# cov

Covariance matrix for financial time series object

`cov`

is not recommended. Use `timetable`

instead. For more information, see Convert Financial Time Series Objects fints to Timetables.

## Description

`cov(`

returns a variance or covariance
matrix. `X`

)

If `X`

is a financial time series object with one series,
`cov(X)`

returns the variance. For a financial time series object
containing multiple series, where each row is an observation, and each series a
variable, `cov(X)`

is the covariance matrix.

`diag(cov(X))`

is a vector of variances for each series and
`sqrt(diag(cov(X)))`

is a vector of standard deviations.

`cov(X)`

normalizes by (`N`

-`1`

) if `N`

> `1`

, where
`N`

is the number of observations. This makes
`cov(X)`

the best unbiased estimate of the covariance matrix if
the observations are from a normal distribution. For `N`

=
`1`

, `cov`

normalizes by
`N`

.

`cov`

for financial time series objects is based on the
MATLAB^{®}
`cov`

function. See `cov`

.

`cov(`

normalizes by
`X`

,1)`N`

and produces the second moment matrix of the observations
about their mean. `cov(X, Y, 0)`

is the same as ```
cov(X,
Y)
```

and `cov(X, 0)`

is the same as
`cov(X)`

. The mean is removed from each column before
calculating the result.

`cov(`

normalizes
by `X`

,`Y`

)`N`

and produces the second moment of the sample about its mean.
`var(X, 0)`

is the same as `var(X)`

.

`cov(X,Y)`

normalizes by (`N`

-`1`

) if `N`

> `1`

, where
`N`

is the number of observations. This makes
`cov(X,Y)`

the best unbiased estimate of the covariance matrix
if the observations are from a normal distribution. For `N`

=
`1`

, `cov`

normalizes by `N`

.
`cov(X, Y)`

, where `X`

and
`Y`

are financial time series objects with the same number of
elements, is equivalent to `cov([X(:) Y(:)])`

.

## Examples

## Input Arguments

## Version History

**Introduced before R2006a**