Variance

y = var(X) y = var(X, 1) y = var(X, W) y = var(X, W, DIM)

| Financial times series object. |

| Weight vector used in calculating variance. |

| Dimension of |

`var`

supports financial time series objects
based on the MATLAB^{®} `var`

function.
See `var`

in the MATLAB documentation.

`y = var(X)`

, if `X`

is a
financial time series object and returns the variance of each series.

`var`

normalizes `y`

by `N`

– `1`

if `N`

> `1`

,
where `N`

is the sample size. This is an unbiased
estimator of the variance of the population from which `X`

is
drawn, as long as `X`

consists of independent, identically
distributed samples. For `N`

= `1`

, `y`

is
normalized by `N`

.

`y = var(X, 1)`

normalizes by `N`

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

is the same as `var(X)`

.

`y = var(X, W)`

computes the variance using
the weight vector `W`

. The length of `W`

must
equal the length of the dimension over which `var`

operates,
and its elements must be nonnegative. `var`

normalizes `W`

to
sum to `1`

. Use a value of `0`

for `W`

to
use the default normalization by `N`

– `1`

,
or use a value of `1`

to use `N`

.

`y = var(X, W, DIM)`

takes the variance along
the dimension `DIM`

of `X`

.

The variance is the square of the standard deviation. Consider if

f = fints((today:today+1)', [4 -2 1; 9 5 7])

then

var(f, 0, 1)

is

[12.5 24.5 18.0]

and

var(f, 0, 2)

is

[9.0; 4.0]

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