Demanda turística internacional e taxa de câmbio
modelagem de dependência baseada no modelo copula-GARCH
DOI:
https://doi.org/10.7784/rbtur.v16.2263Palavras-chave:
Turismo, Cópulas, Taxas de Câmbio.Resumo
A taxa de câmbio pode ser um fator determinante na demanda turística e alterar a competitividade da oferta do trade turístico. O objetivo deste estudo é mensurar a dependência entre a demanda turística internacional e a taxa de câmbio no Brasil. Investigações empíricas dessa relação, pesquisadas há décadas, são relativamente recentes com o uso de modelos cópula-GARCH, na literatura mundial. Este estudo é realizado com dados mensais das taxas de câmbio e do número de chegadas internacionais da Argentina, Estados Unidos e Alemanha, entre 1999 e 2018. Os dados passam por um processo inicial de modelagam de suas distribuições marginais, por meio de modelos ARMA-GARCH, devido sua dependência temporal, e posteriormente, seus os resíduos são utilizados no processo de estimação das cópulas, de onde são extraídas as medidas de associação. Os resultados indicam que a variação da taxa de câmbio não está diretamente associada ao número de chegadas de turistas vindos da Alemanha e dos Estados Unidos. Entretanto, para a Argentina, o resultado da medida de correlação foi negativo e estatisticamente significativo, indicando uma fraca associação entre as variáveis. Esse sinal indica que quando a moeda local se desvaloriza em relação à moeda brasileira, o número de chegadas diminui. As conclusões deste estudo podem ajudar gestores de organizações turísticas a compreender a relação entre câmbio e demanda turística internacional no Brasil.
Downloads
Referências
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 .
Publicado
Como Citar
Edição
Seção
Licença
Copyright (c) 2021 Bruno Vitor Luna Gouveia, Mariana de Freitas Coelho, Júlio César Araújo da Silva Júnior, Maurício Silva Lacerda
![Creative Commons License](http://i.creativecommons.org/l/by/4.0/88x31.png)
Este trabalho está licenciado sob uma licença Creative Commons Attribution 4.0 International License.
Autores que publicam nesta revista concordam com os seguintes termos:
a. Autores mantém os direitos autorais e concedem à revista o direito de primeira publicação, com o trabalho simultaneamente licenciado sob a Licença Pública Creative Commons Atribuição 4.0 Internacional (CC BY 4.0) que permite o compartilhamento do trabalho com reconhecimento da autoria e publicação inicial nesta revista.
b. Autores têm autorização para assumir contratos adicionais separadamente, para distribuição não-exclusiva da versão do trabalho publicada nesta revista (ex.: publicar em repositório institucional ou como capítulo de livro), com reconhecimento de autoria e publicação inicial nesta revista.
c. Autores têm permissão e são estimulados a publicar e distribuir seu trabalho online (ex.: em repositórios institucionais ou na sua página pessoal) a qualquer ponto antes ou durante o processo editorial, já que isso pode gerar alterações produtivas, bem como aumentar o impacto e a citação do trabalho publicado (Veja O Efeito do Acesso Livre).