Mathematics,
Год журнала:
2024,
Номер
12(23), С. 3789 - 3789
Опубликована: Ноя. 30, 2024
This
study
employed
the
dynamic
conditional
correlation
algorithm
and
incorporated
temporal
dynamics
of
spillover
effect
to
enhance
Multivariate
Stochastic
Volatility
(MSV)
model.
Consequently,
a
DGC-t-MSV
model
(multiple
stochastic
volatility
coefficient
with
Granger
causality
test)
was
constructed
simulate
examine
effects
between
China’s
carbon
market
traditional
energy
market.
The
findings
reveal
following:
(1)
A
significant
in
price
exists
markets,
notably
fluctuating
index.
China
exerts
stronger
unidirectional
on
Price
fluctuations
impact
prices
through
mechanisms
such
as
cost
transmission
expectations.
(2)
In
initial
stages,
markets
showed
an
overall
downward
trend,
underscoring
positive
influence
policy
incentives
technological
advancements
growth
alternative
energy.
mutual
weakening
markets.
(3)
display
high
degree
interdependence
short-term
persistence,
evidence
long
memory
inertia
these
movements.
Integration
Bayesian
approach
Markov
Chain
Monte
Carlo
(MCMC)
method
introduction
time-varying
factor
enabled
efficient
measurement
Applied Economics,
Год журнала:
2024,
Номер
unknown, С. 1 - 17
Опубликована: Авг. 8, 2024
To
quantify
the
impacts
of
risk
shocks
on
time-domain
and
frequency-domain
spillovers,
we
propose
a
new
empirical
framework
based
TVP-VAR
wavelet
coherence
analysis.
We
illustrate
methodology
by
analysing
spillovers
among
gold,
oil,
emerging,
developed
markets
from
10
February
2011
to
2
April
2024
obtain
intriguing
findings.
First,
dynamic
rise
significantly
during
turbulent
periods.
The
net
spillover
results
show
that
gold
emerging
are
mainly
receivers,
emitters,
oil
market
plays
switching
role
over
time.
Second,
have
frequency-dependent
markets.
effects
concentrated
in
medium-
long-term
ranges
2015,
2018,
2020–2021,
relationship
between
volatility
total
connectedness
is
positive.
impact
heterogeneous
time
frequency
domains
lead-lag
relationship.
Our
findings
important
implications
for
policymakers
investors.
Mathematics,
Год журнала:
2024,
Номер
12(23), С. 3789 - 3789
Опубликована: Ноя. 30, 2024
This
study
employed
the
dynamic
conditional
correlation
algorithm
and
incorporated
temporal
dynamics
of
spillover
effect
to
enhance
Multivariate
Stochastic
Volatility
(MSV)
model.
Consequently,
a
DGC-t-MSV
model
(multiple
stochastic
volatility
coefficient
with
Granger
causality
test)
was
constructed
simulate
examine
effects
between
China’s
carbon
market
traditional
energy
market.
The
findings
reveal
following:
(1)
A
significant
in
price
exists
markets,
notably
fluctuating
index.
China
exerts
stronger
unidirectional
on
Price
fluctuations
impact
prices
through
mechanisms
such
as
cost
transmission
expectations.
(2)
In
initial
stages,
markets
showed
an
overall
downward
trend,
underscoring
positive
influence
policy
incentives
technological
advancements
growth
alternative
energy.
mutual
weakening
markets.
(3)
display
high
degree
interdependence
short-term
persistence,
evidence
long
memory
inertia
these
movements.
Integration
Bayesian
approach
Markov
Chain
Monte
Carlo
(MCMC)
method
introduction
time-varying
factor
enabled
efficient
measurement
