Applied Computational Intelligence and Soft Computing,
Год журнала:
2025,
Номер
2025(1)
Опубликована: Янв. 1, 2025
The
ongoing
conflict
between
Russia
and
Ukraine
has
introduced
significant
volatility
into
the
global
oil
markets,
highlighting
need
for
robust
forecasting
models
to
understand
anticipate
price
fluctuations
during
such
geopolitical
events.
This
study
presents
a
comprehensive
hybrid
modeling
approach
predict
prices
in
context
of
across
three
distinct
periods:
before
war,
immediate
aftermath
conflict.
Using
advanced
machine
learning
techniques,
we
developed
system
combining
Random
Forest,
ElasticNet,
K‐Nearest
Neighbors,
Gradient
Boosting,
Support
Vector
Regressor
models.
These
were
integrated
through
Voting
enhance
prediction
accuracy.
Our
analysis
revealed
varying
levels
predictive
performance
different
periods.
Prior
showed
strong
capabilities,
evidenced
by
low
mean‐squared
error
(MSE)
values
high
coefficients
determination
(
R
2
).
However,
struggled
capture
extreme
volatility,
resulting
significantly
increased
MSE
negative
values.
Predictions
demonstrated
improvements,
with
reduction
positive
values,
indicating
return
relatively
more
stable
market
conditions.
Notably,
data
from
both
war
periods
could
further
improve
accuracy,
as
it
would
reduce
impact
conflict’s
on
model
performance.
results
emphasize
challenges
instability
underscore
importance
approaches
adapt
rapidly
changing
dynamics.
Energy Economics,
Год журнала:
2024,
Номер
132, С. 107468 - 107468
Опубликована: Март 15, 2024
Should
investors
and
policy
makers
in
agricultural
markets
consider
oil
market's
incontestable
impact
on
portfolio
risk
management?
This
paper
investigates
the
time-varying
market
linkages
between
energy
commodities
presence
of
two
important
exogenous
shocks,
viz.,
COVID-19
pandemic
subsequent
2022
Russia–Ukraine
military
conflict.
We
use
a
novel
parameter
vector
autoregressive
model
with
common
factor
error
structure
to
estimate
tail
connectedness
for
period
December
31,
2019
18,
2023.
Our
findings
provide
clear
evidence
asymmetry
volatility
evolution.
determine
that
spillover
magnitudes
are
much
stronger
across
quantiles
than
at
mean.
note
crude
is
main
transmitter
shocks
system
before
onset
Russia-Ukraine
conflict
lower
distribution.
While
natural
gas
transmit
both
pre-
post-conflict
announcement
periods.
Furthermore,
found
transmission
commodities.
Numerous
observed
shift
their
position
from
transmitters
receivers
volatility,
vice
versa,
due
Ukraine.
causality
results
depict
patterns
other
has
varying
Commodities
which
conflicting
countries
major
world
exports
of,
such
as
wheat,
have
notably
increased
dependency
oil.
Thus,
we
advise
policymakers
seriously
management
monitoring
policies.