Analyzing relationship between tourist arrivals from China and India to Thailand using copula based GARCH and seasonal pattern

• Main Authors: Ornanong Puarattanaarunkorn, Songsak Sriboonchitta
• Format: Book Series
• Published: 2018
• Subjects: Computer Science; Engineering
• Online Access: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84897895114&origin=inward; http://cmuir.cmu.ac.th/jspui/handle/6653943832/53403
• Institution: Chiang Mai University

Chinese and Indian are the emerging tourist markets for Thailand. The two nations have tourism potential and make for interesting on doing a study about their tourism demand that was measure as the number of tourist arrivals. This study analyzed relationship between the tourist arrivals from China and India to Thailand by using the copula based GARCH model and the seasonal pattern. The findings by the copula based GARCH model show that there exists a weak positive dependence between the growth rates of tourist arrivals from China and India to Thailand and that this dependence keeps varying over time. The rotated Joe 180° copula, which can capture the lower (left) tail dependence, is chosen to describe the dependence structure. These mean that the growth rates of the tourist arrivals from China and India show a co-movementwhich is both upward and downward but with weak dependence. The rise or loss of tourism demand from China (India) is slightly correlated by a rise or loss of tourism demand from India (China). These results correspond to the seasonal patterns in which the seasonal pattern of China is in a direction opposite to the seasonal pattern of India in several periods, and the patterns showing a co-movement during some periods. Understanding the relationship between Chinese arrivals and Indian arrivals in each time period, it could contribute to policy implications such as developing the appropriate marketing and promotion strategies to attract other tourist markets as substitutes when we lose the regular tourist markets due to shock effects or low season. © Springer International Publishing Switzerland 2014.