Forecasting and modelling for the inbound tourism demand volatility

Many past studies conducted have explored and focused primarily on forecasting and predicting tourism demand. However, this research aims to concentrate on the aspect of how tourism demand volatility can be forecasted and accounted for, rather than solely on how we can forecast and anticipate tourism demand itself. Seven source countries for monthly tourist arrivals in Malaysia will be considered and focused as a case study in this paper. Furthermore, the model theory of generalised autoregressive conditional heteroscedastic (GARCH) family volatility are explored in its relevance and introduced in this study. Additionally, the performance and evaluation of a new adaptive forecasting method, smooth transition exponential smoothing (STES), will be compared to GARCH-type models and several ad hoc methods for Malaysia's tourism demand volatility forecasting and modelling. Notably, our research conducted reported findings that monthly seasonality impacts existed in the mean equation, and the GARCH family models revealed that news shock continuously impact on Malaysia's tourism demand volatility. The EGARCH and GJR models utilised in this study also exhibited the asymmetric and leverage effects. Interestingly, it was also observed that the STES model surpassed GARCH family models and several ad hoc methods in forecasting and modelling.