International Tourist Arrivals Volatility Comovements and Spillovers. The Case of Thailand
Abstract
This paper examined the international tourist arrivals volatility comovements and spillovers for Malaysian (GML), Japanese (GJP), British (GUK) and American (GUS) tourists. The data used in this study was the monthly data from 1985 to 2015. The three Multivariate GARCH models, namely the VAR (5)-diagonal VECH and the VAR (5)-diagonal BEKK, were employed.The empirical results overall showed that the estimates of the VAR (5)-diagonal VECH parameters are statistically significant in the case of GML with GUS and GJP with GUS except in the case of GML with GJP, GML with GUK, GJP with GUK and GUK with GUS. This indicates that the short run persistence of shocks on the dynamic conditional correlations is greatest for GML with GJP, while the largest long run persistence of shocks to the conditional correlations for GML with GUS. In addition, the estimates of the VAR (5)-diagonal BEKK parameters are statistically in the case of GML with GUK, GML with GUS and GUK with GUS except in the case of GML with GJP, GJP with GUK and GJP with GUS. This indicates that the short run persistence of shocks on the dynamic conditional correlations is greatest for GJP with GUK, while the largest long run persistence of shocks to the conditional correlations for GML with GUK.
Finally, we found that the best model in volatility analysis is the VAR (5) - diagonal BEKK model. Therefore, we can say Japanese tourists with British tourists are the most susceptible for the short run. But for the long run, Malaysian tourists with British tourists are the most susceptible.
References
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[4] Bunnag, T. (2014). Analysis of the relationship between the variables in Japanese Tourists demand using VECM and cointegration: The case of Thailand, Journal of Environment Management and Tourism, 5(2): 114-126. doi:10.14505/jemt.v5.2(10).02
[5] Bunnag, T. (2014).The real exchange rate volatility comovements and spillovers in Thailand’s international trade: A multivariate GARCH approach, Journal of Applied Economic Sciences, 9(4): 614-631.
[6] Bunnag, T. (2015).Hedging petroleum futures with multivariate GARCH models, International Journal of Energy Economics and Policy, 5(1): 105-120.
[7] Calvet, L., Fisher, A.J. and Thompson, S.B. (2006). Volatility Comovements. A multifrequency Approach, Journal of Econometrics, 131: 179-215.
[8] Coşkun, O., Özer, M. (2011). MGARCH Modeling of Inbound Tourism Demand Volatility in Turkey, MIBES Transaction, 5(1): 24-48.
[9] Enders, W. (2004). Applied Econometric Time Series, John Wiley & Son.
[10] Engle, R.F. (2002). Dynamic Conditional Correlation: A Simple Class of Multivariate GARCH Models. Journal of Business and Economic Statistics, 37: 827-840.
[11] Harasarn, A., Chancharut, S. (2014). Evolutional-genetic approach to formation of sustainable international tourism and economic growth in Thailand. Cointegration and the Granger causality, Journal of Environmental Management and Tourism, 5(2): 237-248.
[12] Hoti, S., Leon, C.J., McAleer, M. (2005). Modeling the uncertainty in international tourist arrivals to the Canary Island, Working Paper, University of the Western University.
[13] Hoti, S., McAleer, M., Shareef, R. (2007). Modeling volatility spillovers and causality in international tourism and country risk for Cyprus and Malta, Tourism Management, 28: 1472-1484.
[14] Kroner, K.F., Ng, V.K. (1998). Modeling asymmetric comovement of asset returns, Review of Financial Studies, 11, 122-150.
[15] Malliga, S. (2014). The income and price elasticities of tourist demand in Thailand, Journal of Environmental Management and Tourism, 5(1): 14-28.
[16] Seo, J.W., Park, S.Y., Yu, L. (2009). The analysis of the relationships of Korean outbound tourism demand: The Jeju Island and three international destinations, Tourism Management, 30(4): 530-542.
[17] Shareef, R., McAleer, M. (2008). Modelling international tourism demand and uncertainty in Maldives and Seychelles: A portfolio approach, Mathematics and Computers in Simulation, 78(2): 459-468.
[18] Song, H., Witt, S.F. (2003). Tourism forecasting: the general-to-specific approach, Journal of Travel Research, 42: 65-74.
[19] Tse, Y. (2000). A Test for Constant Correlations in a Multivariate GARCH Model, Journal of Econometrics, 98: 107-127.
Published
2016-11-10
How to Cite
BUNNAG, Tanattrin.
International Tourist Arrivals Volatility Comovements and Spillovers. The Case of Thailand.
Journal of Environmental Management and Tourism, [S.l.], v. 6, n. 1, p. 5-21, nov. 2016.
ISSN 2068-7729.
Available at: <https://journals.aserspublishing.eu/jemt/article/view/368>. Date accessed: 26 apr. 2024.
Section
Journal of Environmental Management and Tourism
Keywords
the international tourist arrivals volatility; comovements and spillovers; multivariate GARCH models
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.