Applied Economics Letters,
Год журнала:
2024,
Номер
unknown, С. 1 - 7
Опубликована: Июль 30, 2024
This
paper
uses
quantile
regression
to
investigate
how
carbon
market
prices
affect
oil
fear.
Our
results
show
a
significant
and
strong
negative
effect
of
changes
in
on
fear
at
high
quantiles.
Notably,
this
is
driven
by
the
decrease
rather
than
increase
prices.
We
also
observe
that
COVID-19
pandemic
enhances
impact
decreased
International Review of Economics & Finance,
Год журнала:
2024,
Номер
95, С. 103507 - 103507
Опубликована: Авг. 16, 2024
This
study
explores
the
interdependencies
among
developed
stock
markets
using
LASSO
technique
with
quantile
regression
within
a
network
analysis
framework.
Traditional
forecasting
methods
often
fail
during
volatile
market
conditions,
necessitating
innovative
approaches
that
blend
interconnectedness
and
factor
modeling.
By
employing
regression,
which
examines
financial
assets
across
various
distribution
quantiles,
this
addresses
tail
risk,
critical
aspect
of
behavior
crises.
The
framework
provides
insights
into
relationships
between
markets,
highlighting
how
variables
interact
complex
system.
assesses
behaviors
at
different
levels,
considering
clustering
coefficients
to
analyze
cycles,
middlemen,
ins,
outs.
Additionally,
it
impact
several
factors
on
interconnectedness,
offering
interplay
individual
stocks
broader
conditions.
Key
findings
demonstrate
incorporating
models
enhances
accuracy
informs
better
decision-making,
leading
portfolios
can
withstand
extreme
conditions
provide
superior
risk-adjusted
returns.
Applied Economics Letters,
Год журнала:
2024,
Номер
unknown, С. 1 - 7
Опубликована: Июль 30, 2024
This
paper
uses
quantile
regression
to
investigate
how
carbon
market
prices
affect
oil
fear.
Our
results
show
a
significant
and
strong
negative
effect
of
changes
in
on
fear
at
high
quantiles.
Notably,
this
is
driven
by
the
decrease
rather
than
increase
prices.
We
also
observe
that
COVID-19
pandemic
enhances
impact
decreased