AIMS Mathematics,
Journal Year:
2023,
Volume and Issue:
9(1), P. 896 - 917
Published: Dec. 5, 2023
<abstract><p>An
empirical
investigation
was
held
regarding
whether
topological
properties
associated
with
point
clouds
formed
by
cryptocurrencies'
prices
could
contain
information
on
(locally)
explosive
dynamics
of
the
processes
involved.
Those
are
financial
bubbles.
The
Phillips,
Shi
and
Yu
<sup>[<xref
ref-type="bibr"
rid="b33">33</xref>,<xref
rid="b34">34</xref>]</sup>
(PSY)
timestamping
method
as
well
notions
Topological
Data
Analysis
(TDA)
like
persistent
simplicial
homology
landscapes
were
employed
a
dataset
consisting
time
series
daily
closing
Bitcoin,
Ethereum,
Ripple
Litecoin.
note
provides
some
evidence
that
TDA
be
useful
in
detecting
If
robust,
such
an
conclusion
opens
interesting
paths
further
research.</p></abstract>
Applied Economics,
Journal Year:
2024,
Volume and Issue:
unknown, P. 1 - 18
Published: June 27, 2024
This
study
primarily
explores
the
mechanisms
of
risk
propagation
among
cryptocurrencies,
unveiling
for
first
time
frequency
dimension
within
cryptocurrency
market
and
identifying
role
oscillation
in
this
process.
By
employing
Variational
Mode
Decomposition
(VMD)
DY
spillover
matrix
to
construct
a
complex
network,
paper
analyzes
nine
major
cryptocurrencies
from
2017
2023.
Key
findings
include
significant
capabilities
Ethereum
(ETH)
Bitcoin
(BTC)
during
periods
high
volatility,
while
stablecoins
such
as
Tether
USD
Coin
exhibit
minimal
ability.
Additionally,
characteristics
have
been
enhanced
following
COVID-19
pandemic.
Overall,
most
is
realized
through
high-frequency
oscillations.
The
robustness
conclusions
verified
using
Time-Varying
Parameter
Vector
Autoregressive
(TVP-VAR)
model.
results
are
understanding
dynamic
market,
predicting
future
risks,
formulating
management
strategies.
Furthermore,
methodology
provide
new
perspectives
tools
exploring
relationships
cryptocurrencies.
Fractal and Fractional,
Journal Year:
2024,
Volume and Issue:
8(10), P. 571 - 571
Published: Sept. 29, 2024
Jump
dynamics
in
financial
markets
exhibit
significant
complexity,
often
resulting
increased
probabilities
of
subsequent
jumps,
akin
to
earthquake
aftershocks.
This
study
aims
understand
these
complexities
within
a
multifractal
framework.
To
do
this,
we
employed
the
high-frequency
intraday
data
from
six
major
cryptocurrencies
(Bitcoin,
Ethereum,
Litecoin,
Dashcoin,
EOS,
and
Ripple)
forex
(Euro,
British
pound,
Canadian
dollar,
Australian
Swiss
franc,
Japanese
yen)
between
4
August
2019
October
2023,
at
5
min
intervals.
We
began
by
extracting
daily
jumps
realized
volatility
using
MinRV-based
approach
then
applying
Multifractal
Detrended
Fluctuation
Analysis
(MFDFA)
those
explore
their
characteristics.
The
results
MFDFA—especially
fluctuation
function,
varying
Hurst
exponent,
Renyi
exponent—confirm
that
all
jump
series
properties.
However,
range
exponent
values
indicates
Dashcoin
has
highest
Litecoin
lowest
strength.
Moreover,
show
persistent
behavior
positive
autocorrelation,
indicating
higher
probability
positive/negative
being
followed
another
jump.
Additionally,
findings
rolling-window
MFDFA
with
window
length
250
days
reveal
most
time.
These
are
useful
for
market
participants,
investors,
policymakers
developing
portfolio
diversification
strategies
making
important
investment
decisions,
they
could
enhance
efficiency
stability.
