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|Title:||Copula based GARCH dependence model of Chinese and Korean tourist arrivals to Thailand: Implications for risk management|
|Abstract:||China and Korea are two of the important tourist markets for Thailand. The growth rates of tourist arrivals from these two countries have volatility and also seem to have co-movement. Understanding the dependence between these tourists markets has importance for strategic planning and processes for decision-making. The purpose of this study is to find out the dependence between the growth rates of tourist arrivals from China and Korea to Thailand by using the copula based GARCH model. Copula provides a potential and flexible method to model the dependence between random variables. It is preferable to the conventional approach because the copula can cross over the restriction of normal distribution and linear assumption, according to the Pearson correlation. The results of the analysis can contribute to appropriate policy implications. The results show that there exists a weak positive dependence and that the rotated Joe 180° copula is the best fit, which provides an evidence of lower tail dependence. The growth rates of tourist arrivals from China and Korea have co-movement that is both upward and downward, but with a weak dependence. The rise or loss of tourism demand from China (Korea) is slightly correlated by the rise or loss of tourism demand from Korea (China). The time-varying rotated Joe 180° copula is the best fit and the most significant, which implies that the dependence parameter has varied over time. The policy implications for the risk management of the tourism demand should provide enough motivation for the marketing and promotion of the tourism demand by considering the time-varying dependency of China and Korea. Moreover, they should consider alternative target markets as substitutes when theres a loss of arrivals from these two markets in order to diversify the risk of tourism demand. © Springer International Publishing Switzerland 2014.|
|Appears in Collections:||CMUL: Journal Articles|
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