Finance & Economics,
Journal Year:
2024,
Volume and Issue:
1(8)
Published: Aug. 14, 2024
The
new
energy
industry
is
developing
continuously
in
China,
and
products
are
also
gaining
popularity.
Investors’
attention
to
the
has
increased
significantly.
In
Chinese
market,
due
impact
of
coronavirus
epidemic,
development
entire
ups
downs.
This
research
selects
15
representative
stocks
sector.
After
distinguishing
between
epidemic
era
post-epidemic
era,
Markowitz
model
used
calculate
corresponding
tangential
portfolio
under
maximum
sharpe
ratio
two
periods
respectively.
comparing
change
position
weight
analyzing
reasons
for
change,
four
high-quality
established.
Corresponding
hybrid
large
enterprises.
Finally,
according
stability
overall
return
risk
industry,
it
concluded
that
investors
should
regularly
review
investment
profit
loss,
appropriately
incorporate
traditional
into
asset
hedging.
Journal of Futures Markets,
Journal Year:
2024,
Volume and Issue:
44(10), P. 1613 - 1639
Published: July 18, 2024
ABSTRACT
We
apply
a
Time‐Varying
Parameter
Vector
Auto
Regressive
(TVP‐VAR)
connectedness
approach
on
global
assets
to
investigate
time‐varying
dynamic
connectedness,
portfolio
performance,
and
hedge
effectiveness
during
COVID‐19
the
Russia–Ukraine
war.
With
increased
changing
role
of
energy
soft
commodities
these
two
events,
we
find
minimum
correlation
(connectedness)
performing
better
war
that
cumulative
returns
portfolios
are
higher
COVID‐19.
Additionally,
varying
(stable)
equity
market
indices
(cryptocurrencies).
This
paper
provides
specific
insights
investors
about
using
optimal
hedging
pandemics
military
conflicts.
China Finance Review International,
Journal Year:
2024,
Volume and Issue:
unknown
Published: June 24, 2024
Purpose
This
study
conducts
a
comparative
analysis
of
the
diversification
effects
China's
national
carbon
market
(CEA)
and
EU
ETS
Phase
IV
(EUA)
within
major
commodity
markets.
Design/methodology/approach
The
employs
TVP-VAR
extension
spillover
index
framework
to
scrutinize
information
spillovers
among
energy,
agriculture,
metal,
Subsequently,
explores
practical
applications
these
findings,
emphasizing
how
investors
can
harness
insights
from
refine
their
investment
strategies.
Findings
First,
CEA
provide
ample
opportunities
for
portfolio
between
metal
markets,
desirable
feature
that
EUA
does
not
possess.
Second,
comprising
exclusively
energy
assets
often
exhibits
highest
Sharpe
ratio.
Nevertheless,
inclusion
agricultural
commodities
in
carbon-oriented
may
potentially
compromise
its
performance.
Finally,
our
results
underscore
pronounced
advantage
minimum
portfolios;
particularly
those
designed
minimize
net
pairwise
volatility
spillover,
context
market.
Originality/value
addresses
previously
unexplored
intersection
with
an
emphasis
on
role
CEA.
PLoS ONE,
Journal Year:
2025,
Volume and Issue:
20(4), P. e0319844 - e0319844
Published: April 14, 2025
Structural
deformation
control
of
constructed
bridges
not
only
affects
the
alignment
bridge,
it
is
also
key
to
ensure
safety.
Factors
such
as
temperature
and
time
interval
in
actual
construction
can
make
bridge
deviate
from
design
state,
therefore,
this
paper
proposes
a
method
based
on
error
analysis
correction
eliminate
these
errors,
realize
structural
control.
The
cantilever
deflection
main
girder
modeled
effect
subsequent
cantilevers
at
current
section
further
considered.
elevation
caused
by
factors
calculated,
linear
minimum
variance
estimate
employed
reduce
error.
Practical
engineering
verification
carried
out
Shanxi,
where
proposed
implemented
measuring
comparing
with
original
value,
purpose
obtaining
reasonable
for
next
cantilever.
results
show
that,
application
correction,
generated
during
process
less
than
20
mm,
after
completion
30
shape
internal
condition
structure
be
conformed
requirements.
International Journal of Information Management Data Insights,
Journal Year:
2024,
Volume and Issue:
4(2), P. 100251 - 100251
Published: May 23, 2024
The
emergence
of
cryptocurrencies
has
generated
enthusiasm
and
concern
in
the
modern
global
economy.
However,
their
high
volatility,
erratic
price
fluctuations,
tendency
to
exhibit
bubbles
have
made
investors
cautious
about
investing
them.
Consequently,
it
is
essential
develop
methods
models
forecast
cryptocurrency
returns
benefit
investors,
traders,
scientific
community.
Despite
considerable
volume
research
on
Bitcoin
forecasting,
other
received
little
attention
academic
literature.
Additionally,
current
body
literature
predicting
prices
or
emphasizes
use
in-sample
methodologies.
this
method
susceptible
overfitting.
To
address
these
gaps
literature,
study
employs
autoregressive
moving
average
(ARMA),
generalized
conditional
heteroskedasticity
(GARCH),
exponential
(EGARCH),
long
short-term
memory
(LSTM)
deep
learning
neural
networks
for
ten
most
actively
traded
digital
currencies:
Bitcoin,
Ethereum,
Ripple,
Chainlink,
Litecoin,
Cardano,
Ethereum
Classic,
Cash,
Tether,
Binance
Coin.
assess
accuracy
two
models,
utilizes
an
out-of-sample
with
data
gathered
sequentially
from
November
9,
2017,
September
18,
2022.
results
indicate
that
all
accuracy,
as
evidenced
by
low
root
mean
square
error
(RMSE),
absolute
(MAE),
squared
(MSE)
values.
Meanwhile,
hybrid
EGARCH-LSTM
GARCH-LSTM
demonstrate
slightly
better
compared
models.
findings
are
valuable
researchers
involved
forecasting.
