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Comparison of linear and non-linear GARCH models for forecasting volatility of select emerging countries

机译：线性和非线性GARCH模型对选择新兴国家的波动性的比较

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Purpose - Several empirical studies have proven that emerging countries are attractive destinations for Foreign Institutional Investors (FIIs) because of high expected returns, weak market efficiency and high growth that make them attractive destination for diversification of funds. But higher expected returns come coupled with high risk arising from political and economic instability. This study aims to compare the linear (symmetric) and non-linear (asymmetric) Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models in forecasting the volatility of top five major emerging countries among E7, that is, China, India, Indonesia, Brazil and Mexico. Design/methodology/approach - The volatility of financial markets of five major emerging countries has been empirically investigated for a period of two decades from January 2000 to December 2019 using univariate volatility models including GARCH 1, 1, Exponential Generalized Autoregressive Conditional Heteroscedasticity (E-GARCH 1, 1) and Threshold Generalized Autoregressive Conditional Heteroscedasticity (T-GARCH-1,1) models. Further, to examine time-varying volatility, the distinctions of structural break have been captured in view of the global financial crisis of 2008. Thus, the period under the study has been segregated into pre- and post-crisis, that is, January 2001-December 2008 and January 2009-December 2019, respectively. Findings - The findings indicate that GARCH (1,1) model is superior to non-linear GARCH models for forecasting volatility because the effect of leverage is insignificant. China has been considered as most volatile, whereas India is volatile but positively skewed and Indonesia is the least volatile country. The results can help investors in better international diversification of their portfolio and identifying best suitable hedging opportunities. Practical implications - This study can help investors to construct a more risk-adjusted returns international portfolio. Further, it adds to the scant literature available on the inconclusive debate on the choice of linear versus non-linear models to forecast market volatility. Originality/value - Earlier studies related to univariate volatility models are mostly applications of the models. Only few studies have considered the robustness while applying the models. However, none of the studies to the best of the authors' searches have considered these models for identifying the diversification opportunity among the emerging countries. Hence, this study is able to derive diversification and hedging opportunities by applying wide ranges of the statistical applications and models, that is, descriptive, correlations and univariate volatility models. It makes the study more rigorous and unique compared to the previous literature.
机译：目的 - 若干实证研究证明，由于高效回报，市场效率疲软和高增长，使新兴国家对外国机构投资者（FIIS）有吸引力的目的地，使其成为资金多样化目的地的有吸引力的目的地。但较高的预期收益与政治和经济不稳定产生的高风险相结合。本研究旨在比较线性（对称）和非线性（非对称）广义自回归条件异素（GARCH）模型在E7中预测前五个主要新兴国家的波动性，即中国，印度，印度尼西亚，巴西和墨西哥。设计/方法/方法 - 五个主要新兴国家的金融市场波动性已经从2000年1月到2019年1月到2019年12月的经验调查了二十几十年，其中包括加粗挥发性模型，包括GARCH 1,1，1，指数广泛的自我评级条件异源性（E- GARCH 1,1）和阈值广义自回归条件异染性（T-GARCH-1,1）模型。此外，为了检查时变波动性，鉴于2008年全球金融危机，已经捕获了结构突破的区别。因此，该研究的期间已被隔离为危机前和危机前，即2001年1月 - 分别为2008年和2009年1月至2019年12月。调查结果表明，GARCH（1,1）模型优于非线性加粗模型，用于预测波动性，因为杠杆的效果是微不足道的。中国被认为是最挥之不便的，而印度则不稳定，但积极倾斜，印度尼西亚是最不挥发的国家。结果可以帮助投资者在他们的投资组合的更好国际多样化中，并确定最佳的套期保值机会。实际意义 - 本研究可以帮助投资者构建一个更具风险调整的回报国际产品组合。此外，它增加了关于线性与非线性模型选择的不确定辩论中可用的不确定辩论，以预测市场波动。原创性/值 - 与单变量波动模型相关的早期研究大多是模型的应用。只有少数研究在应用模型时考虑了鲁棒性。然而，作者搜索中最好的研究都认为这些模型用于识别新兴国家之间的多元化机会。因此，本研究能够通过应用统计应用和模型的广泛范围来实现多样化和对冲机会，即描述性，相关性和单变量波动模型。与以前的文献相比，这项研究更加严谨和独特。

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