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]]
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 at 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]] > > ______________________________________________ > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code.-- Statistics & Software Consulting GKX Group, GKX Associates Inc. tel: 1-877-GKX-GROUP email: ggrothendieck at gmail.com
This is exactly what I want. However, with my function it produces a vector of NAs ... Gabor Grothendieck <ggrothendieck at 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 at 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]] > > ______________________________________________ > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code.-- Statistics & Software Consulting GKX Group, GKX Associates Inc. tel: 1-877-GKX-GROUP email: ggrothendieck at gmail.com