Journal of Futures Markets,
Journal Year:
2023,
Volume and Issue:
43(11), P. 1615 - 1644
Published: July 14, 2023
Abstract
While
there
is
a
large
literature
on
modeling
volatility
smile
in
options
markets,
most
such
studies
are
eventually
focused
the
forecasting
performance
of
model
parameters
and
not
applicability
models
trading
environment.
Drawing
analogy
like
term
structure
context
interest
rates
fixed‐income
we
evaluate
Dynamic
Nelson–Siegel
(DNS)
approach
to
dynamics
environment
against
competing
alternatives.
Using
model‐based
mispricing
as
our
sorting
criterion,
deploying
strategy
going
long
upper
deciles
short
lower
deciles,
show
that
dynamic
consistently
outperform
their
static
counterparts,
with
worst
outperforming
best
terms
percentage
mean
returns
from
portfolios
Sharpe
ratio.
Specifically,
find
DNS
outperforms
all
other
specifications
selected
criteria.
Financial Innovation,
Journal Year:
2024,
Volume and Issue:
10(1)
Published: Jan. 29, 2024
Abstract
Determining
which
variables
affect
price
realized
volatility
has
always
been
challenging.
This
paper
proposes
to
explain
how
financial
assets
influence
by
developing
an
optimal
day-to-day
forecast.
The
methodological
proposal
is
based
on
using
the
best
econometric
and
machine
learning
models
forecast
volatility.
In
particular,
forecasting
from
heterogeneous
autoregressive
long
short-term
memory
are
used
determine
of
Standard
Poor’s
500
index,
euro–US
dollar
exchange
rate,
gold,
Brent
crude
oil
natural
gas.
These
influenced
gas
in
87.4%
days
analyzed;
rate
was
primary
asset
explained
40.1%
influence.
results
proposed
daily
analysis
differed
those
methodology
study
entire
period.
traditional
model,
studies
period,
cannot
temporal
effects,
whereas
can.
allows
us
distinguish
effects
for
each
day,
week,
or
month
rather
than
averages
periods,
with
flexibility
analyze
different
frequencies
periods.
capability
key
analyzing
influences
making
decisions
about
