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#' @title Figure of Historical SImulation VaR and histogram of L/P
#'
#' @description Plots figure showing the historical simulation VaR and histogram
#' of L/P for specified confidence level and holding period implied by data
#' frequency.
#'
#' @param Ra Vector of profit loss data
#' @param cl ES confidence level
#'
#' @references Dowd, K. Measuring Market Risk, Wiley, 2007.
#'
#' @author Dinesh Acharya
#' @examples
#'
#' # Plots figure showing VaR and histogram of P/L data
#' Ra <- rnorm(100)
#' HSVaRFigure(Ra, .95)
#'
#' @export
HSVaRFigure<- function(Ra, cl){
# Determine if there are two arguments and ensure that arguments are read as intended
if (nargs() < 2) {
stop("Too few arguments")
}
if (nargs() > 2){
stop("Too many arguments")
}
if (nargs() == 2){
profit.loss <- as.vector(Ra)
n <- length(profit.loss)
}
# Check that inputs have correct dimensions
if (length(cl) != 1) {
stop("Confidence level must be a scalar")
}
if ( cl >= 1){
stop("Confidence level must be less than 1")
}
# VaR estimation
VaR <- HSVaR(profit.loss, cl) # HS VaR
# Histogram
n <- hist(profit.loss, main = "Historical Simulation VaR", col = 4,
xlab = "Loss(+) / Profit(-)", ylab = "Frequency")
v <- c(0, .625 * max(n$counts)) # Coordinates for VaR line
u <- VaR * matrix(1, length(v),1) # Coordinates for VaR line
lines(u, v, type = "l", col="blue")
cl.for.label <- 100 * cl
legend("topleft",c(paste('VaR at', cl.for.label, '% CL'),
paste('=', VaR)), bty="n", cex = 0.7)
}
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