Description Usage Arguments Details Value Author(s) References See Also Examples

Computes the variance covariance matrix of sample Sharpe ratios.

1 |

`X` |
an |

`vcov.func` |
a function which takes an object of class |

`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 |

Given *n* contemporaneous observations of *p* returns
streams, this function estimates the asymptotic variance
covariance matrix of the vector of sample Sharpes,
*[zeta1,zeta2,...,zetap]*

One may use the default method for computing covariance,
via the `vcov`

function, or via a 'fancy' estimator,
like `sandwich:vcovHAC`

, `sandwich:vcovHC`

, *etc.*

This code first estimates the covariance of the *2p* vector of
the vector *x* stacked on its Hadamard square, *x^2*. This is
then translated back to a variance covariance on the vector of
sample Sharpe ratios via the Delta method.

a list containing the following components:

`SR` |
a vector of (annualized) Sharpe ratios. |

`Ohat` |
a |

`p` |
the number of assets. |

Steven E. Pav [email protected]

Sharpe, William F. "Mutual fund performance." Journal of business (1966): 119-138. http://ideas.repec.org/a/ucp/jnlbus/v39y1965p119.html

Lo, Andrew W. "The statistics of Sharpe ratios." Financial Analysts Journal 58, no. 4 (2002): 36-52. http://ssrn.com/paper=377260

sr-distribution functions, `dsr`

Other sr: `as.sr`

, `confint.sr`

,
`dsr`

, `is.sr`

,
`plambdap`

, `power.sr_test`

,
`predint`

, `print.sr`

,
`reannualize`

, `se`

,
`sr_equality_test`

, `sr_test`

,
`sr_unpaired_test`

, `sr`

,
`summary`

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 | ```
X <- matrix(rnorm(1000*3),ncol=3)
colnames(X) <- c("ABC","XYZ","WORM")
Sigmas <- sr_vcov(X)
# make it fat tailed:
X <- matrix(rt(1000*3,df=5),ncol=3)
Sigmas <- sr_vcov(X)
## Not run:
if (require(sandwich)) {
Sigmas <- sr_vcov(X,vcov.func=vcovHC)
}
## End(Not run)
# add some autocorrelation to X
Xf <- filter(X,c(0.2),"recursive")
colnames(Xf) <- colnames(X)
Sigmas <- sr_vcov(Xf)
## Not run:
if (require(sandwich)) {
Sigmas <- sr_vcov(Xf,vcov.func=vcovHAC)
}
## End(Not run)
# should run for a vector as well
X <- rnorm(1000)
SS <- sr_vcov(X)
``` |

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