# estimatePortStd

Estimate standard deviation of portfolio returns

## Syntax

``pstd = estimatePortStd(obj,pwgt)``

## Description

example

````pstd = estimatePortStd(obj,pwgt)` estimate standard deviation of portfolio returns for `PortfolioCVaR` or `PortfolioMAD` objects. For details on the workflows, see PortfolioCVaR Object Workflow and PortfolioMAD Object Workflow.```

## Examples

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Given a portfolio `pwgt`, use the `estimatePortStd` function to show the standard deviation of portfolio returns.

```m = [ 0.05; 0.1; 0.12; 0.18 ]; C = [ 0.0064 0.00408 0.00192 0; 0.00408 0.0289 0.0204 0.0119; 0.00192 0.0204 0.0576 0.0336; 0 0.0119 0.0336 0.1225 ]; m = m/12; C = C/12; rng(11); AssetScenarios = mvnrnd(m, C, 20000); p = PortfolioCVaR; p = setScenarios(p, AssetScenarios); p = setDefaultConstraints(p); p = setProbabilityLevel(p, 0.95); pwgt = estimateFrontierLimits(p); pstd = estimatePortStd(p, pwgt); disp(pstd)```
``` 0.0223 0.1010 ```

The function `rng`($seed$) resets the random number generator to produce the documented results. It is not necessary to reset the random number generator to simulate scenarios.

Given a portfolio `pwgt`, use the `estimatePortStd` function to show the standard deviation of portfolio returns.

```m = [ 0.05; 0.1; 0.12; 0.18 ]; C = [ 0.0064 0.00408 0.00192 0; 0.00408 0.0289 0.0204 0.0119; 0.00192 0.0204 0.0576 0.0336; 0 0.0119 0.0336 0.1225 ]; m = m/12; C = C/12; rng(11); AssetScenarios = mvnrnd(m, C, 20000); p = PortfolioMAD; p = setScenarios(p, AssetScenarios); p = setDefaultConstraints(p); pwgt = estimateFrontierLimits(p); pstd = estimatePortStd(p, pwgt); disp(pstd)```
``` 0.0222 0.1010 ```

The function `rng`($seed$) resets the random number generator to produce the documented results. It is not necessary to reset the random number generator to simulate scenarios.

## Input Arguments

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Object for portfolio, specified using a `PortfolioCVaR` or `PortfolioMAD`object.

For more information on creating a `PortfolioCVaR` or `PortfolioMAD` object, see

Data Types: `object`

Collection of portfolios, specified as a `NumAssets`-by-`NumPorts` matrix, where `NumAssets` is the number of assets in the universe and `NumPorts` is the number of portfolios in the collection of portfolios.

Data Types: `double`

## Output Arguments

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Estimates for standard deviations of portfolio returns for each portfolio in `pwgt`, returned as a `NumPorts` vector.

## Tips

You can also use dot notation to estimate the standard deviation of portfolio returns.

`pstd = obj.estimatePortStd(pwgt);`

## Version History

Introduced in R2012b