2013
DOI: 10.1016/s2212-5671(13)00056-7
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Comparing the Performances of GARCH-type Models in Capturing the Stock Market Volatility in Malaysia

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Cited by 85 publications
(75 citation statements)
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“…Based on the lowest forecasting errors finding for MAE, MSE, RMSE, GARCH (1, 1) model was found to be a robust model for all three stock markets in Malaysia, Indonesia, and Japan and EWMA model which was found to be a better model for Hong Kong stock market. In the case of Malaysia, results obtained for this study are consistent with the findings in Angabini and Wasiuzzaman (2010), Kosapattarapim, Lin and Mccrae (2011) and Lim and Sek (2013) that the appropriate for forecasting model for Malaysia stock market is GARCH (1, 1), as compared to other forecasting models. In overall, GARCH (1, 1) appears to be the better forecasting model for most stock markets in the sample, which confirms the claim made by Minkah (2007).…”
Section: Resultssupporting
confidence: 89%
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“…Based on the lowest forecasting errors finding for MAE, MSE, RMSE, GARCH (1, 1) model was found to be a robust model for all three stock markets in Malaysia, Indonesia, and Japan and EWMA model which was found to be a better model for Hong Kong stock market. In the case of Malaysia, results obtained for this study are consistent with the findings in Angabini and Wasiuzzaman (2010), Kosapattarapim, Lin and Mccrae (2011) and Lim and Sek (2013) that the appropriate for forecasting model for Malaysia stock market is GARCH (1, 1), as compared to other forecasting models. In overall, GARCH (1, 1) appears to be the better forecasting model for most stock markets in the sample, which confirms the claim made by Minkah (2007).…”
Section: Resultssupporting
confidence: 89%
“…However, to forecast well stock market volatility, having a reliable forecasting model is essential. Past studies fail to arrive at a common conclusion in terms of forecasting models for a specific stock market (Adebayo & Sivasamy, 2014;Lim & Sek, 2013;Ladokhin, 2009;Febrian, 2006;Schwert, 1990), which may be due to the variation in their selected data samples and study periods (Wilhelmsson, 2006). It is a common belief that stock markets in developed countries are less volatile as compared to those in developing countries due to the weaker economic fundamentals often present in emerging markets.…”
mentioning
confidence: 99%
“…Gokcan [12] found that for emerging stock markets the GARCH (1, 1) model performed better in volatility prediction of time series data. Lim and Sek [13] modelled the volatility of stock market in Malaysia and established symmetric and asymmetric GARCH models had different performances in different time frames. Kannadhasan et al [14], Joshi [15], Banumathy et al [16], Goudarzi and Ramanarayanan [17] found that the volatility of returns in Indian Stock Market was persistent, asymmetric and asymmetric negative effect was greater than the positive.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Conclusions are described in section 5. Lim & Sek (2013) conducted an empirical study of Malaysian stock market using different GARCH models to identify the volatility of stock market. They used stock market data from January 1990 to December 2010 and divided the three-time parts of crises i.e.…”
Section: Introductionmentioning
confidence: 99%