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  • 标题:Modeling Stock Market Returns under Self-exciting Threshold Autoregressive Model: Evidence from West Africa
  • 本地全文:下载
  • 作者:Emmanuel Numapau Gyamfi ; Kwabena A. Kyei
  • 期刊名称:International Journal of Economics and Financial Issues
  • 电子版ISSN:2146-4138
  • 出版年度:2016
  • 卷号:6
  • 期号:3
  • 页码:1194-1199
  • 语种:English
  • 出版社:EconJournals
  • 摘要:The study seeks to investigate whether non-linear patterns are present in the returns of two indices on the stock markets in Ghana and Nigeria between the period of 2011 and 2015.The results of applying four linearity tests on the returns concluded that the null of linearity is rejected on all four tests for the Ghanaian index but mixed for the Nigerian index. We modelled the indices under the non-linear self-exciting threshold autoregressive (SETAR) model. We compared the modelling performance of the non-linear SETAR model with that of the standard AR (1) and AR (2) by analyzing AIC values of the respective models. Our results show that the SETAR model fits the data well. Hence, modelling stock market returns from Ghana and Nigeria using linear models might lead to spurious conclusions. Keywords: Threshold models, Linearity tests, Self-Exciting Threshold Autoregressive (SETAR) model JEL Classifications: C12, C13, C24
  • 其他摘要:The study seeks to investigate whether non-linear patterns are present in the returns of two indices on the stock markets in Ghana and Nigeria between the period of 2011 and 2015.The results of applying four linearity tests on the returns concluded that the null of linearity is rejected on all four tests for the Ghanaian index but mixed for the Nigerian index. We modelled the indices under the non-linear self-exciting threshold autoregressive (SETAR) model. We compared the modelling performance of the non-linear SETAR model with that of the standard AR (1) and AR (2) by analyzing AIC values of the respective models. Our results show that the SETAR model fits the data well. Hence, modelling stock market returns from Ghana and Nigeria using linear models might lead to spurious conclusions. Keywords: Threshold models, Linearity tests, Self-Exciting Threshold Autoregressive (SETAR) model JEL Classifications: C12, C13, C24
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