An empirical study of inbound tourism demand in China: a copula-GARCH approach

Tang, Jiechen; Ramos, Vicente; Cang, Shuang; Sriboonchitta, Songsak

doi:10.1080/10548408.2017.1330726

Cited by 13 publications

(10 citation statements)

References 35 publications

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“…Recently, copula methods have been applied to model the temporal dependence of some variables referring to tourism through copulas (Tang et al, 2017; Zhu et al, 2017). But there are few studies that treat dependence simultaneously of different tourism variables by copulas; one example is Cai et al (2019), which studies the impact of certain climatic variables on the tourist destination.…”

Section: Introductionmentioning

confidence: 99%

A Multivariate Prediction Copula Model to Characterize the Expenditure Categories in Tourism

Brida

Lanzilotta

Moreno

et al. 2020

Journal of Hospitality & Tourism Research

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The aim of this article is to introduce a multivariate statistical model that represents the expenditure of tourists disaggregated by categories. The model is applied to study the distribution of the expenditure of cruise passengers in Uruguay, using data of the 2016-2017 cruise season survey (collected by the Ministry of Tourism). Given the mixed distribution in each component of the main variable, the model is implemented in two stages and using copulas to obtain a conditional distribution of the different items of expenditure, characterizing the dependence between them. The empirical results show that the key variables that determine the average spending of cruise tourists are their residence and the port of arrival of the cruise. The parameters representing dependence of the copula show moderate association between the different categories of expenditure, in particular for cruisers disembarking in Montevideo, the capital of Uruguay. In addition, it can be noted that the expenditure pattern in each item shows time dependence. In general, the empirical results show that a cruiser that spends more on one item is likely to spend more (less) on a complementary (noncomplementary) items of expense.

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Section: Introductionmentioning

confidence: 99%

A Multivariate Prediction Copula Model to Characterize the Expenditure Categories in Tourism

Brida

Lanzilotta

Moreno

et al. 2020

Journal of Hospitality & Tourism Research

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“…Primeiro, os parâmetros de dependência nas cópulas propostas podem ser constantes ou variáveis no tempo para capturar possíveis relações dinâmicas e não lineares (Pérez-Rodríguez et al, 2015). Ademais, o modelo cópula fornece informações úteis sobre a dependência de cauda que uma simples análise de correlação linear não pode fornecer (Tang et al, 2017). Terceiro, são capazes de capturar dependência simétrica e assimétrica (Tang et al, 2016).…”

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“…Por exemplo, Zhang et al (2012) elaboraram um modelo composto por uma especificação do modelo Tobit e uma cópula arquimediana para analisar a dependência entre o uso do tempo e o comportamento dos gastos dos turistas no Japão. Além disso, estudos analisaram o co-movimento de fluxos turísticos em países asiáticos (Liu & Sriboonchitta, 2013;Puarattanaarunkorn & Sriboonchitta, 2014;Liu et al, 2014;Tang et al, 2017). A aplicação de cópulas foi estendida para incluir previsão de demanda turística internacional (Zhu et al, 2017(Zhu et al, , 2018…”

Section: Fonte: Elaborado Pelos Autoresunclassified

“…Estudos recentes utilizam cópulas com o objetivo de analisar a relação entre demanda turística internacional e seus determinantes (Zhang et al, 2012;Pérez-Rodríguez et al, 2015;Wanke et al, 2019). Cópulas também foram aplicadas para analisar a relação de dependência entre diferentes fluxos turísticos (Liu & Sriboonchitta, 2013;Liu, Sriboonchitta, Nguyen & Kreinovich, 2014;Puarattanaarunkorn & Sriboonchitta, 2014;Tang, Ramos, Cang & Sriboonchitta, 2017) ou na previsão de demanda Lim;Xie & Wu, 2017. Contudo, encontramos apenas dois estudos que investigaram a relação de dependência, por meio de cópulas, entre as taxas de câmbio e demanda turística internacional (Tang, Sriboonchitta, Ramos &Wong, 2016;Chang & Chang, 2020), sendo que a abordagem ainda não foi aplicada no contexto brasileiro.…”

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Demanda turística internacional e taxa de câmbio

Gouveia

Coelho

Júnior

et al. 2022

RBTUR

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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.

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“…The copula function can show co-movement during extreme events, and both constant and time-varying dependence can be considered. Since it is crucial for tourism developers and destination managers to understand the implied threat to the tourism industry of volatility in tourism demand, recent research has investigated the time-varying dependence between tourism demand and its associated variables, e.g., [11,18], and the interdependence between tourist source countries or between tourist destinations [1,[19][20][21] by applying dynamic copula models.…”

Section: Introductionmentioning

confidence: 99%

Co-Movement between Tourist Arrivals of Inbound Tourism Markets in South Korea: Applying the Dynamic Copula Method Using Secondary Time Series Data

Choi

Kim

2021

Sustainability

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Tourism demand is severely affected by unpredicted events, which has prompted scholars to examine ways of predicting the effects of positive and negative shocks on tourism, to ensure a sustainable tourism industry. The purpose of this study was to investigate if non-linear dependence structures exist between tourist flows into South Korea from five major source countries, as South Korea has undergone fluctuations in tourist arrivals due to diverse circumstances and has complex relations with tourism source countries. Additionally, the study examines the structures of extreme tail dependence, which is indicated in the case of unexpected events, and identifies how co-movements vary over time through dynamic copula–GARCH (generalized autoregressive conditional heteroskedasticity) tests. The secondary time series data for the 2005–2019 period of tourist arrivals to Korea were derived from the Korea Tourism Knowledge and Information System for testing the copula models. The copula estimations indicate significant dependencies among all market pairs as well as the strongest dependence between China and Taiwan. Moreover, extreme tail dependence structures show co-movements for four pairs of tourism markets in only negative shocks, for five pairs in both positive and negative conditions, but no co-movement in the China–Taiwan pair. Finally, the dynamic dependence structures reveal that the China–Taiwan dependence is higher than the other time-varying dependence structures, implying that the two markets complement each other.

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An empirical study of inbound tourism demand in China: a copula-GARCH approach

Cited by 13 publications

References 35 publications

A Multivariate Prediction Copula Model to Characterize the Expenditure Categories in Tourism

A Multivariate Prediction Copula Model to Characterize the Expenditure Categories in Tourism

Demanda turística internacional e taxa de câmbio

Co-Movement between Tourist Arrivals of Inbound Tourism Markets in South Korea: Applying the Dynamic Copula Method Using Secondary Time Series Data

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