Description Usage Arguments Details Value Author(s) References See Also Examples
Computes the prediction interval for Sharpe ratio.
1 2 
x 
a (nonempty) numeric vector of data values, or an
object of class 
oosdf 
the future (or 'out of sample', thus 'oos') degrees of freedom. In the vanilla Sharpe case, this is the number of future observations minus one. 
oosrescal 
the rescaling parameter for the future Sharpe ratio. The default value holds for the case of unattributed models ('vanilla Shape'), but can be set to some other value to deal with the magnitude of attribution factors in the future period. 
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 
level 
the confidence level required. 
level.lo 
the lower confidence level required. 
level.hi 
the upper confidence level required. 
Given n_0 observations xi from a normal random variable, with mean mu and standard deviation sigma, computes an interval [y_1,y_2] such that with a fixed probability, the sample Sharpe ratio over n future observations will fall in the given interval. The coverage is over repeated draws of both the past and future data, thus this computation takes into account error in both the estimate of Sharpe and the as yet unrealized returns.
A matrix (or vector) with columns giving lower and upper
confidence limits for the parameter. These will be labelled as
level.lo and level.hi in %, e.g. "2.5 %"
Steven E. Pav [email protected]
Sharpe, William F. "Mutual fund performance." Journal of business (1966): 119138. http://ideas.repec.org/a/ucp/jnlbus/v39y1965p119.html
Pav, Steven. "Inference on the Sharpe ratio via the upsilon distribution.' Arxiv (2015). http://arxiv.org/abs/1505.00829
Other sr: as.sr
, confint.sr
,
dsr
, is.sr
,
plambdap
, power.sr_test
,
print.sr
, reannualize
,
se
, sr_equality_test
,
sr_test
, sr_unpaired_test
,
sr_vcov
, sr
,
summary.sr
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21  # should reject null
etc < predint(rnorm(1000,mean=0.5,sd=0.1),oosdf=127,ope=1)
etc < predint(matrix(rnorm(1000*5,mean=0.05),ncol=5),oosdf=63,ope=1)
# check coverage
mu < 0.0005
sg < 0.013
n1 < 512
n2 < 256
p < 100
x1 < matrix(rnorm(n1*p,mean=mu,sd=sg),ncol=p)
x2 < matrix(rnorm(n2*p,mean=mu,sd=sg),ncol=p)
sr1 < as.sr(x1)
sr2 < as.sr(x2)
## Not run:
# takes too long to run ...
etc1 < predint(sr1,oosdf=n21,level=0.95)
is.ok < (etc1[,1] <= sr2$sr) & (sr2$sr <= etc1[,2])
covr < mean(is.ok)
## End(Not run)

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