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Authors
Advisor(s)
Abstract(s)
We
show
that
the
out-of-sample
forecast
of
the
equity
risk
premium
can
b e
signi cantly
improved
by
taking
into
account
the
frequency-domain
relationship
b etween
the
equity
risk
premium
and
several
p otential
predictors.
We
consider
fteen
predictors
from
the
existing
literature,
for
the
out-of-sample
forecasting
p erio d
from
January
1990
to
Decemb er
2014.
The
b est
result
achieved
for
individual
predictors
is
a
monthly
out-of-sample
R
2
of
2.98
%
and
utility
gains
of
549
basis
p oints
p er
year
for
a
mean-variance
investor.
This
p erformance
is
improved
even
further
when
the
individual
forecasts
from
the
frequency-
decomp osed
predictors
are
combined.
These
results
are
robust
for
di erent
subsamples,
including
the
Great
Mo deration
p erio d,
the
Great
Financial
Crisis
p erio d
and,
more
generically,
p erio ds
of
bad,
normal
and
go o d
economic
growth.
The
strong
and
robust
p erformance
of
this
metho d
comes
from
its
ability
to
disentangle
the
information
aggregated
in
the
original
time
series
of
each
variable,
which
allows
to
isolate
the
frequencies
of
the
predictors
with
the
highest
predictive
p ower
from
the
noisy
parts.
Description
Keywords
Predictability Equity risk premium Frequency domain Discrete wavelets
Citation
Faria, G., Verona, F. (2016). Forecasting the equity risk premium with frequency-decomposed predictors. Working papers: Economics. N.º 6, 40 p.