{"title":"利用garch模型对马来西亚股市波动率进行建模","authors":"Nor Alwani Binti Omar, F. A. Halim","doi":"10.1109/ISMSC.2015.7594096","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":407600,"journal":{"name":"2015 International Symposium on Mathematical Sciences and Computing Research (iSMSC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Modelling volatility of Malaysian stock market using garch models\",\"authors\":\"Nor Alwani Binti Omar, F. A. Halim\",\"doi\":\"10.1109/ISMSC.2015.7594096\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":407600,\"journal\":{\"name\":\"2015 International Symposium on Mathematical Sciences and Computing Research (iSMSC)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-05-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 International Symposium on Mathematical Sciences and Computing Research (iSMSC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISMSC.2015.7594096\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 International Symposium on Mathematical Sciences and Computing Research (iSMSC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISMSC.2015.7594096","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
摘要
随着时间的推移,股市的波动性发生了变化。金融危机等因素很容易影响波动率的走势。这种不可预测的变化意味着不确定的风险,并不是大多数股市参与者所喜欢的。这是因为更高的风险可能导致更高的回报或损失。基于这些原因,本研究对波动率进行了建模,以调查FTSE Bursa Malaysia KLCI在2008年至2009年全球金融危机期间股票收益波动率的行为。样本由2002年1月至2011年12月FBM KLCI指数日收益率的2473个观测值组成。为了模拟马来西亚股票市场的波动率,我们使用了GARCH模型家族中的三个模型。GARCH(1,1)的结果表明,股票市场波动存在波动聚类效应和持续效应。此外,非对称模型TGARCH和EGARCH检测了数据序列中杠杆效应的存在。最后,最后的评价表明,EGARCH模型优于GARCH模型的其他类别,具有最好的预测波动率的能力。综上所述,本研究结果表明GARCH模型对波动率进行建模的能力,并表明马来西亚股票收益数据中存在波动率聚类、杠杆效应和肥尾效应。
Modelling volatility of Malaysian stock market using garch models
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.