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

Abstract

Accurate tourism volatility forecasts for popular tourist destinations, like the Victoria Falls Rainforest, are vital to tourism destination managers and policymakers. The Victoria Falls Rainforest in Zimbabwe is under the town of Victoria Falls and is one of the natural wonders of the world. The rainforest has many exceptional plant species not common in the region and hence attracts many tourists. Financial, political and economic environments differently affect the Zimbabwean tourism industry, as evidenced by large tourist arrival fluctuations. Previous research focused more on tourism determinants than tourism volatilities. Researchers noted political instability and exchange rates as the major Zimbabwean tourism determinants. Estimates of the Victoria Falls Rainforest tourist arrival volatilities are projected using the monthly tourist arrival figures from the Zimbabwe Parks and Wildlife Management Authority and Zimbabwe Tourism Authority. The first difference of logarithmic transformed series is stationary. The univariate SARIMA(2,1,0)(2,0,0)12-ARCH(1) model fits extremely well and provides an informative out-of-sample volatility forecast because it captures tourism volatility effects, dynamics and non-linearity of conditional variances. The results indicate that positive tourism shocks affect tourist arrival volatility positively. Volatility estimates indicated minimal uncertainty in the first half of the forecasted year and then became constant throughout the year. This encourages the continuation of the implementation of new favourable policies and marketing strategies by the government and tourism destination managers to keep the destination distinctive and attractive. The New Zimbabwe political dispensation is likely to enhance investment opportunities at the Victoria Falls Rainforest as a destination because of minimal uncertainties exhibited by volatility forecasts. Potential employment creation, improved economic environment and other positives are some of the expectations from the model results.