rollcast  R Documentation 
Computes rolling onestep ahead forecasts of Value at Risk and Expected Shortfall (Conditional Value at Risk) by means of plain historical simulation age and volatilityweighted historical simulation as well as filtered historical simulation.
rollcast( x, p = 0.975, model = c("EWMA", "GARCH"), method = c("plain", "age", "vwhs", "fhs"), lambda = c(0.94, 0.98), nout = NULL, nwin = NULL, nboot = NULL, smoothscale = c("none", "lpr", "auto"), smoothopts = list(), ... )
x 
a numeric vector of asset returns 
p 
confidence level for VaR calculation; default is 
model 
model for estimating conditional volatility; options are 
method 
method to be used for calculation; default is 
lambda 
decay factor for the calculation of weights; default is 
nout 
number of outofsample observations; most recent observations are used;
default is 
nwin 
window size for rolling onestep forecasting; most recent observations
before outofsample are used; default is 
nboot 
size of bootstrap sample; must be a single nonNA integer value
with 
smoothscale 
a character object; defines the smoothing approach
for the unconditional variance from the logarithm of the squared centralized
returns; for 
smoothopts 
additional arguments of 
... 
additional arguments of the

Returns a list with the following elements:
Numerical vector containing outofsample forecasts of Value at Risk
Numerical vector containing outofsample forecasts of Expected Shortfall (Conditional Value at Risk)
Numerical vector containing outofsample returns
Confidence level for VaR calculation
Model for estimating conditional volatility
Method to be used for calculation
Number of outofsample observations
Window size for rolling onestep forecasting
Size of bootstrap sample
prices < DAX$price.close returns < diff(log(prices)) n < length(returns) nout < 250 # number of obs. for outofsample forecasting nwin < 500 # window size for rolling forecasts ### Example 1  plain historical simulation results1 < rollcast(x = returns, p = 0.975, method = 'plain', nout = nout, nwin = nwin) matplot(1:nout, cbind(results1$xout, results1$VaR, results1$ES), type = 'hll', xlab = 'number of outofsample obs.', ylab = 'losses, VaR and ES', main = 'Plain HS  97.5% VaR and ES for the DAX30 return series') ### Example 2  age weighted historical simulation results2 < rollcast(x = returns, p = 0.975, method = 'age', nout = nout, nwin = nwin) matplot(1:nout, cbind(results2$xout, results2$VaR, results2$ES), type = 'hll', xlab = 'number of outofsample obs.', ylab = 'losses, VaR and ES', main = 'Age weighted HS  97.5% VaR and ES for the DAX30 return series') ### Example 3  volatility weighted historical simulation  EWMA results3 < rollcast(x = returns, p = 0.975, model = 'EWMA', method = 'vwhs', nout = nout, nwin = nwin) matplot(1:nout, cbind(results3$xout, results3$VaR, results3$ES), type = 'hll', xlab = 'number of outofsample obs.', ylab = 'losses, VaR and ES', main = 'Vol. weighted HS (EWMA)  97.5% VaR and ES for the DAX30 return series') ### Example 4  volatility weighted historical simulation  GARCH results4 < rollcast(x = returns, p = 0.975, model = 'GARCH', method = 'vwhs', nout = nout, nwin = nwin) matplot(1:nout, cbind(results4$xout, results4$VaR, results4$ES), type = 'hll', xlab = 'number of outofsample obs.', ylab = 'losses, VaR and ES', main = 'Vol. weighted HS (GARCH)  97.5% VaR and ES for the DAX30 return series') ### Example 5  filtered historical simulation  EWMA results5 < rollcast(x = returns, p = 0.975, model = 'EWMA', method = 'fhs', nout = nout, nwin = nwin, nboot = 10000) matplot(1:nout, cbind(results5$xout, results5$VaR, results5$ES), type = 'hll', xlab = 'number of outofsample obs.', ylab = 'losses, VaR and ES', main = 'Filtered HS (EWMA)  97.5% VaR and ES for the DAX30 return series') ### Example 6  filtered historical simulation  GARCH results6 < rollcast(x = returns, p = 0.975, model = 'GARCH', method = 'fhs', nout = nout, nwin = nwin, nboot = 10000) matplot(1:nout, cbind(results6$xout, results6$VaR, results6$ES), type = 'hll', xlab = 'number of outofsample obs.', ylab = 'losses, VaR and ES', main = 'Filtered HS (GARCH)  97.5% VaR and ES for the DAX30 return series')
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