This is exactly what I want. However, with my function it produces a vector of
NAs ...
Gabor Grothendieck <ggrothendi...@gmail.com> schrieb am 16:23 Sonntag, 28.Mai
2017:
Maybe you want this.It computes VaRfun(r[c(i-500, i-1)] for each i for
which the argument to r makes sense.
rollapply(r, width = list(c(-500, -1)), FUN = VaRfun),
On Sat, May 27, 2017 at 5:29 PM, Sepp via R-help <r-help@r-project.org> wrote:
> Hello,
> I am fairly new to R and trying to calculate value at risk with exponentially
> decreasing weights.My function works for a single vector of returns but does
> not work with rollapply(), which is what I want to use. The function I am
> working on should assig exponentially decreasing weights to the K most recent
> returns and then order the returns in an ascending order. Subsequently it
> should pick the last return for which the cumulative sum of the weights is
> smaller or equal to a significance level. Thus, I am trying to construct a
> cumulative distribution function and find a quantile.
> This is the function I wrote:
> VaRfun <- function(x, lambda = 0.94) {
> #create data.frame and order returns such that the lates return is the first
> df <- data.frame(weight = c(1:length(x)), return = rev(x)) K <- nrow(df)
> constant <- (1-lambda)/(1-lambda^(K))#assign weights to the returns for(i
> in 1:nrow(df)) { df$weight[i] <- lambda^(i-1) * constant }#order
> returns in an ascending order df <- df[order(df$return),]
> #add the cumulative sum of the weights df$cum.weight <- cumsum(df$weight)
> #calculate value at risk VaR <- -tail((df$return[df$cum.weight <= .05]), 1)
> signif(VaR, digits = 3)}
> It works for a single vector of returns but if I try to use it with
> rollapply(), such as
> rollapply(r, width = list(-500, -1), FUN = VaRfun),
> it outputs a vector of NAs and I don't know why.
> Thank you for your help!
> [[alternative HTML version deleted]]
>
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