Comparing the Performances of GARCH-type Models in Capturing the Stock Market Volatility in Malaysia

Lim, Ching Mun; Sek, Siok Kun

doi:10.1016/s2212-5671(13)00056-7

Cited by 83 publications

(75 citation statements)

References 8 publications

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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.…”

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confidence: 99%

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Comparative Study of Volatility Forecasting Models: The Case of Malaysia, Indonesia, Hong Kong and Japan Stock Markets

Lee¹,

Nguyen²

2017

JEW

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the four selected countries, results obtained confirm that volatility in developed markets is not necessarily always lower than the volatility in emerging markets. Among all the three models, GARCH (1, 1) model is found to be the best forecasting model for stock markets in Malaysia, Indonesia, and Japan, while EWMA model is found to be the best forecasting model for Hong Kong stock market. The outperformance of GARCH (1, 1) found supports again what is found in Minkah (2007).

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Section: Resultssupporting

confidence: 89%

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confidence: 99%

Comparative Study of Volatility Forecasting Models: The Case of Malaysia, Indonesia, Hong Kong and Japan Stock Markets

Lee¹,

Nguyen²

2017

JEW

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“…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%

Time Series Analysis of Stock Market Volatility in Pakistan

Rashid

Ismail

Rashid

2020

AJPAS

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The stock market in an emerging country like Pakistan has been volatile from the earliest times. This paper studies the volatility of Pakistan Stock Exchange (PSX) (using Karachi Stock Exchange 100 Index (KSE-100) as a proxy) through the application of Generalized Autoregressive Conditional Heteroscedasticity (GARCH) family models. The sample period consists of 4831 daily observations for the 19 year trading period (from 2000 to 2019). Symmetric GARCH (2, 1), asymmetric EGARCH (1, 1), GJR-GARCH (1, 1) and APARCH (1, 1) models were used under Gaussian distributional assumptions. The results validate the empirical findings of previous studies conducted in Pakistan that log returns of KSE-100 Index are characterized by volatility clustering, time-variability, leptokurtic distribution with dominant ARCH and GARCH effects. An interesting feature of Pakistan Stock Exchange revealed by asymmetric models (used in the study) is that PSX is more volatile to good news than bad news. Moreover EGARCH (1, 1) outperforms all other models of the study on the basis of AIC/BIC criterion. However the comparison of correlations of variances predicted by three asymmetric models reveal that correlations among them are very high, with minimum correlation being 98%. This essentially means that all three asymmetric models provide a good fit to PSX.

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“…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%

Studying the Volatility of Pakistan Stock Exchange and Shanghai Stock Exchange Markets in the Light of CPEC: An Application of GARCH and EGARCH Modelling

Ahsanuddin¹,

Fraz²,

Fatima³

2019

ijSciences

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Globalization in the new millennium has brought a set of profound social, economic and political changes in the world. Pakistan and her neighboring country China have joined hands and has initiated China-Pakistan Economic Corridor (CPEC) which is an ambitious project that focuses on improving connectivity and cooperation among both the neighboring giants. The growing nexus between China and Pakistan through CPEC is going to strengthen their markets as well stock markets. Ultimately both the economies are bound to benefit consequently resulting in increased GDP of both the countries. In this piece of research, we have studied the impact of CPEC on Pakistan Stock Market (PSX) and SSE by employing GARCH and EGARCH model from January 2011 to March 2019 on daily basis which shows recent purchases of up to 45% of Pakistani shares owned by China. Empirical analysis shows that in PSX risk parameter is little bit higher in post-CPEC comparatively pre-CPEC period. Volatility of SSE has high in both pre and post-CPEC period as compared to PSX. It reveals that PSX market after the advent of CPEC is showing stability which is a sign of encouragement for businessmen, traders and investors to invest. Market stability has increased manifold and the findings are supported by Morgan Stanley Capital International (MSCI). Recently Bloomberg has ranked Pakistan amongst the first 5 best performing stocks around the world. The research findings show PSX has a high potential for growth and is expected to become a lucrative market for businessmen, traders and investors.

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Comparing the Performances of GARCH-type Models in Capturing the Stock Market Volatility in Malaysia

Cited by 83 publications

References 8 publications

Comparative Study of Volatility Forecasting Models: The Case of Malaysia, Indonesia, Hong Kong and Japan Stock Markets

Comparative Study of Volatility Forecasting Models: The Case of Malaysia, Indonesia, Hong Kong and Japan Stock Markets

Time Series Analysis of Stock Market Volatility in Pakistan

Studying the Volatility of Pakistan Stock Exchange and Shanghai Stock Exchange Markets in the Light of CPEC: An Application of GARCH and EGARCH Modelling

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