Functions to compute Value-at-Risk (VaR), Conditional Value-at-Risk (CVaR) and Expected Loss (EL) at data from scale-shape families.

1 2 3 4 5 |

`data` |
data at which to compute the risk measure. |

`model` |
an object of class |

`level` |
real: probability needed for VaR and CVaR. |

`N0` |
real: expected frequency for expected loss. |

`rob` |
logical; if |

`x` |
an object of (S3-)class |

`...` |
further arguments for |

The risk measures `getVaR`

, `getCVaR`

, `getEL`

return
an (S3) object of class `"riskMeasure"`

, i.e., a numeric vector
of length 2 with components `"Risk"`

and `"varofRisk"`

containing the respective risk measure
and a corresponding (asymptotic) standard error for the risk
measure. To the return class `"riskMeasure"`

,
there is a particular `print`

-method; if the corresponding argument
`level`

is `NULL`

(default) the corresponding standard error
is printed together with the risk measure; otherwise a corresponding
CLT-based confidence interval for the risk meausre is produced.

Peter Ruckdeschel peter.ruckdeschel@uni-oldenburg.de

P. Ruckdeschel, N. Horbenko (2013): Optimally-Robust Estimators in Generalized Pareto Models. Statistics 47(4), 762–791. N. Horbenko, P. Ruckdeschel, T. Bae (2011): Robust Estimation of Operational Risk. Journal of Operational Risk 6(2), 3–30.

`GParetoFamily`

, `GEVFamily`

, `WeibullFamily`

, `GammaFamily`

1 2 3 4 5 6 7 8 9 10 11 12 | ```
set.seed(123)
GPD <- GParetoFamily(loc=20480, scale=7e4, shape=0.3)
data <- r(GPD)(500)
getCVaR(data,GPD,0.99)
## Not run: # to reduce checking time
getVaR(data,GPD,0.99)
getEL(data,GPD,5)
getVaR(data,GPD,0.99, rob=FALSE)
getEL(data,GPD,5, rob=FALSE)
getCVaR(data,GPD,0.99, rob=FALSE)
## End(Not run)
``` |

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