The goal of `quarks`

is to enable the user to compute Value at Risk
(VaR) and Expected Shortfall (ES) by means of various types of
historical simulation. Currently plain historical simulation as well as
age-, volatility-weighted- and filtered historical simulation are
implemented in `quarks`

. Volatility weighting can be carried out via an
exponentially weighted moving average (EWMA) model or other GARCH-type
models.

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

```
install.packages("quarks")
```

The data set `DAX30`

, which is implemented in the `quarks`

package,
contains daily financial data of the German stock index DAX from January
2000 to December 2020 (currency in EUR). In the following examples of
the (out-of-sample) one-step forecasts of the 97.5%-VaR (red line) and
the corresponding ES (green line) are illustrated. Exceedances are
indicated by the colored circles.

```
library(quarks) # Call the package
```

```
# Calculating the returns
prices <- DAX30$price.close
returns <- diff(log(prices))
### Example 1 - plain historical simulation
results1 <- rollcast(x = returns, p = 0.975, method = 'plain', nout = 250,
nwin = 500)
plot(results1)
```

```
### Example 2 - age weighted historical simulation
results2 <- rollcast(x = returns, p = 0.975, method = 'age', nout = 250,
nwin = 500)
plot(results2)
```

```
### Example 3 - volatility weighted historical simulation - EWMA
results3 <- rollcast(x = returns, p = 0.975, model = 'EWMA',
method = 'vwhs', nout = 250, nwin = 500)
plot(results3)
```

```
### Example 4 - volatility weighted historical simulation - GARCH
results4 <- rollcast(x = returns, p = 0.975, model = 'GARCH',
method = 'vwhs', nout = 250, nwin = 500)
plot(results4)
```

```
### Example 5 - filtered historical simulation - EWMA
results5 <- rollcast(x = returns, p = 0.975, model = 'EWMA',
method = 'fhs', nout = 250, nwin = 500, nboot = 10000)
plot(results5)
```

```
### Example 6 - filtered historical simulation - GARCH
results6 <- rollcast(x = returns, p = 0.975, model = 'GARCH',
method = 'fhs', nout = 250, nwin = 500, nboot = 10000)
plot(results6)
```

To further analyze these results one might apply e.g. the traffic light test to assess the performance of these methods.

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