VaR {BEKKs} | R Documentation |

## Calculating Value-at-Risk (VaR)

### Description

Method for calculating VaR from estimated covariance processes (bekk_fit) or predicted covariances (predict).

### Usage

```
VaR(x, p = 0.99, portfolio_weights = NULL, distribution = "empirical")
```

### Arguments

`x` |
An object of class "bekkFit" from the function bekk_fit or an object of class "bekkForecast" from the function predict. |

`p` |
A numerical value that determines the confidence level. The default value is set at 0.99 in accordance with the Basel Regulation. |

`portfolio_weights` |
A vector determining the portfolio weights to calculate the portfolio VaR. If set to "NULL", the univariate VaR for each series are calculated. |

`distribution` |
A character string determining the assumed distribution of the residuals. Implemented are "normal", "empirical" and "t". The default is using the empirical distribution of the residuals. |

### Value

Returns a S3 class "var" object containing the VaR forecast and respective confidence bands.

### Examples

```
data(StocksBonds)
obj_spec <- bekk_spec()
x1 <- bekk_fit(obj_spec, StocksBonds, QML_t_ratios = FALSE, max_iter = 50, crit = 1e-9)
# single VaRs of series
x2 <- VaR(x1, distribution="normal")
plot(x2)
# VaR of equally-weighted portfolio
portfolio_weights <- c(0.5, 0.5)
x3 <- VaR(x1, portfolio_weights = portfolio_weights)
plot(x3)
# VaR of traditional 30/70 weighted bond and stock portfolio
portfolio_weights <- c(0.3, 0.7)
x4 <- VaR(x1, portfolio_weights = portfolio_weights)
plot(x4)
```

*BEKKs*version 1.4.4 Index]