Volatility behaviour of BRIC capital markets in the 2008 international financial crisis

Pimenta, Tabajara; Lima, Fabiano Guasti; Gaio, Luiz Eduardo

doi:10.5897/ajbm2013.7162

Cited by 11 publications

(2 citation statements)

References 28 publications

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“…Some authors argue that the best model specification depends on the underlying asset class, and linear GARCH models and non-linear GARCH models are found best fitted for currencies and stock prices, respectively (Hansen and Lunde, 2005; Kat and Heynen, 1994). Tabajara et al . (2014) analysed volatility behaviour of BRIC countries during the 2008 financial crisis.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Sharma

Aggarwal

Yadav

2021

JAMR

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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.

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Section: Literature Reviewmentioning

confidence: 99%

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

Sharma

Aggarwal

Yadav

2021

JAMR

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“…The results showed that the Subprime crisis period turned out to have bigger impact on stock market volatility with high shock persistence and asymmetric effects. Tabajara et al (2014) compared the stock market behaviour of Brazil, Russia, India and China (BRIC) emerging economies to those of the industrialized economy of USA, Japan, United Kingdom and Germany in the light of 2008 global financial crisis using GARCH, EGARCH and TARCH univariate models. The stock market behaviours of the BRIC's emerging markets and the industrialized economies in terms of shock persistence effects on volatility, asymmetry and delayed reaction of volatility to stock market changes were found to be similar in both markets.…”

Section: Introductionmentioning

confidence: 99%

Generalized Autoregressive Conditional Heteroscedasticity (Garch) Models and Optimal for Nigerian Stock Exchange

Eriyeva

Okoli

2022

Int. J. Res. Granthaalayah

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This paper focused on comparative performance of GARCH models, ascertaining the best model fit, estimating the parameters and making prediction from optimal model. The study used UBA daily stock exchange prices sourced from the official websites of www.investing.com,on the daily basis of the Nigeria stock exchange rate over a period of ten years from 06/06/2012 – 04/06/2021. Five GARCH models (SGARCH, GJRGARCH or TGARCH, EGARCH, APGARCH and IGARCH) were fitted to the secondary data set of the Nigerian Stock exchange market for the period of June 2012- June 2021 and the results of the findings were obtained. The AIC results were SGARCH (1,1) (-6.1784), GJRGARCH (1,1) (-6.1778), EGARCH (1,1) (-6.1714) , APGARCH (1,1) (-6.1245) and IGARCH(1,1) with the value of AIC -6.1793. The EGARCH (1, 1) was found to be the optimal model with AIC value of -6.1714. The further findings indicated volatility clustering and leverage effect. The result of the analysis equally showed parameter estimates of the EGARCH (1,1) model and all the parameters were significant including mean and alpha. Prediction using the optimal model was made with an initial out of sample of 200 and n ahead of 200 with predicted values within the 95% confidence interval resulting there is no sign of volatility and clustering. Based on the findings of the study, other time series packages should be compared with GARCH models, data should be making available for easy access and investors should be encouraged to invest in United Bank for Africa (UBA, Nigeria).

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Univariate GARCH Model Generated Volatility Skews for the CIVETS Stock Indices

Labuschagne

Oberholzer

Venter

2017

Advances in Applied Economic Research

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Volatility behaviour of BRIC capital markets in the 2008 international financial crisis

Cited by 11 publications

References 28 publications

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

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

Generalized Autoregressive Conditional Heteroscedasticity (Garch) Models and Optimal for Nigerian Stock Exchange

Univariate GARCH Model Generated Volatility Skews for the CIVETS Stock Indices

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