文章基本信息

标题：Comparison of forecasting performance between MODWT-GARCH(1,1) and MODWT-EGARCH(1,1) models: Evidence from African stock markets
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作者：Mohd Tahir Ismail ; Mohd Tahir Ismail ; Buba Audu 等
期刊名称：The Journal of Finance and Data Science
印刷版ISSN：2405-9188
出版年度：2016
卷号：2
期号：4
页码：254-264
DOI：10.1016/j.jfds.2017.03.001
语种：English
出版社：Elsevier
摘要：Abstract Many researchers documented that if stock markets' returns series are significantly skewed, linear-GARCH(1,1) grossly underestimates the forecast values of the returns. However, this study showed that the linear Maximal Overlap Discreet Wavelet Transform MODWT-GARCH(1,1) actually gives an accurate forecast value of the returns. The study used the daily returns of four African countries' stock market indices for the period January 2, 2000, to December 31, 2014. The Maximal Overlap Discreet Wavelet Transform-GARCH(1,1) model and the Maximal Overlap Discreet Wavelet Transform-EGARCH(1,1) model are exhaustively compared. The results show that although both models fit the returns data well, the forecast produced by the Maximal Overlap Discreet Wavelet Transform-EGARCH(1,1) model actually underestimates the observed returns whereas the Maximal Overlap Discreet Wavelet Transform-GARCH(1,1) model generates an accurate forecast value of the observed returns.
关键词：Volatility;Asset returns;MODWT;GARCH;EGARCH