as.sropt | R Documentation |
Computes the Sharpe ratio of the Markowitz portfolio of some observed returns.
as.sropt(X, drag = 0, ope = 1, epoch = "yr")
## Default S3 method:
as.sropt(X, drag = 0, ope = 1, epoch = "yr")
## S3 method for class 'xts'
as.sropt(X, drag = 0, ope = 1, epoch = "yr")
X |
matrix of returns, or |
drag |
the 'drag' term, |
ope |
the number of observations per 'epoch'. For convenience of
interpretation, The Sharpe ratio is typically quoted in 'annualized'
units for some epoch, that is, 'per square root epoch', though returns
are observed at a frequency of |
epoch |
the string representation of the 'epoch', defaulting to 'yr'. |
Suppose x_i
are n
independent draws of a q
-variate
normal random variable with mean \mu
and covariance matrix
\Sigma
. Let \bar{x}
be the (vector) sample mean, and
S
be the sample covariance matrix (using Bessel's correction). Let
\zeta(w) = \frac{w^{\top}\bar{x} - c_0}{\sqrt{w^{\top}S w}}
be the (sample) Sharpe ratio of the portfolio w
, subject to
risk free rate c_0
.
Let w_*
be the solution to the portfolio optimization problem:
\max_{w: 0 < w^{\top}S w \le R^2} \zeta(w),
with maximum value z_* = \zeta\left(w_*\right)
.
Then
w_* = R \frac{S^{-1}\bar{x}}{\sqrt{\bar{x}^{\top}S^{-1}\bar{x}}}
and
z_* = \sqrt{\bar{x}^{\top} S^{-1} \bar{x}} - \frac{c_0}{R}
The units of z_*
are \mbox{time}^{-1/2}
.
Typically the Sharpe ratio is annualized by multiplying by
\sqrt{\mbox{ope}}
, where \mbox{ope}
is the number of observations
per year (or whatever the target annualization epoch.)
Note that if ope
and epoch
are not given, the
converter from xts
attempts to infer the observations per epoch,
assuming yearly epoch.
An object of class sropt
.
Steven E. Pav shabbychef@gmail.com
sropt
, sr
, sropt-distribution functions,
dsropt, psropt, qsropt, rsropt
Other sropt:
confint.sr()
,
dsropt()
,
is.sropt()
,
pco_sropt()
,
power.sropt_test()
,
reannualize()
,
sropt
,
sropt_test()
nfac <- 5
nyr <- 10
ope <- 253
# simulations with no covariance structure.
# under the null:
set.seed(as.integer(charToRaw("be determinstic")))
Returns <- matrix(rnorm(ope*nyr*nfac,mean=0,sd=0.0125),ncol=nfac)
asro <- as.sropt(Returns,drag=0,ope=ope)
# under the alternative:
Returns <- matrix(rnorm(ope*nyr*nfac,mean=0.0005,sd=0.0125),ncol=nfac)
asro <- as.sropt(Returns,drag=0,ope=ope)
# generating correlated multivariate normal data in a more sane way
if (require(MASS)) {
nstok <- 10
nfac <- 3
nyr <- 10
ope <- 253
X.like <- 0.01 * matrix(rnorm(500*nfac),ncol=nfac) %*%
matrix(runif(nfac*nstok),ncol=nstok)
Sigma <- cov(X.like) + diag(0.003,nstok)
# under the null:
Returns <- mvrnorm(ceiling(ope*nyr),mu=matrix(0,ncol=nstok),Sigma=Sigma)
asro <- as.sropt(Returns,ope=ope)
# under the alternative
Returns <- mvrnorm(ceiling(ope*nyr),mu=matrix(0.001,ncol=nstok),Sigma=Sigma)
asro <- as.sropt(Returns,ope=ope)
}
# using real data.
if (require(xts)) {
data(stock_returns)
asro <- as.sropt(stock_returns)
}
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.