quarks

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 and volatility-weighted historical simulation is implemented in quarks. Volatility weighting is carried out via an exponentially weighted moving average (EWMA). In future versions of quarks volatility filtering by means of GARCH-type models will be considered.

Installation

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

install.packages("quarks")

Example 1

This is a basic example which shows you how to solve a common problem. The data DAX30 in this package contains daily financial data of the DAX from 2000 to December 2020 (currency in EUR). In the following examples the (out-of-sample) one-step forecasts of the 99%-VaR (red line) and the corresponding ES (green line) are computed. 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 = 0.99, method = 'plain', nout = 250,
                     nwin = 250)
plot(results1)

### Example 2 - age weighted historical simulation 
results2 <- rollcast(x = returns, p = 0.99, method = 'age', nout = 250,
                     nwin = 250)
plot(results2)

### Example 3 - volatility weighted historical simulation 
results3 <- rollcast(x = returns, p = 0.99, method = 'vwhs', nout = 250,
                     nwin = 250)
plot(results3)

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