Dynamic Copula-Based GARCH Model Analysis China Outbound Tourism Demand - Innovative Management in Information and Production - page 114

China underwent a rapid growth from 2000 to 2010. Outbound travel has increased
from around 10.5 million in 2000 to 57.4 million in 2010, the average annual growth
rate is 18.5% Tourism Flows Outbound C China ( 2010 ). According to the WTO,
China placed third position in international tourism spending in 2010 UNWTO
Tourism Highlights 2011 Edition ( 2011 ). This information highlights that China
has became one of most important tourism source country in the global tourism
market, and continuous growth of outbound tourism will bring tremendous business
opportunities.
The purpose of this study is to examine the time-varying volatility and time-
varying dependence structure among the destinations in China outbound tourism
demand, we selected South Korea, Japan, Singapore, and Thailand as sample for
this study (the top four tourism destinations for China mainland tourist). Based on
the motivations discussed above, four research questions were formulated for this
study: (1) Is the volatility high or low among the four destinations? (2) What is
the conditional dependence among the four destinations? (3) Is the dependence
between the four destinations time-varying over the study time horizon? (4) Is
the dependence negative (substitute) or positive (complement) among the four
destinations? The answer of these four questions can be used to help destination
manager and policy makers.
This paper is organized as follows. Section 2 provides a literature review of
the tourism demand. Section 3 describes the econometrics models used in the
paper, namely, dynamic copula—GARCH. Section 4 discusses the data presented
in the paper and also describes the estimate results of four kinds of copula-based
GARCH. The last section provides implications for policy planning and destination
management.

2 Literature Review

A large number of scholars have used the autoregressive conditional heteroskedas-
ticity (GARCH) model as their tourism model ( Chan et al. ( 2005 ); Shaeef and
McAleer ( 2005 , 2007 ); Seo et al. ( 2009 ); Kim and Wong ( 2006 ); Bartolom et al.
( 2009 ); Co¸kun and Ozer ( 2011 ); Daniel and Rodrigues ( 2010 )). The univariate
the autoregressive conditional heteroskedasticity (GARCH) model was applied in
the Shaeef and McAleer ( 2005 ), Kim and Wong ( 2006 ), Bartolom et al. ( 2009 ),
and Daniel and Rodrigues ( 2010 ), which analyze tourism demand at different time
series frequencies, ranging from monthly, weekly, and daily data. However, the
univariate GARCH model has a drawback that it cannot examine the conditional
correlation or dependence among destination. Hence, Chan et al. ( 2005 ), Shaeef
and McAleer ( 2005 ), and Bartolom et al. ( 2009 ) developed multivariate GARCH
model for researching tourism demand, based on the univariate GARCH model.
For example, Chan et al. ( 2005 ) used the symmetric CCC-MGARCH, symmetric
VARMA-GARCH, and asymmetric VARMA-GARCH to study Australia's tourism
demand from the four leading source countries. They examined the presence of
interdependent effects in the conditional variance between the four leading countries