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

Abstract

PurposeSeveral 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/approachThe 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.FindingsThe 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 implicationsThis 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/valueEarlier 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.

Do green bonds offer a diversification opportunity during COVID-19?—an empirical evidence from energy, crypto, and carbon markets

“…Similarly, the log return of each asset under examination is more pronounced in the COVID-19 pandemic. Further, volatility clustering is seen multiple times in each series, which depicts that high and low changes follow high changes followed by low changes (Sharma et al 2020).…”

“…According to Lopes (1987), "risk refers to situations in which a decision is made whose consequences depend on the outcomes of future events having known probabilities." Portfolio diversification brace risk mitigation to a larger extent (Lassance et al 2022;Zaimovic et al 2021;Sharma et al 2020). The advent of globalization and digitization has dismantled the distinct separation between different financial markets.…”

Assessing the probability of bankruptcy when investing in cryptocurrency

“…Classical and modernized linear programming and regression analysis methods were shown in the work of Meade (2002), in which the author concluded that linear modeling of the rate shows a more accurate forecast compared to non-linear modeling for a local, short period. Meade (2002) used a linear AR-GARCH model in contrast to the work of Sharma et al (2021), which proved the effectiveness of using both symmetric and asymmetric models of autoregressive conditional heteroscedasticity (GARCH) in forecasting the volatility of five developing countries, such as China, India, Indonesia, Brazil, and Mexico.…”

“…http://dx.doi.org/10.21511/imfi.19 (3).2022. 26 At the same time, other cryptocurrencies ceased to exist altogether or their value fell below 1% of the original value. Musialkowska et al (2020) found that bitcoin can be freely used as a currency in Venezuela, where the national currency is devalued due to high inflation.…”

“…It is expected that cryptocurrency is not http://dx.doi.org/10.21511/imfi.19 (3).2022. 26 devaluated by reckless monetary authorities. It should encourage both people and companies to use them as a medium of exchange and a store of value.…”