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Modelling volatility of Malaysian stock market using garch models

机译:使用garch模型对马来西亚股市的波动性进行建模

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Stock market volatility was changed over time. The factor such as financial crisis can easily influence the movement of the volatility. This unpredictable change means uncertain risks and not well preferred by the most of the stock market players. It is because higher risk can lead to a higher returns or losses. For these reason, this study has modelled volatility to investigate the behavior of stock return volatility of FTSE Bursa Malaysia KLCI with regard to the global financial crisis occurred in 2008 until 2009. The sample consists of 2473 observations of daily index return of FBM KLCI from January 2002 to December 2011. In order to model volatility of Malaysian stock market, three of the family of GARCH models was used. The results of GARCH (1, 1), indicate the presence of volatility clustering and persistence effects on the stock market volatility. Besides, the asymmetric models which are TGARCH and EGARCH detect the presence of leverage effects in the data series. Finally, the last evaluation shows that EGARCH model has outperformed the other class of GARCH model and has the best ability in forecasting the volatility. In conclusion, the results from this study show the ability of GARCH model in modelling volatility and indicate the existence of volatility clustering, leverage effects, and fat tailed in the Malaysian stock returns data.
机译:股市波动随时间而改变。金融危机等因素很容易影响波动率的变化。这种不可预测的变化意味着不确定的风险,因此大多数股票市场参与者都不怎么喜欢它。这是因为较高的风险可能导致较高的收益或损失。基于这些原因,本研究对波动率建模,以调查富时大马隆综合指数在2008年至2009年发生的全球金融危机中的股票收益率波动行为。该样本包括2月份1月以来的FBM KLCI每日指数收益观察2002年至2011年12月。为了建模马来西亚股市的波动性,使用了GARCH模型系列中的三个。 GARCH(1,1)的结果表明,存在波动性聚类和持久性对股市波动性的影响。此外,TGARCH和EGARCH的非对称模型可检测数据序列中杠杆效应的存在。最后,最后的评估表明EGARCH模型优于其他GARCH模型,并且具有最佳的波动率预测能力。总而言之,这项研究的结果表明了GARCH模型在建模波动性方面的能力,并指出了在马来西亚股票收益数据中存在波动性聚类,杠杆效应和脂肪拖尾的情况。

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