I need to perform rolling VaR estimation of daily stock returns. At first I did the following:
library(PerformanceAnalytics)
data(edhec)
sample<-edhec[,1:5]
var605<-rollapply(as.zoo(sample),width=60,FUN=function(x) VaR(R=x,p=.95,method="modified",invert=T),by.column=TRUE,fill=NA)
It performs the computation and returns a zoo object but gives a series of warnings as follows:
VaR calculation produces unreliable result (inverse risk) for column: 1 : -0.00030977098532231
Then, I tried the same with sample of my data as follows:
library(foreign)
sample2 <- read.dta("sample2.dta")
sample2.xts <- xts(sample2[,-1],order.by=as.Date(sample2$datadate,format= "%Y-%m-%d"))
any(is.na(sample2.xts))
var605<-rollapply(as.zoo(sample2.xts),width=60,FUN=function(x) VaR(R=x,p=.95,method="modified",invert=T),by.column=TRUE,fill=NA)
But is does not return any zoo object and gives the following warnings and error:
VaR calculation produces unreliable result (inverse risk) for column: 1 : -0.0077322590200255
Error in if (eval(tmp < 0)) { : missing value where TRUE/FALSE needed
Called from: top level
From an earlier post (Using rollapply function for VaR calculation using R) I understand that rolling estimation cannot be performed if complete rolling window is missing, but in my data (sample2.dta) there are no missing values.
sample2.dta can be downloaded from https://drive.google.com/file/d/0B8usDJAPeV85WDdDQTFEbGQwaUU/edit?usp=sharing
Can anyone please help me to resolve and understand this issue?
The problem is that sometimes there is no variation in your data for the 60-period window.
R> no_var <- rollapply(sample2.xts, 60, sd, by.column=TRUE)
R> any(no_var==0)
[1] TRUE
R> head(no_var[-(1:60),])
001034 001038 001055 001066 001109
1984-03-26 -0.0003322471 -0.0001498238 0 -0.0111818465 0
1984-03-27 -0.0003322471 -0.0001498238 0 0.0002076288 0
1984-03-28 -0.0003322471 -0.0545102488 0 0.0092900768 0
1984-03-29 -0.0199407074 -0.0565552432 0 -0.0183491390 0
1984-03-30 0.0192762133 -0.0023488011 0 0.0000000000 0
1984-04-02 -0.0003322471 0.0000000000 0 0.0560894683 0
I've committed a patch to PerformanceAnalytics on R-Forge (r3525) to allow the NaN
to pass through the reaonableness check.
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