The Fondazione Eni Enrico Mattei series on Economics, the Environment and Sustainable Development
Edited by Alessandro Lanza, Anil Markandya and Francesco Pigliaru
Chapter 2: Forecasting international tourism demand and uncertainty for Barbados, Cyprus and Fiji
Felix Chan, Suhejla Hoti, Michael McAleer and Riaz Shareef 1. INTRODUCTION Volatility in monthly international tourist arrivals is the squared deviation from the mean monthly international tourist arrivals, and is widely used as a measure of risk or uncertainty. Monthly international tourist arrivals to each of the three Small Island Tourism Economies (SITEs) analysed in this chapter, namely Barbados, Cyprus and Fiji, exhibit distinct patterns and positive trends. However, monthly international tourist arrivals for some SITEs have increased rapidly for extended periods, and stabilized thereafter. Most importantly, there have been increasing variations in monthly international tourist arrivals in SITEs for extended periods, with subsequently dampened variations. Such ﬂuctuating variations in monthly international tourist arrivals, which vary over time, are regarded as the conditional volatility in tourist arrivals, and can be modelled using ﬁnancial econometric time series techniques. Fluctuating variations, or conditional volatility, in international monthly tourist arrivals are typically associated with unanticipated events. There are time-varying eﬀects related to SITEs, such as natural disasters, ethnic conﬂicts, crime, the threat of terrorism, and business cycles in tourist source countries, among many others, which can cause variations in monthly international tourist arrivals. Owing to the nature of these events, recovery from variations in tourist arrivals from unanticipated events may take longer for some countries than for others. These time-varying eﬀects may not necessarily exist within SITEs, and hence may be intrinsic to the tourist source countries. In this chapter, we show how the generalized autoregressive conditional heteroscedasticity (GARCH) model...
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