Studying Volatility and Dependency of Chinese Outbound Tourism Demand in Singapore, Malaysia, and Thailand: A Vine Copula Approach

Abstract

This paper investigates the volatility and dependence of Chinese tourism demand for Singapore, Malaysia, and Thailand (SMT) destinations, using the vine copula based auto regression moving average-generalized autoregressive conditional heteroskedasticity (ARMA-GARCH) model. It is found that a jolt to the tourist flow can have long-standing ramifications for the SMT countries. The estimation of the vine copulas among SMT show that the Survival Gumbel, Frank, and Gaussian copulas are the best copulas for Canonical vine (C-vine) or Drawable vine (D-vine) among the possible pair-copulas. In addition, this paper illustrates the making of time-varying Frank copulas for vine copulas. Finally, there is a discussion on tourism policy planning for better managing the tourism demand for the SMT countries. We suggest tour operators and national tourism promotion authorities of SMT collaborate closely in the marketing and promotion of joint tourism products.KeywordsBayesian Information CriterionGARCH ModelTail DependenceGaussian CopulaTourism DemandThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.