Analysis of Volatility and Dependence between the Tourist Arrivals from China to Thailand and Singapore: A Copula-Based GARCH Approach

This paper aims to estimate the dependency between the growth rates of tourist arrivals of Thailand and Singapore from China, and also analyze their conditional volatilities. Firstly, we assume that both margins are skewed-t distribution, and then make use of ARMA-GARCH model to fit monthly time series data. Secondly, fifteen types of static copulas are used to fit static dependence between tourist arrivals to Thailand and Singapore from China. We take the AIC, BIC and the two tests based on Kendall’s transform as criterions for goodness of fit test. Moreover, we apply time-varying copulas that described the dynamic Kendall’s tau process. Results show that each growth rate of tourist arrivals has a long-run persistence of volatility, and the time-varying Gaussian copula has the highest explanatory power of all the dependence structures between tourist arrivals to Thailand and Singapore from China in terms of AIC and BIC values.

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