Volatility Analysis of International Tourist Arrival Growth Rates to Thailand using Garch and GJR Model
Abstract
This paper examined volatility of international tourist arrival growth rates to Thailand using monthly time series data for the period 1979-2010. The variable of interest for policy maker was the tourist arrival growth rates at any given month as this figure was directly related to tourism revenue growth rates. In this study we considered the volatility of international tourist arrival growth rates to Thailand by employing GARCH and GJR model. GARCH and GJR model were widely used to manage the risk exposure of financial and tourism risk.Considering the number of tourist arrivals and growth rate of tourist arrivals, it was found that the majority of tourists were from Malaysia and Japan. This study could be used to compare with the USA and the UK for making policy because of the difference in tourism volatility.
From this study the GARCH model generated relatively accurate tourism volatility forecasts except for the Japan and the USA volatility, and the GJR model generated relatively accurate tourism volatility forecasts except for the Malaysia and the UK volatility.
References
[1] Amy, Y.F., Tan, C. McCahon, Miller, J. (2002). Stability of inbound tourism demand model for Indonesia and Malaysia the pre and post-formation of tourism development organizations, Journal of hospitality and tourist research, 3: 361-378.
[2] Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroscedasticity, Journal of Econometrics, 31: 307-327.
[3] Box, G.E.P., Jenkins, G.M. (1976). Time Series Analysis: Forecasting and Control, Rev. Ed., San Francisco: Holden-Day.
[4] Brooks, C. (2004) Introduction Econometrics for Finance, Cambridge University Press, Cambridge.
[5] Chan, F., Lim, C., McAleer, M. (2005). Modeling Multivariate International Tourism Demand and Volatility, Journal of Tourism Management, 26: 301-479.
[6] Domroes, M. (1985). Tourism Resources and their Development in Maldives Islands, GeoJournal, 10: 119-126.
[7] Enders, W. (2004). Applied Econometric Time Series, John Wiley & Son, New Jersey.
[8] Frances, P.H. (1998). Time Series Models for Business and Economics Forecasting, Cambridge University Press, Cambridge.
[9] Glosten, L.R., Jagannathan, R., Runkle, D.E. (1993). On the relation between the expected value and volatility of the nominal excess return on stocks, Journal of Finance, 46: 1779-1801.
[10] Greene, W.H. (2000). Econometric Analysis, Prentice-Hall, Upper Saddle River, New Jersey.
[11] Hoti, S., McAleer, M., Shareef, R. (2005). Modeling country risk and uncertainty in small island tourism economies, Tourism Economics, 11(2): 159-183.
[12] Witt, S.F., Martin, C.A. (1987). Econometrics models for forecasting international tourism demand, Journal of Travel Research, 25: 23-30.
[2] Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroscedasticity, Journal of Econometrics, 31: 307-327.
[3] Box, G.E.P., Jenkins, G.M. (1976). Time Series Analysis: Forecasting and Control, Rev. Ed., San Francisco: Holden-Day.
[4] Brooks, C. (2004) Introduction Econometrics for Finance, Cambridge University Press, Cambridge.
[5] Chan, F., Lim, C., McAleer, M. (2005). Modeling Multivariate International Tourism Demand and Volatility, Journal of Tourism Management, 26: 301-479.
[6] Domroes, M. (1985). Tourism Resources and their Development in Maldives Islands, GeoJournal, 10: 119-126.
[7] Enders, W. (2004). Applied Econometric Time Series, John Wiley & Son, New Jersey.
[8] Frances, P.H. (1998). Time Series Models for Business and Economics Forecasting, Cambridge University Press, Cambridge.
[9] Glosten, L.R., Jagannathan, R., Runkle, D.E. (1993). On the relation between the expected value and volatility of the nominal excess return on stocks, Journal of Finance, 46: 1779-1801.
[10] Greene, W.H. (2000). Econometric Analysis, Prentice-Hall, Upper Saddle River, New Jersey.
[11] Hoti, S., McAleer, M., Shareef, R. (2005). Modeling country risk and uncertainty in small island tourism economies, Tourism Economics, 11(2): 159-183.
[12] Witt, S.F., Martin, C.A. (1987). Econometrics models for forecasting international tourism demand, Journal of Travel Research, 25: 23-30.
Published
2016-11-14
How to Cite
BUNNAG, Tanattrin.
Volatility Analysis of International Tourist Arrival Growth Rates to Thailand using Garch and GJR Model.
Journal of Environmental Management and Tourism, [S.l.], v. 5, n. 1, p. 70-83, nov. 2016.
ISSN 2068-7729.
Available at: <https://journals.aserspublishing.eu/jemt/article/view/400>. Date accessed: 24 apr. 2024.
Section
Journal of Environmental Management and Tourism
Keywords
volatility; GARCH model; GJR model; tourism risk; growth rate of tourist arrivals
Copyright© 2023 The Author(s). Published by ASERS Publishing 2023. This is an open access article distributed under the terms of CC-BY 4.0 license.