Modelling tourism demand volatility using a seasonal autoregressive integrated moving average autoregressive conditional heteroscedasticity model for Victoria Falls Rainforest arrivals in Zimbabwe

A new model predicts stable tourism volatility at Victoria Falls Rainforest, offering optimism for Zimbabwe's tourism sector. The SARIMA(2,1,0)(2,0,0)12-ARCH(1) model, applied to monthly tourist arrival data from 2008–2017, reveals minimal uncertainty in the first half of forecasted years, followed by consistent volatility. This suggests that favorable policies and marketing strategies could sustain Victoria Falls' appeal, potentially boosting employment and economic growth under Zimbabwe's new political dispensation. Previous studies had focused on tourism determinants like political instability and exchange rates, but this research advances understanding by modeling volatility dynamics—key for destination managers and policymakers.

Makoni, T., & Chikobvu, D. (2018). Modelling tourism demand volatility using a seasonal autoregressive integrated moving average autoregressive conditional heteroscedasticity model for Victoria Falls Rainforest arrivals in Zimbabwe. Journal of Economic and Financial Sciences, 11(1). https://doi.org/10.4102/jef.v11i1.167