Modelling multivariate international tourism demand and volatility

Chan, Félix; Lim, Christine; McAleer, Michael

doi:10.1016/j.tourman.2004.02.013

Cited by 174 publications

(119 citation statements)

References 11 publications

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“…The GARCH(1,1), GJR(1,1) and EGARCH(1,1) conditional volatility models have been estimated using monthly and daily tourist arrivals data in several papers, including Chan, Lim and McAleer (2005), Shareef (2005, 2007), Shareef and McAleer (2005 , , and McAleer (2009, 2010). However, these papers have not estimated any spillover effects between tourist arrivals and exchange rates using daily and weekly data, and have not examined world price or price volatility effects.…”

Section: Conditional Mean and Conditional Volatility Modelsmentioning

confidence: 99%

Aggregation, Heterogeneous Autoregression and Volatility of Daily International Tourist Arrivals and Exchange Rates

Chang

McAleer

2010

SSRN Journal

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Tourism is a major source of service receipts for many countries, including Taiwan. The two leading tourism countries for Taiwan are Japan and USA, which are sources of short and long haul tourism, respectively. As a strong domestic currency can have adverse effects on international tourist arrivals through the price effect, daily data from 1 January 1990 to 31 December 2008 are used to model the world price, exchange rates, and tourist arrivals from the world, USA and Japan to Taiwan, and their associated volatility. Inclusion of the exchange rate and its volatility captures approximate daily and weekly price and price volatility effects on world, US and Japanese tourist arrivals to Taiwan. The Heterogeneous Autoregressive (HAR) model is used to approximate the slowly decaying correlations associated with the long memory properties in daily and weekly exchange rates and international tourist arrivals, to test whether alternative short and long run estimates of conditional volatility are sensitive to the long memory in the conditional mean, to examine asymmetry and leverage in volatility, and to examine the effects of temporal and spatial aggregation. The approximate price and price volatility effects tend to be different, with the exchange rate typically having the expected negative impact on tourist arrivals to Taiwan, whereas exchange rate volatility can have positive or negative effects on tourist arrivals to Taiwan. For policy purposes, the empirical results suggest that an arbitrary choice of data frequency or spatial aggregation will not lead to robust findings as they are generally not independent of the level of aggregation used.

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Section: Conditional Mean and Conditional Volatility Modelsmentioning

confidence: 99%

Aggregation, Heterogeneous Autoregression and Volatility of Daily International Tourist Arrivals and Exchange Rates

Chang

McAleer

2010

SSRN Journal

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“…GARCH models have been widely used in the financial modelling context to investigate the volatility of the time series. Chan et al (2005) applied three multivariate GARCH models to examine the volatility of tourism demand and the effects of various shocks in the tourism demand models. They found that tourism demand was affected by the conditional variances of the models that underline the demand for Australian tourism by the four leading tourism source markets.…”

Section: Time Series Modelsmentioning

confidence: 99%

Tourism Demand Modelling and Forecasting

Song

2012

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This paper reviews the published studies on tourism demand modelling and forecasting since 2000. One of the key findings of this review is that the methods used in analysing and forecasting the demand for tourism have been more diverse than those identified by other review articles. In addition to the most popular time series and econometric models, a number of new techniques have emerged in the literature. However, as far as the forecasting accuracy is concerned, the study shows that there is no single model that consistently outperforms other models in all situations. Furthermore, this study identifies some new research directions, which include improving the forecasting accuracy through forecast combination; integrating both qualitative and quantitative forecasting approaches, tourism cycles and seasonality analysis, events' impact assessment and risk forecasting.

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“…However, Gustavsson and Nordstrom (2001) found opposite results to Goh and Law (2002)'s studies. Applying ARCH and GARCH model Chan et al (2005) tries to estimate and forecast volatility in tourism demand and its affect to various shocks. While Li et al (2005) and Lim…”

Section: Introductionmentioning

confidence: 99%

Forecasting International Tourists Footfalls in India: An Assortment of Competing Models

Padhan¹

2011

IJBM

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The paper deals with forecasting international tourists footfalls in India, applying an assortment of uni-variate time series forecasting models, for monthly data spreading over Dec 1990 to Jan 2010. The forecasting performance of various competing models is evaluated with MAPE and other criteria. Also the actual and forecasted values are compared since Feb 2010 to Sept 2010 for better evaluation. The SARIMA model performs better than other competing model for forecasting, with lowest MAPE value. In fact it has an advantage over other models as it explicates autoregressive and moving average process not only for the data series but also of seasonality. As a policy implication, SARIMA model can be used for forecasting tourists demand in India.

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Modelling multivariate international tourism demand and volatility

Cited by 174 publications

References 11 publications

Aggregation, Heterogeneous Autoregression and Volatility of Daily International Tourist Arrivals and Exchange Rates

Aggregation, Heterogeneous Autoregression and Volatility of Daily International Tourist Arrivals and Exchange Rates

Tourism Demand Modelling and Forecasting

Forecasting International Tourists Footfalls in India: An Assortment of Competing Models

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