The Economics of Small Island Tourism

The Economics of Small Island Tourism

International Demand and Country Risk Analysis

The Fondazione Eni Enrico Mattei series on Economics, the Environment and Sustainable Development

Riaz Shareef, Suheija Hoti and Michael McAleer

This study forms an entirely new area of research on Small Island Tourism Economies (SITEs). It addresses the importance of uncertainty in monthly international tourist arrivals and country risk indicators to the macroeconomy.


Matteo Manera

Subjects: development studies, tourism, economics and finance, environmental economics, environment, environmental economics, tourism, geography, tourism


Matteo Manera This monograph analyses the conditional volatility in two data series, specifically the monthly international tourist arrivals to seven Small Island Tourism Economies (SITEs), namely Barbados, Cyprus, Dominica, Fiji, Maldives, Malta and Seychelles, and the monthly risk returns for six separate SITEs, namely the Bahamas, Cyprus, Dominican Republic, Haiti, Jamaica, and Malta. These two series exhibit distinct seasonal patterns and positive trends. Moreover, the conditional volatilities have increased rapidly for extended periods, and stabilised thereafter. Most importantly, there have been increasing variations in monthly international tourist arrivals and country risk returns in SITEs for extended periods, with subsequently dampened variations. Such fluctuating variations over time are interpreted as the conditional volatility in tourist arrivals and risk returns, respectively, and can be modelled using modern financial econometric time series techniques. The authors have identified several reasons why it is crucial to model and forecast the uncertainty or volatility in international tourist arrivals. First, governments as well as tour operators need to examine the underlying uncertainty that is intrinsic to the total numbers, as well as in the growth rate, of monthly international tourist arrivals, and country risk ratings and risk returns. Second, in the literature it is widely believed that the forecast confidence intervals are time varying. Therefore, more accurate confidence intervals can be obtained by modelling the conditional variance of the errors. Finally, if the heteroscedasticity in the errors is examined carefully and is dealt with accurately, more efficient estimators for the parameters in the conditional mean can be...