Description Usage Arguments Details Value Author(s) References See Also Examples
Computes approximate confidence intervals on the (optimal) Signal-Noise ratio
given the (optimal) Sharpe ratio.
Works on objects of class sr
and sropt
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | ## S3 method for class 'sr'
confint(
object,
parm,
level = 0.95,
level.lo = (1 - level)/2,
level.hi = 1 - level.lo,
type = c("exact", "t", "Z", "Mertens", "Bao"),
...
)
## S3 method for class 'sropt'
confint(
object,
parm,
level = 0.95,
level.lo = (1 - level)/2,
level.hi = 1 - level.lo,
...
)
## S3 method for class 'del_sropt'
confint(
object,
parm,
level = 0.95,
level.lo = (1 - level)/2,
level.hi = 1 - level.lo,
...
)
|
object |
an observed Sharpe ratio statistic, of class |
parm |
ignored here, but required for the general method. |
level |
the confidence level required. |
level.lo |
the lower confidence level required. |
level.hi |
the upper confidence level required. |
type |
which method to apply. |
... |
further arguments to be passed to or from methods. |
Constructs confidence intervals on the Signal-Noise ratio given observed Sharpe ratio statistic. The available methods are:
The default, which is only exact when returns are normal, based on inverting the non-central t distribution.
Uses the Johnson Welch approximation to the standard error, centered around the sample value.
Uses the Johnson Welch approximation to the standard error, performing a simple correction for the bias of the Sharpe ratio based on Miller and Gehr formula.
Uses the Mertens higher order approximation to the standard error, centered around the sample value.
Uses the Bao higher order approximation to the standard error, performing a higher order correction for the bias of the Sharpe ratio.
Suppose xi are n independent draws of a q-variate normal random variable with mean mu and covariance matrix Sigma. Let xbar be the (vector) sample mean, and S be the sample covariance matrix (using Bessel's correction). Let
z* = sqrt(xbar' S^-1 xbar)
Given observations of z*, compute confidence intervals on the population analogue, defined as
zeta* = sqrt(mu' Sigma^-1 mu)
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 shabbychef@gmail.com
Sharpe, William F. "Mutual fund performance." Journal of business (1966): 119-138. https://ideas.repec.org/a/ucp/jnlbus/v39y1965p119.html
Other sr:
as.sr()
,
dsr()
,
is.sr()
,
plambdap()
,
power.sr_test()
,
predint()
,
print.sr()
,
reannualize()
,
se()
,
sr_equality_test()
,
sr_test()
,
sr_unpaired_test()
,
sr_vcov()
,
sr
,
summary.sr
Other sropt:
as.sropt()
,
dsropt()
,
is.sropt()
,
pco_sropt()
,
power.sropt_test()
,
reannualize()
,
sropt_test()
,
sropt
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | # using "sr" class:
ope <- 253
df <- ope * 6
xv <- rnorm(df, 1 / sqrt(ope))
mysr <- as.sr(xv,ope=ope)
confint(mysr,level=0.90)
# using "lm" class
yv <- xv + rnorm(length(xv))
amod <- lm(yv ~ xv)
mysr <- as.sr(amod,ope=ope)
confint(mysr,level.lo=0.05,level.hi=1.0)
# rolling your own.
ope <- 253
df <- ope * 6
zeta <- 1.0
rvs <- rsr(128, df, zeta, ope)
roll.own <- sr(sr=rvs,df=df,c0=0,ope=ope)
aci <- confint(roll.own,level=0.95)
coverage <- 1 - mean((zeta < aci[,1]) | (aci[,2] < zeta))
# using "sropt" class
ope <- 253
df1 <- 4
df2 <- ope * 3
rvs <- as.matrix(rnorm(df1*df2),ncol=df1)
sro <- as.sropt(rvs,ope=ope)
aci <- confint(sro)
# on sropt, rolling your own.
zeta.s <- 1.0
rvs <- rsropt(128, df1, df2, zeta.s, ope)
roll.own <- sropt(z.s=rvs,df1,df2,drag=0,ope=ope)
aci <- confint(roll.own,level=0.95)
coverage <- 1 - mean((zeta.s < aci[,1]) | (aci[,2] < zeta.s))
# using "del_sropt" class
nfac <- 5
nyr <- 10
ope <- 253
set.seed(as.integer(charToRaw("be determinstic")))
Returns <- matrix(rnorm(ope*nyr*nfac,mean=0,sd=0.0125),ncol=nfac)
# hedge out the first one:
G <- matrix(diag(nfac)[1,],nrow=1)
asro <- as.del_sropt(Returns,G,drag=0,ope=ope)
aci <- confint(asro,level=0.95)
# under the alternative
Returns <- matrix(rnorm(ope*nyr*nfac,mean=0.001,sd=0.0125),ncol=nfac)
asro <- as.del_sropt(Returns,G,drag=0,ope=ope)
aci <- confint(asro,level=0.95)
|
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