Dynamic Copula-Based GARCH Model Analysis China Outbound Tourism Demand - Innovative Management in Information and Production - page 123

history information parameter is only significant in Gaussian copula. These results
show that the latest return information in Gaussian and Clayton copula and history
information in Gaussian is a meaningful measure; (4) between Singapore and South
Korea, the autoregressive parameter is only significant in Student-t copula, while
history information parameter is not significant in all copula. These results show that
the just latest return information in Student-t copula is a meaningful measure; (5)
between Singapore and Japan, the autoregressive and history information parameter
is only significant in Clayton copula. This result implies that the latest return
information and history information is a meaningful measure in Clayton copula;
(6) between Japan and South Korea, the autoregressive parameter is significant in
Gaussian, Gumbel, and Clayton copula, indicating a degree of persistence pertaining
to the dependence structure. History information parameter is significant in Student-
t and Clayton copula, indicating the latest return information is a meaningful
measure; (7) the degree of freedom is significant in all destination and not every
row (from 9 t0 141) in the Student-t copula, indicating extreme dependence and tail
dependence for all the tourist arrival return. The dependence parameter estimates
between the four destination returns are plotted in Figs. 3 - 6 . We can observe that
different copula generates different dependence structure. From figure, we know the
conditional dependence estimates (Pearson's

ρ t ) between four destinations based
on Guassian copula-GARCH. DT12 and DT23 have the same structure, increasing
and stabling at 0.70 and 0.326, respectively. All the dependence structure for
tourism demand among four destinations has shown increasing patterns, implying
that a positive relationship tends to increase as time progresses. Figure 4 plots the
conditional dependence estimates (Pearson's ρ t ) between the four destinations based

on Student-t copula-GARCH. DT12 is higher than other dependence structures and
close to 1 at some times, dictating that Thailand and Singapore have a higher
correlation and could be recognized as the “complement effect.” The reason is
their geographic position and the large number of groups of tourists traveling to
Thailand and Singapore at the same time. DT13, DT14, DT24, and DT34 have the
same structure and shock in 0.05, 0.2, 0.2, and 0.4, respectively. DT23 has a higher
relationship from 2000 to 2006.
Figure 5 illustrates the implied time paths of the conditional dependence es-
timates (Kendall's tau) between the four destinations, based on the Gumbel
copula-GARCH. The Gumbel copula captures the right tail dependence. All of
the conditional dependence changes over time. DT13 is very low and nearly 0.01;
it dictates that Thailand and South Korea have a lower correlation. It means
that the improbability of Thailand and South Korea tourist market booms at the
same time. DT23 and DT24's conditional dependence obviously exhibited negative
trends, implying that negative relationship tends to increase as time progresses.
Figure 6 plots the conditional dependence estimates (Kendall's tau) between the four
destinations based on the Clayton copula-GARCH. The Clayton copula captures the
left tail dependence. DT24 is very low and nearly 0.0001; it dictates that Singapore
and Japan have a lower correlation. It means that the improbability of Thailand and
South Korea tourist market crashes at the same time. DT13 jumps from 0.01 to 0.24,
and DT14 and DT34 shock around at 0.6 and 0.15, respectively.