## Documentation Center |

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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