# Documentation

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# var

Variance

## Syntax

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

## Arguments

 `X` Financial times series object. `W` Weight vector used in calculating variance. `DIM` Dimension of `X` used in calculating variance.

## Description

`var` supports financial time series objects based on the MATLAB® `var` function. See `var`.

`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`.

## Examples

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]`