The goal of `ufRisk`

is to enable the user to compute one-step ahead
forecasts of Value at Risk (VaR) and Expected Shortfall (ES) by means of
various parametric and semiparametric GARCH-type models. For the latter
the estimation of the nonparametric scale function is carried out by
means of a data-driven smoothing approach. Currently the GARCH, the
exponential GARCH (EGARCH), the Log-GARCH, the asymmetric power ARCH
(APARCH), the FIGARCH and FI-Log-GARCH can be employed within the scope
of `ufRisk`

. Model quality, in terms of forecasting VaR and ES, can be
assessed by means of various backtesting methods.

You can install the released version of ufRisk from CRAN with:

```
install.packages("ufRisk")
```

The data set `ESTX`

, which is implemented in the `ufRisk`

package,
contains daily financial data of the EURO STOXX 50 (ESTX) index from
April 2007 to December 2021. In the following an example of the
(out-of-sample) one-step forecasts of the
97.5-VaR
(red line) and the corresponding ES (green line) as well as the
99-VaR
(green line), which are obtained by employing a FIGARCH model to the
`ESTX`

return series, are illustrated. Exceedances are indicated by the
colored circles.

```
# Applying the FIGARCH model to the ESTX return series
x = ESTX$price.close
results = varcast(x, model = 'fiGARCH', a.v = 0.99, a.e = 0.975, n.out = 250)
```

**Visualize your results by means of the implemented plot method**

Plotting the out-of-sample loss series:

```
plot(results, plot.option = 1)
```

Plotting the out-of-sample loss series, VaR.v & breaches:

```
plot(results, plot.option = 2)
```

Plotting the out-of-sample loss series, VaR.e, ES & breaches:

```
plot(results, plot.option = 3)
```

Assess the quality of your model by employing various backtesting
methods by means of the functions `trafftest`

, `covtest`

and `lossfunc`

.

**Conduct a traffic light test for VaR and ES**

```
trafftest(results)
#>
#> ###################################
#> # Backtesting results #
#> ###################################
#>
#> # Traffic light zone boundaries #
#> Zone Probability
#> Green zone: p < 95%
#> Yellow zone: 95% <= p < 99.99%
#> Red zone: p >= 99.99%
#>
#> # Test 1: 99%-VaR #
#> Number of violations: 4
#> p = 0.8922: Green zone
#>
#> # Test 2: 97.5%-VaR #
#> Number of violations: 9
#> p = 0.9005: Green zone
#>
#> # Test 3: 97.5%-ES #
#> Number of weighted violations: 5.1227
#> p = 0.9188: Green zone
#>
#> # Weighted Absolute Deviation #
#> WAD = 1.6793
```

**Conduct a conditional and unconditional coverage test as well as an
independence test**

```
covtest(results, conflvl = 0.95)
#>
#> ##################################
#> # Test results #
#> ##################################
#>
#> # Unconditional coverage test #
#>
#> H0: w = 0.99
#> p_[uc] = 0.3805
#> Decision: Fail to reject H0
#>
#> # Independence test #
#>
#> H0: w_[00] = w_[10]
#> p_[ind] = 0.6865
#> Decision: Fail to reject H0
#>
#> # Conditional coverage test #
#>
#> H0: w_[00] = w_[10] = 0.99
#> p_[cc] = 0.6275
#> Decision: Fail to reject H0
#>
```

**Calculate different loss functions**

```
lossfunc(results)
#> Please note that the following results are multiplied with 10000.
#>
#> $lossfunc1
#> [1] 7.693316
#>
#> $lossfunc2
#> [1] 14.31244
#>
#> $lossfunc3
#> [1] 14.56085
#>
#> $lossfunc4
#> [1] 13.61529
```

In `ufRisk`

four functions are available.

**Original functions since version 1.0.0:**

`covtest`

: Coverage tests`lossfunc`

: Calculation of loss functions`trafftest`

: Traffic light tests for VaR and ES`varcast`

: One-step ahead forecasting of VaR and ES

For further information on each of the functions, we refer the user to the manual or the package documentation.

`ESTX`

: Daily financial time series data of the EURO STOXX 50 Index (ESTX) from April 2007 to December 2021`WMT`

: Daily financial time series data of Walmart Inc. (WMT) from January 2000 to December 2021

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