文章基本信息

标题：Forecasting performance of smooth transition autoregressive (STAR) model on travel and leisure stock index
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作者：Usman M. Umer ; Tuba Sevil ; Güven Sevil 等
期刊名称：The Journal of Finance and Data Science
印刷版ISSN：2405-9188
出版年度：2019
卷号：5
期号：1
页码：12-21
DOI：10.1016/j.jfds.2018.02.004
语种：English
出版社：Elsevier
摘要：AbstractTravel and leisure market records a consecutive robust growth and become among the fastest economic sectors. Numerous studies proposed distinct forecasting models to predict the dynamics of this sector and provide early recommendations for investors and policy makers. In this paper, we compare the forecasting performance of smooth transition autoregressive (STAR) and linear autoregressive (AR) models using the monthly returns of Turkey and FTSE travel and leisure indices from April 1997 to August 2016. MSCI world index used as a proxy of the overall market. The result shows that nonlinear LSTAR model cannot improve the out-of-sample forecast of linear AR model. This finding demonstrates little to be gained from using LSTAR model in the prediction of travel and leisure stock index.
关键词：Nonlinear time-series;Out-of-sample forecasting;Smooth transition autoregressive;Travel and leisure