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The objective of this research was to study the concordance between the volatility of the monthly growth rates of the Chinese tourists traveling to Thailand ( ) and Singapore ( ) from 2008-2017. The copula based ARMA-GARCH model was used in this research. The results showed that ARMA (0,1)-GARCH (1,1) models with Skew Normal distribution were the models suitable for the two data series. After that, the copula model was employed to analyze the concordance between the volatility of the data series: and obtained from ARMA (0,1) -GARCH (1,1) models. The results showed that the Gumbel copula was appropriate to describe the structure of concordance. The concordance value between the data series was in the form of Kendall’s tau representing the dependency, was equal to 0.45. This indicated that the volatility between the two data series was concordant in the same direction at a moderate level. That is, if the growth rate of Chinese tourists traveling to one country is low, that of another country will also be low as well. The concordance value on the upper tail was 0.54, indicating the dependent value at a moderate level between two data series on the right tail, or the data with the upper value. Therefore, if the growth rate of Chinese tourists traveling to Thailand is more volatile or extreme, the growth rate of Chinese tourists traveling to Singapore will be in a concordance manner at a moderate level or vice versa. This finding can be useful for risk management in the tourism industry related to carrying the number of Chinese tourists traveling to both countries.
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