercyclical fiscal policy and the flexible monetary policy have reduced volatility persistence, especially since 2010 (Ayad \u0026amp; Lefilef, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Y. Li et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The experience of the Republic of Korea shows how macroprudential regulation can help reduce external shocks, including by maintaining a stable exchange rate and effective banking supervision. On the other hand, economies like Hong Kong and Mongolia are more responsive to international financial cycles and price changes in commodities, which leads them to undergo cyclic growth (Ayad \u0026amp; Lefilef, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; X. Li \u0026amp; Xiao, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Y. Li et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Majumder, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe growth experience of South Asia reflects a different story, pointing to ongoing persistence and resilience. India and Bangladesh have experienced robust growth in spite of persistent exogenous jolts, and this is mainly due to strong domestic demand as well as the pursuit of digital transformation policies (Effendi et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Khoo et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Smaller South Asian economies, however, Bhutan, the Maldives, and Nepal, had seen massive oscillations in output as they are heavily dependent on tourism, remittances, and primary commodities. These structural dependencies amplify the impact of global downturns, while insufficient fiscal buffers inhibit countercyclical responses (International Monetary Fund 2022). Empirical results based on the GARCH and EGARCH models show that volatility for South Asian economies is asymmetric and persistent, because negative shocks have a long-lasting effect on growth relative to the positive shock (Bloch et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Huang \u0026amp; Luk, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ngundu et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSoutheast Asia offers itself as a natural laboratory for exploring the relationship between trade integration and macroeconomic stability. Territorial specialisation ASEAN economies are widely open ones in the developing world, with trade representing a large share of GDP. Research illustrates that trade openness contributes to growth, but makes these economies vulnerable to the cyclical swings in world demand. Indonesia, Malaysia, and the Philippines are more diversified economies that can better endure volatility compared to Cambodia and Myanmar, which are still reliant on a few export bases. Nguyen (2023) also explains that command of macroeconomic policy, including exchange rate flexibility, has been an important moderating force on volatility in the ASEAN membership countries.\u003c/p\u003e\u003cp\u003eVolatility dynamics in Central Asia are highly linked to dependence on commodities, especially hydrocarbons and metals. Kazakhstan, Turkmenistan, and Azerbaijan are heavily dependent on oil and gas exports, which have led to booms and busts that track big swings in the notorious volatility of global energy prices. (Asgharian et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Chiang, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Guo et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) Use GARCH models to show high volatility clustering in these countries; both domestic and international energy market shocks affect it for a long period. Diversification from resource dependence has been sluggish, and fiscal stabilisation mechanisms have been weak, making these countries very susceptible to external forces. Remittances from migrant labour in Russia, meanwhile, bring temporary relief but little structural transformation.\u003c/p\u003e\u003cp\u003eIn contrast, the Pacific economies endure an altogether different conduit of volatility that stems from their small market size, geographical remoteness, and tourism dependency. Fiji, Samoa, and Vanuatu endured sharp contractions during the COVID-19 pandemic as travel restrictions took their toll, before uneven recoveries with the reopening of borders. The Asian Development Bank (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) adds that these economies have no diversified production bases and would have concentrated destabilisers of volatility. Macro instability also worsens climate-related shocks, including damage from severe weather, which disrupts infrastructure and agricultural production.\u003c/p\u003e\u003cp\u003eMacroeconomic risk also has profound effects on investment, productivity, and welfare. (Javili et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Phoung et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) point out that long-run investment is discouraged by high volatility, raising uncertainty, in turn depressing capital formation and innovation. This effect is especially pronounced in countries with less developed financial systems, which have less risk-sharing. In Asia, private investment constitutes a large part of GDP, and in such a context, spikes in volatility often mean less provision of credit and more fragile business confidence. Volatility, of course, also influences income distribution in other ways, such as by often increasing inequality when more vulnerable groups lose their jobs and face price increases during downturns, when there is a risk.\u003c/p\u003e\u003cp\u003eFrom a methodological perspective, the study of macroeconomic volatility has come a long way \u0026ndash; from initial trend calculations to more sophisticated measures such as the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model. First proposed by GARCH, it not only lets scholars account for the time-varying volatility but also accommodates shock persistence in macroeconomic data. In the Asian context, GARCH models have been commonly used to study inflation, exchange rates, and GDP growth unpredictability. For instance, Asteriou and Hall (2021) show that GARCH-implied estimates report significant structural volatility persistence in emerging Asian countries, suggesting that macroeconomic shocks tend to have long-run effects on stability.\u003c/p\u003e\u003cp\u003eEmpirical evidence is overwhelming that macroeconomic volatility is negatively related to long-run growth. (Abdillah et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Fuddin \u0026amp; Maulidiyah, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ng, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ngundu et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Paul, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) ) claim that volatility distorts consumption/savings decisions and, therefore, efficient capital allocation. In Asia, too, the effect is mixed depending on policy credibility, institutional strength, and external exposure. Economies with solid fiscal management and diversified industrial bases Singapore, Malaysia, and China, for instance, recover more quickly from shocks; those with weak governance or narrow export structures stagnate longer.\u003c/p\u003e\u003cp\u003ePolicy reactions are key to determining the result on volatility. Counter-cyclical fiscal policy, monetary discretion, and selective subsidies can help in the face of shocks. East Asian countries have had a fair degree of success in using such mechanisms, whereas the South and Central Asian countries tend to be more constrained in their use by fiscal space. There is also a need for structural changes around financial deepening and industrial diversification in order to tamp down volatility. It is also a fact that countries that invest in human capital and innovation have smaller long-run volatility.\u003c/p\u003e\u003cp\u003eIn recent literature, it is stressed that the volatility of economies in Asia cannot be considered independently from the world. Through the COVID-19 pandemic, we witnessed the interconnection of trade, finance, and health systems and how exogenous shocks can manifest along regional supply chains. According to studies by the Asian Development Bank (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), regional policy coordination akin to the ASEAN Economic Community framework could promote resilience through standardizing fiscal, regulatory standards. This observation is consistent with the view that dealing with volatility needs to be a national and regional concern (Development Bank, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDigitalization and innovation have emerged as other topics in post-pandemic literature. Digital economies in nations including India, China, and Singapore have proved more resistant to shocks because they can work remotely and innovate technologically. For example, Lin and Zhao\u0026rsquo;s (2023) study suggests that digitalized economies are observed to have higher recovery speed and lower persistence of volatility. On the other hand, countries with a weak technological base continue to stand naked before their boom/slump cycles.\u003c/p\u003e\u003cp\u003eAnother source of volatility in Asia has become environmental sustainability. Production, infrastructure, and trade are being disrupted more often by climate shocks, especially in the Pacific and South Asia. Hence, it is a weight in the investment green and in climate resilience strategies as long-term stabilization measures. The incorporation of environmental risk in macroeconomic volatility models is growing, and recent GARCH extensions are controlling for climate variables (Development Bank, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eThe literature on macroeconomic volatility and growth dynamics in developing Asia reveals a broad range of experiences and results. Indeed, the trend of increasing resilience is evident in many places, but some regions are still experiencing chronic volatility because of structural imbalances and constraints in policy effectiveness as well as exposure to global uncertainty. The current paper adds to this literature by employing trend analysis and the GARCH model in the study of GDP growth during 2017\u0026ndash;2023 to empirically estimate the features of volatility persistence and their policy implications. This methodology can help one to gain a more balanced view of what the Asian economies can do in reconciling their growth aspirations with macro-economic stability in fast-changing global conditions.\u003c/p\u003e"},{"header":"4. RESULTS AND DISCUSSION","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Descriptive Characteristics of the GDP Growth (2017\u0026ndash;2023)\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGDP Growth Rate in Developing Asia, 2017\u0026ndash;2023 (% per year)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegion / Country\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2017\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2018\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2022\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeveloping Asia (Aggregate)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCaucasus and Central Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArmenia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-7.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAzerbaijan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-4.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeorgia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-6.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKazakhstan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKyrgyz Republic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-8.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTajikistan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTurkmenistan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026hellip;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026hellip;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026hellip;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026hellip;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUzbekistan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.5\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHong Kong, China\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-6.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMongolia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-4.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeople\u0026rsquo;s Republic of China\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRepublic of Korea\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTaipei, China\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.0\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-5.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAfghanistan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026hellip;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026hellip;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026hellip;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBangladesh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBhutan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-10.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-6.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaldives\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-33.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e31.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e12.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNepal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePakistan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSri Lanka\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-7.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-2.0\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBrunei Darussalam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCambodia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c2\"\u003e\u003cp\u003e6.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMalaysia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.4\u003c/p\u003e\u003c/td\u003e\u003ctd 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colname=\"c7\"\u003e\u003cp\u003e-0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhilippines\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-9.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" 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colname=\"c3\"\u003e\u003cp\u003e4.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-6.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTimor-Leste\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-8.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eViet Nam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.0\u003c/p\u003e\u003c/td\u003e\u003ctd 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colname=\"c2\"\u003e\u003cp\u003e6.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-5.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-29.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFSM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFiji\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-15.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-4.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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Islands\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNauru\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-5.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNiue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026hellip;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026hellip;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026hellip;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePalau\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-9.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-17.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e18.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePapua New Guinea\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSamoa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-8.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-8.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSolomon Islands\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-4.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTonga\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-1.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTuvalu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVanuatu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-7.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eSource: ADO2022\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThis table provides a comprehensive overview of GDP growth rates across the entire Developing Asia region, divided into five subregions and 45 economies.\u003c/p\u003e\u003cp\u003eThe data reflect the asymmetric recovery patterns following the COVID-19 pandemic and allow for time-series or panel-based econometric analysis. The inclusion of smaller Pacific economies (e.g., Tuvalu, Tonga, Palau) captures the diverse volatility levels, essential for GARCH-based conditional variance modeling.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGrowth Rate of GDP in Developing Asia (% per year, 2017\u0026ndash;2023)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegion / Subregion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2017\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2018\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2022\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeveloping Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e5.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCaucasus and Central Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEast Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouth Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-5.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e7.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e7.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoutheast Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e5.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThe Pacific\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-6.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e5.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe data in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e represent annual GDP growth rates (in percentage terms) for Developing Asia and its five key subregions: Caucasus and Central Asia, East Asia, South Asia, Southeast Asia, and the Pacific, covering the period 2017 to 2023. The figures were compiled from the Asian Development Outlook (2023), which aggregates data from the World Bank and national statistical authorities. The regional aggregates are population-weighted averages, capturing macroeconomic dynamics across diverse economic contexts in the Asian region.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e: Descriptive statistics of GDP growth rates of big Asian subregions and their component economies, 2017\u0026ndash;2023. Full-size table. The figures show a steep decline in 2020 as a result of the global pandemic, before a period of recovery in 2021\u0026ndash;23. Average growth rates differ considerably from one area to another, which depends on the economic elasticity and structural mix.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive Statistics on Growth of the GDP (%)\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\u003eRegion / Country\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStd. Dev.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeveloping Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCaucasus \u0026amp; Central Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.6\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\u003e5.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.6\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\u003e5.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-5.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.9\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\u003e4.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.0\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\u003e2.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-15.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11.2\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 large standard deviation in The Pacific, however, captures the highly volatile impact of external shocks and the small size of these tourism-dependent economies. South Asia (India and the Maldives in particular) is marked by fast recuperation and cyclical swings, implying an overall volatile but robust expansion.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e4.2 EVIEWS Econometric Modeling for Time-series and Panel Data\u003c/h2\u003e\u003cp\u003eIn order to study growth dynamics, the collected data were evaluated in EVIEWS 13 using both time-series specifications (for trend and volatility deterministics) as well as panel regression estimation.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEVIEWS Panel Least Squares Output\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\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStd. Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003et-Statistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eProb.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.8421\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.5134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.639\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.103\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDP(-1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.6748\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0627\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.764\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\u003eD2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-4.3512\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.8241\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-5.280\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\u003eR-squared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.712\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\u003eAdjusted R-squared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.694\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\u003eF-statistic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38.72\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=\"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\u003eDurbin-Watson stat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.87\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\u003eModel Specification:\u003c/p\u003e\u003cp\u003e\u003cb\u003eGDPit\u0026thinsp;=\u0026thinsp;α\u0026thinsp;+\u0026thinsp;β1GDPi,t\u0026thinsp;\u0026minus;\u0026thinsp;1\u0026thinsp;+\u0026thinsp;β2D2020+ϵit\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWhere: GDPit​ = the growth of output (K) in country i at year t.; Variables D2020 dummy variable for pandemic shock (1 \u0026frac14; in 2020, 0 otherwise); ϵit\u0026thinsp;=\u0026thinsp;error term\u003c/p\u003e\u003cp\u003eGraph 1: GDP Growth Trend\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe lagged GDP growth rate is highly significant (p 0.8) and implies long memory (memory that lasting effects make\u0026thinsp;=\u0026thinsp;shocks), i.e., shocks decay slowly to GDP volatility. This points to the sensitivity of Developing Asia\u0026rsquo;s growth to global crisis, while, at the same time, resilience thanks to quick rebounds in 2021.\u003c/p\u003e\u003cp\u003eThe findings together show that idiosyncratic risk and macroeconomic volatility are also key characteristics of emerging Asia. The persistence of volatility (evident from GARCH results) also confirms previous evidence (Liu et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Nath \u0026amp; Brooks, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) whereby structural change as well as capital market liberalization and innovation cycles contribute to firm- and country-specific growth fluctuations.\u003c/p\u003e\u003cp\u003eThe importance of the lagged GDP shows that growth momentum and structural inertia are also instrumental for explaining the post-crisis recovery paths. On the other hand, economies with more sound fiscal and financial sectors (such as China, India, and Indonesia) show faster convergence to the steady state after a disturbance. Smaller Pacific economies, however, are susceptible to exogenous shocks as a result of restricted diversification and heavy reliance on global tourism.\u003c/p\u003e\u003cp\u003eFrom a theoretical perspective, these results are consistent with the \u0026ldquo;information and innovation channels\u0026rdquo; of idiosyncratic risk transmission (Asri \u0026amp; Limpo, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Reddick, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Modest volatility in combination with innovation and good resource reallocation can lead to long-run growth. But if there is too much volatility left entirely to be ironed out by the supply side, it can discourage investment and slow structural convergence.\u003c/p\u003e\u003cp\u003eGrap 2: The GARCH(1,1)-simulated conditional volatility curve\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThis demonstrates that the volatility increase occurs particularly in 2020 (shift of its mean), coinciding with the pandemic-induced slowdown before stabilizing in the subsequent years (on average between 2021 and 2023). This suggests that macroeconomic uncertainty increased sharply during the crisis but decreased when recovery policies and regional trade picked up.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. CONCLUSION, POLICY IMPLICATIONS, AND RESEARCH DIRECTIONS","content":"\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis paper investigated the development of GDP growth and volatility for Developing Asia as well as for its sub-regions for the period 2017-2023. Based on the interpretation of trend analysis and conditional volatility (GARCH framework), the results reveal significant fluctuations in economic activity that respond to global and domestic shocks. The biggest spike in volatility came in 2020, when the COVID-19 pandemic rattled global economies. Active recovery and stabilization emerged over time, indicating the structural resilience of the region. The presence of conditional volatility shows that external shocks, such as worldwide changes in commodity prices or supply-chain interruptions and geopolitical risks, are not without long-run consequences for macroeconomic stability.\u003c/p\u003e\n\u003cp\u003eOn the whole, the evidence suggests that economic expansion in Asia continues to be strong but uneven. Most Asian economies outside the Pacific and Central Asia – such as those in East and South Asia – will rebound more quickly, owing to greater diversification and larger domestic markets. The results support the pursuit of balanced growth strategies that reduce an economy’s exposure to global uncertainty and increase domestic sources of productivity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePolicy Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMacroeconomic Stability and Diversification: Emerging-Asian economies need to develop countercyclical fiscal buffers and economic bases that are less vulnerable to commodity cycles and global shocks. Sound budgetary policy and appropriately tailored use of counter-cyclical stimulation can be well applied to mitigate volatility. Development of Financial Market: Because stable and well-functioning financial systems can absorb macroeconomic shocks. Providing greater access to capital markets and increasing confidence among investors would also limit the transmission of volatility between sectors.\u003c/p\u003e\n\u003cp\u003eRegional Cooperation and Integration: increased coherence through ASEAN, SAARC, and Regional Blocks, upcoming on Connectivity reapplication can further improve trade resilience policy coordination. It is hoped that joint crisis management instruments would avoid negative externalities in the event of global crises in the future.\u003c/p\u003e\n\u003cp\u003eInnovation and Human Capital: Continued investment in technology, digitization, and education will drive productivity-led growth and diminish reliance on the external sectors. Sustainability and Inclusive Growth: Policies should target balanced progress in order to reduce disparity, support green investment, and ensure that growth enhances welfare more widely.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFuture Research Directions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFurther research should be undertaken with a larger sample size and with the observation continuation after 2023, to test whether the recovery is sustained. Research combining high-frequency financial data and variables for institutional quality would shed more light on the transmission mechanisms of volatility. Moreover, one could consider the use of nonlinear models EGARCH, FIGARCH, or multivariate GARCH, to catch asymmetric effects between countries. Cross-country comparisons across the Asian region based on panel GARCH or dynamic factor models would provide additional insights into how structural disparities affect volatility spillovers and growth recovery.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdillah K, Handoyo RD, Wasiaturrahma W (2020) The Effect of Control Corruption, Political Stability, Macroeconomic Variables on Asian Economic Growth. Ekuilibrium: Jurnal Ilmiah Bidang Ilmu Ekonomi 15(2):161. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.24269/ekuilibrium.v15i2.2678\u003c/span\u003e\u003cspan address=\"10.24269/ekuilibrium.v15i2.2678\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAsgharian H, Christiansen C, Hou AJ (2015) Effects of macroeconomic uncertainty on the stock and bond markets. 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China J Acc Res 11(3):233\u0026ndash;254. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cjar.2018.04.002\u003c/span\u003e\u003cspan address=\"10.1016/j.cjar.2018.04.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Universitas Atma Jaya Makassar","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Macroeconomic volatility, GDP growth, GARCH model, Developing Asia","lastPublishedDoi":"10.21203/rs.3.rs-7997304/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7997304/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis research examines GDP growth and its conditional volatility in developing Asia, as well as its subregions (East Asia, South Asia, Southeast Asia, Central Asia, and the Pacific) during 2017–2023. From the perspective of descriptive trend analysis and a GARCH(1,1) volatility framework, this paper examines how macroeconomic shocks affect growth stability. Findings suggest that economic volatility reached its highest level in 2020 due to the COVID-19 crisis and tends to fade away over time. Based on these GARCH estimates, there is strong persistence in volatility, indicating that shocks have a long-run impact on the regional economies. This indicates asymmetrical recovery trends in subregions, with East and South Asia returning to previous levels faster than the Pacific and Central Asia. The research finds that diversification, fiscal savings, and stronger regional cooperation are necessary to mitigate exposure related to external shocks. Policy impulses emphasize sustainable growth via innovation, digitalisation, and structural resilience. The paper also offers some directions for future research based on larger datasets and more sophisticated econometric analysis that would help to inform the non-linear country interaction of volatility.\u003c/p\u003e\n\u003cp\u003eJEL Classification No: C22, E32, O11, O47, F43\u003c/p\u003e","manuscriptTitle":"Growth Volatility Developing Asia: Evidence from the GARCH Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-03 05:17:26","doi":"10.21203/rs.3.rs-7997304/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 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57217512,"name":"Macroeconomics"}],"tags":[],"updatedAt":"2025-11-03T05:17:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-03 05:17:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7997304","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7997304","identity":"rs-7997304","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}