International tourism demand and exchange rate
dependence modeling based on copula-GARCH model
DOI:
https://doi.org/10.7784/rbtur.v16.2263Keywords:
Tourism, Copulas, Exchange Rates.Abstract
The exchange rate can be a determining factor in tourist demand and it can change the tourism trade's competitiveness. This study aims to measure the dependence between international tourist demand and the exchange rate in Brazil. Empirical investigations of this relation, using the copula-GARCH model, are relatively recent in the world literature. The application is carried out with monthly data on exchange rates and international arrivals from Argentina, the United States, and Germany, between 1999 and 2018. The results indicate that the exchange rate variation is not directly associated with the number of tourist arrivals from Germany and the United States. However, for Argentina, the correlation measure was negative and statistically significant, indicating a weak association between the variables. This indicates that when the local currency depreciates against the Brazilian currency, the number of arrivals decreases. This study's conclusions can help managers of tourist organizations understand the relationship between foreign exchange and international tourist demand in Brazil.
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References
Akaike, H. (1974). A New Look At The Statistical Model Identification. IEEE transactions on automatic control, 19, 716–723. https://doi.org/10.1109/TAC.1974.1100705
Camara, I. L. P. (2019). Análise econométrica dos determinantes econômicos da demanda turística internacional para o estado do Rio de janeiro. Dissertação de Mestrado, Universidade Federal Fluminense, Niterói, RJ, Brasil.
Camara, I. L. P. da, Monteiro, J. E. D., & Santos, G. E. de O. (2021). Fatores determinantes da demanda turística inter-nacional para o Rio de Janeiro: evidências baseadas em modelos de regressão linear. Revista Turismo Em Análise, 32(1), 100-119. https://doi.org/10.11606/issn.1984-4867.v32i1p100-119
Castro, R. M. d., & Giraldi, J. d. M. E. (2012). Processo de desenvolvimento e gestão de marca-país: um estudo sobre a marca Brasil. Turismo-Visão e Ação, 14, 164–183. https://doi.org/10.14210/rtva.v14n2.p164-183
Coelho, M. F. (2015) O que Atrai o Turista? Gestão da Competitividade de Destinos a Partir de Atrações e da Atrativi-dade Turística. Revista Rosa dos Ventos, 7(4), 489-505.
Chang, K.-L., & Chang, J.-C. D. (2020). Dynamic dependence between us inbound visits and exchange rate. Journal of Hospitality & Tourism Research, 1096348020913084. https://doi.org/10.1177%2F1096348020913084
Chasapopoulos, P., Den Butter, F. A., & Mihaylov, E. (2014). Demand for tourism in Greece: a panel data analysis using the gravity model. International Journal of Tourism Policy, 5(3), 173-191. https://doi.org/10.1504/ijtp.2014.063105
Cheng, K. M., Kim, H., & Thompson, H. (2013). The real exchange rate and the balance of trade in us tourism. Interna-tional Review of Economics & Finance, 25, 122–128. http://dx.doi.org/10.1016/j.iref.2012.06.007
Clayton, D. G. (1978). A model for association in bivariate life tables and its application in epidemiological studies of familial tendency in chronic disease incidence. Biometrika, 65, 141–151. https://doi.org/10.1093/biomet/65.1.141
Conover, W. J. (1971). Practical nonparametric statistics. John Wiley & Sons.
Conselho Mundial de Viagens e Turismo (2019). The Importance of Travel & Tourism in 2018. WTTC.
Croes, R. R., & Vanegas Sr, M. (2005). An econometric study of tourist arrivals in Aruba and its implications. Tourism Management, 26, 879–890. http://dx.doi.org/10.1016/j.tourman.2004.04.007
Crouch, G. I. (1994a). The study of international tourism demand: A review of findings. Journal of Travel research, 33, 12–23. https://doi.org/10.1177%2F004728759403300102
Crouch, G. I. (1994b). The study of international tourism demand: A survey of practice. Journal of Travel research, 32, 41–55. https://doi.org/10.1177%2F004728759403200408
De Vita, G. (2014). The long-run impact of exchange rate regimes on international tourism flows. Tourism Manage-ment, 45, 226–233. https://doi.org/10.1016/j.tourman.2014.05.001
Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Jour-nal of the American statistical association, 74, 427–431. https://doi.org/10.2307/2286348
Dogru, T., Sirakaya-Turk, E., & Crouch, G. I. (2017). Remodeling international tourism demand: Old theory and new evidence. Tourism management, 60, 47–55. https://doi.org/10.1016/j.tourman.2016.11.010
Dritsakis, N. (2004). Cointegration analysis of German and British tourism demand for Greece. Tourism management, 25, 111–119. https://doi.org/10.1016/S0261-5177(03)00061-x
Dwyer, L., Forsyth, P., & Rao, P. (2002). Destination price competitiveness: Exchange rate changes versus domestic infla-tion. Journal of Travel Research, 40, 328–336. http://dx.doi.org/10.1177/0047287502040003010
Enders, W. (2008). Applied econometric time series. John Wiley & Sons.
Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 987–1007. https://doi.org/10.2307/1912773
Enright, M. J., & Newton, J. (2004). Tourism destination competitiveness: a quantitative approach. Tourism manage-ment, 25, 777–788. https://doi.org/10.1016/j.tourman.2004.06.008
Fernández, C., & Steel, M. F. (1998). On bayesian modeling of fat tails and skewness. Journal of the American Statis-tical Association, 93, 359–371. https://doi.org/10.1080/01621459.1998.10474117
Ferrari, S., & Guala, C. (2017). Mega-events and their legacy: Image and tourism in Genoa, Turin and Milan. Leisure Stu-dies, 36, 119–137. https://doi.org/10.1080/02614367.2015.1037788
Fórum Econômico Mundial (2019). Travel & Tourism Competitiveness Index. Edição de 2019.
Gani, A., & Clemes, M. D. (2017). The main determinants effecting international visitor arrivals in New Zealand: Some empirical evidence. Tourism Economics, 23, 921–940. https://doi.org/10.1177%2F1354816616656417
Genest, C., & Favre, A.-C. (2007). Everything you always wanted to know about copula modeling but were afraid to ask. Journal of hydrologic engineering, 12, 347–368. https://doi.org/10.1061/(ASCE)1084-0699(2007)12:4(347)
Genest, C., Rémillard, B., & Beaudoin, D. (2009). Goodness-of-fit tests for copulas: A review and a power study. Insur-ance: Mathematics and economics, 44, 199–213. https://doi.org/10.1016/j.insmatheco.2007.10.005
Gomes, M. S. (2011). O marketing turístico e o reposicionamento da imagem do brasil no mundo: uma análise do plano aquarela da embratur. Tourism & Management Studies, 579–588.
Gumbel, E. J. (1960). Bivariate exponential distributions. Journal of the American Statistical Association, 55, 698–707. https://doi.org/10.2307/2281591
Huang, W., & Prokhorov, A. (2014). A goodness-of-fit test for copulas. Econometric Reviews, 33, 751–771. https://doi.org/10.1080/07474938.2012.690692
IBGE (2012). Economia do Turismo: Uma perspectiva Macroeconômica 2003-2009. Estudos e Pesquisas Informa-ção Econômica, 18. Rio de Janeiro: IBGE.
Jarque, C. M., & Bera, A. K. (1987). A test for normality of observations and regression residuals. International Statis-tical Review/Revue Internationale de Statistique, 163–172. https://doi.org/10.2307/1403192
Joe, H., & Xu, J. J. (1996). The estimation method of inference functions for margins for multivariate models. Tech-nical report No. 166 University of British Columbia. https://dx.doi.org/10.14288/1.0225985
Jondeau, E., & Rockinger, M. (2006). The copula-garch model of conditional dependencies: An international stock mar-ket application. Journal of international money and finance, 25, 827–853. https://doi.org/10.1016/j.jimonfin.2006.04.007
Just, M., & Łuczak, A. (2020). Assessment of conditional dependence structures in commodity futures markets using copula-garch models and fuzzy clustering methods. Sustainability, 12, 2571. https://doi.org/10.3390/su12062571
Khoshnevis Yazdi, S., & Khanalizadeh, B. (2017). Tourism demand: A panel data approach. Current Issues in Tour-ism, 20, 787–800. https://doi.org/10.1080/13683500.2016.1170772
Kumar, N., Kumar, R. R., Patel, A., Hussain Shahzad, S. J., & Stauvermann, P. J. (2020). Modelling inbound interna-tional tourism demand in small pacific island countries. Applied Economics, 52, 1031–1047. https://doi.org/10.1080/00036846.2019.1646887
Kwiatkowski, D., Phillips, P. C., Schmidt, P., Shin, Y. et al. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of econometrics, 54, 159–178. https://doi.org/10.1016/0304-4076(92)90104-y
Lambert, P., & Laurent, S. (2001). Modelling financial time series using GARCH-type models with a skewed student dis-tribution for the innovations. Technical report UCL.
Li, G., Song, H., & Witt, S. F. (2005). Recent developments in econometric modeling and forecasting. Journal of Travel Research, 44, 82–99. https://doi.org/10.1177%2F0047287505276594
Lim, C. (1997). Review of international tourism demand models. Annals of tourism research, 24, 835–849. https://doi.org/10.1016/S0160-7383(97)00049-2
Liu, J., & Sriboonchitta, S. (2013). Analysis of volatility and dependence between the tourist arrivals from China to Thailand and Singapore: A copula-based garch approach. In Uncertainty analysis in econometrics with appli-cations, 283–294.
Liu, J., Sriboonchitta, S., Nguyen, H. T., & Kreinovich, V. (2014). Studying volatility and dependency of Chinese out-bound tourism demand in Singapore, Malaysia, and Thailand: A vine copula approach. In Modeling dependence in econometrics, 259–274. http://dx.doi.org/10.1007/978-3-319-03395-2_17
Ljung, G. M., & Box, G. E. (1978). On a measure of lack of fit in time series models. Biometrika, 65, 297–303. https://doi.org/10.2307/2335207
Lohmann, G. et al. (2022). O Futuro do turismo no Brasil a partir da análise crítica do período 2000-2019. RBTUR, 16, 1-20. https://doi.org/10.7784/rbtur.v16.2456
Lorde, T., Li, G., & Airey, D. (2016). Modeling Caribbean tourism demand: an augmented gravity approach. Journal of Travel Research, 55, 946–956. https://doi.org/10.1177%2F0047287515592852
Martins, L. F., Gan, Y., & Ferreira-Lopes, A. (2017). An empirical analysis of the influence of macroeconomic determi-nants on world tourism demand. Tourism Management, 61, 248–260. https://doi.org/10.1016/j.tourman.2017.01.008
Meurer, R. (2010). Research note: International travel: The relationship between exchange rate, world gdp, revenues and the number of travellers to Brazil. Tourism Economics, 16, 1065–1072. http://dx.doi.org/10.5367/te.2010.0011
Meurer, R., & Lins, H. N. (2018). The effects of the 2014 World Cup and the 2016 Olympic Games on Brazilian inter-national travel receipts. Tourism economics, 24, 486–491. https://doi.org/10.1177%2F1354816617746261
Ministério do Turismo (2019a). Anuário Estatístico de Turismo (46th ed.). Ano base 2018.
Ministério do Turismo (2019b). Estudo da Demanda Turística Internacional. Ano base 2018.
Mokni, K., & Mansouri, F. (2017). Conditional dependence between international stock markets: A long memory garch-copula model approach. Journal of Multinational Financial Management, 42, 116–131. https://doi.org/10.1016/j.mulfin.2017.10.006
Nagler, T., Schepsmeier, U., Stoeber, J., Brechmann, E. C., Graeler, B., & Erhardt, T. (2019). VineCopula: Statistical Infer-ence of Vine Copulas. https://cran.r-project.org/web/packages/VineCopula/VineCopula.pdf
Nelsen, R. B. (2006). An introduction to copulas. Springer Science & Business Media. https://doi.org/10.1007/0-387-28678-0
Nishio, T. (2013). The impact of sports events on inbound tourism in New Zealand. Asia Pacific journal of tourism research, 18, 934–946. http://dx.doi.org/10.1080/10941665.2012.718718
Organização Mundial do Turismo (2020). World Tourism Barometer (18th ed.). Issue 1.
Pérez-Rodríguez, J. V., Ledesma-Rodríguez, F., & Santana-Gallego, M. (2015). Testing dependence between gdp and tourism’s growth rates. Tourism Management, 48, 268–282. http://dx.doi.org/10.1016/j.tourman.2014.11.007
Phillips, P. C., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75, 335–346. https://doi.org/10.2307/2336182
Puarattanaarunkorn, O., & Sriboonchitta, S. (2014). Copula based garch dependence model of Chinese and Korean tourist arrivals to Thailand: implications for risk management. In Modeling dependence in econometrics, 343–365. https://doi.org/10.1007/978-3-319-03395-2_22
Quadri, D. L., & Zheng, T. (2010). A revisit to the impact of exchange rates on tourism demand: The case of Italy. The Journal of Hospitality Financial Management, 18, 47–60. https://doi.org/10.1080/10913211.2010.10653894
Quayson, J., & Var, T. (1982). A tourism demand function for the Okanagan, bc. Tourism Management, 3, 108–115. https://doi.org/10.1016/0261-5177(82)90006-1
Rabahy, W. A. (1990). Planejamento do Turismo: estudos econômicos e fundamentos econométricos. Edições Layo-la.
Rabahy, W. A., da Silva, J. C. D., & Vassallo, M. D. (2008). Relações determinantes sobre as despesas e asreceitas da conta de viagens internacionais do balanço de pagamentos brasileiro. Revista Turismo em Análise,19,293–306. https://doi.org/10.11606/issn.1984-4867.v19i2p293-306
Rabahy, W. A. (2019). Análise e perspectivas do turismo no Brasil. Revista Brasileira de Pesquisa em Turismo, 14, 1–13. https://doi.org/10.7784/rbtur.v14i1.1903
Santana, G. (2000). An overview of contemporary tourism development in Brazil. International Journal of Contempo-rary Hospitality Management. http://dx.doi.org/10.1108/09596110010347310
Santos, G. E. d. O. (2013). O que determina a satisfação dos turistas internacionais no Brasil? Revista Turismo em Análise, 24, 521–543. https://doi.org/10.11606/issn.1984-4867.v24i3p521-543
Seetanah, B., Durbarry, R., & Ragodoo, J. N. (2010). Using the panel cointegration approach to analyse the determi-nants of tourism demand in South Africa. Tourism Economics, 16, 715–729. https://doi.org/10.5367%2F000000010792278437
Seo, J. H., Park, S. Y., & Yu, L. (2009). The analysis of the relationships of Korean outbound tourism demand: Jeju Island and three international destinations. Tourism Management, 30, 530–543. https://doi.org/10.1016/j.tourman.2008.10.013
Shih, J. H., & Louis, T. A. (1995). Inferences on the association parameter in copula models for bivariate survival data. Biometrics, 1384–1399. https://doi.org/10.2307/2533269
Sklar, A. (1959). Fonctions de répartition à n dimensions et leurs marges. Publications de l’Institut de Statistique de l’Université de Paris, 8, 229–231.
Sobral, F., Peci, A., & Souza, G. (2007). An analysis of the dynamics of the tourism industry in Brazil: challenges and recommendations. International Journal of Contemporary Hospitality Management, 19, 507–512. http://dx.doi.org/10.1108/09596110710775165
Song, H., & Li, G. (2008). Tourism demand modelling and forecasting—a review of recent research. Tourism manage-ment, 29, 203–220. https://doi.org/10.1016/j.tourman.2007.07.016
Tang, J., Ramos, V., Cang, S., & Sriboonchitta, S. (2017). An empirical study of inbound tourism demand in China: a copula-garch approach. Journal of Travel & Tourism Marketing, 34, 1235–1246. https://doi.org/10.1080/10548408.2017.1330726
Tang, J., Sriboonchitta, S., Ramos, V., & Wong, W.-K. (2016). Modelling dependence between tourism demand and ex-change rate using the copula-based garch model. Current Issues in Tourism, 19, 876–894. https://doi.org/10.1080/13683500.2014.932336
Tavares, J. M., & Leitão, N. C. (2017). The determinants of international tourism demand for Brazil. Tourism Econom-ics, 23, 834–845. https://doi.org/10.5367%2Fte.2016.0540
Ulucak, R., Yücel, A. G., & ˙Ilkay, S. Ç. (2020). Dynamics of tourism demand in Turkey: Panel data analysis using gravity model. Tourism Economics, 1354816620901956. https://doi.org/10.1177%2F1354816620901956
Untong, A., Ramos, V., Kaosa-Ard, M., & Rey-Maquieira, J. (2015). Tourism demand analysis of Chinese arrivals in Thailand. Tourism Economics, 21, 1221–1234. https://doi.org/10.5367%2Fte.2015.0520
Valença, M. N., de Souza Melo, A., Sobral, M. F. F., & Xavier, M. G. P. (2015). Relação entre a taxa de câmbio e o setor de turismo: Análise por vetores autorregressivos. Turismo-visão e ação, 17, 737–757. https://doi.org/10.14210/rtva.v17n3.p737-757
Vanegas Sr, M., & Croes, R. R. (2000). Evaluation of demand: Us tourists to Aruba. Annals of Tourism Research, 27, 946–963. https://doi.org/10.1016/S0160-7383(99)00114-0
Wang, H.-C., Chen, N.-H., Lu, C.-L., & Hwang, T.-C. (2008). Tourism demand and exchange rates in Asian countries: New evidence from copulas approach. In 2008 Third International Conference on Convergence and Hybrid Information Technology, 1188–1193. https://doi.org/10.1109/ICCIT.2008.416
Wanke, P., Figueiredo, O. H. d. S., & Moreira Antunes, J. J. (2019). Unveiling endogeneity and temporal dependence be-tween tourism revenues/expenditures and macroeconomic variables in Brazil: A stochastic hidden markov model approach. Tourism Economics, 25, 3–21. https://doi.org/10.1177%2F1354816618787578
Webber, A. G. (2001). Exchange rate volatility and cointegration in tourism demand. Journal of Travel research, 39, 398–405. https://doi.org/10.1177%2F004728750103900406
Wu, D. C., Song, H., & Shen, S. (2017). New developments in tourism and hotel demand modeling and forecasting. Inter-national Journal of Contemporary Hospitality Management. http://dx.doi.org/10.1108/IJCHM-05-2015-0249
Zhang, H., Zhang, J., & Kuwano, M. (2012). An integrated model of tourists’ time use and expenditure behaviour with self-selection based on a fully nested archimedean copula function. Tourism Management, 33, 1562–1573. https://doi.org/10.1016/j.tourman.2012.03.004
Zhang, Y. (2015). International arrivals to Australia: Determinants and the role of air transport policy. Journal of Air Transport Management, 44, 21-24. https://doi.org/10.1016/j.jairtraman.2015.02.004
Zhu, L., Lim, C., Xie, W., & Wu, Y. (2017). Analysis of tourism demand serial dependence structure for forecasting. Tour-ism Economics, 23, 1419–1436. https://doi.org/10.1177%2F1354816617693964
Zhu, L., Lim, C., Xie, W., & Wu, Y. (2018). Modelling tourist flow association for tourism demand forecasting. Current Issues in Tourism, 21, 902–916. https://doi.org/10.1080/13683500.2016.1218827 .
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