Statistical modelling of Zimbabwe’s international tourist arrivals using both symmetric and asymmetric volatility models

Abstract

Orientation: Modelling of international tourist arrivals’ volatility is vital for marketing, planning, policy formulation and investment purposes among others. Symmetric and asymmetric tourism volatility models for years 2000–2017 were fitted to assess the impact of good and bad news on Zimbabwe’s international tourist arrivals’ volatility.

Research purpose: In this article, we model Zimbabwe’s monthly international tourist arrivals’ volatility using both symmetric and asymmetric volatility models and investigate the impact of good and bad news on international tourist arrivals’ volatility.

Motivation for the study: Very few, if any, research papers have looked at volatility clustering and the impact of good and bad news on Zimbabwe’s international tourist arrivals’ volatility. These are important aspects for tourism managers, investors and the government in decision-making processes.

Research design, approach and method: The ARMA-GARCH and ARMA-EGARCH models were used to model tourism volatility.

Main findings: The ARMA(1,1)-GARCH(1,1) model under the t-distribution (STD) innovations shows unexpected tourism shocks having a strong impact, that persists for significant periods of time, on Zimbabwe’s tourist arrivals. The ARMA(1,1)-EGARCH(1,1) model under STD innovations indicated less impact of bad news on future tourism volatility than good news of the same size because the symmetry coefficient gamma (γ) is statistically significant and is above zero.

Practical/managerial implications: Tourism volatility models unearth the impact of news on international tourist arrivals’ volatility. Good news attracts tourism investors, while bad news discourages tourism investors from investing. Tourism stakeholders, government and investors can use these informative volatility models to the success of the tourism industry in Zimbabwe.

Contribution/value-add: This article highlights the importance of volatility models in the tourism industry.