Estimation of rolling Value at Risk (VaR) using R

Question

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?

Answer 1

1) We can reproduce the warning using only VaR as follows:

> VaR(R = edhec[seq(25, length=60), 5], p = .95, method = "modified", invert = TRUE)
VaR calculation produces unreliable result (inverse risk) for column: 1 : -0.000203691774704274
    Equity Market Neutral
VaR                    NA

Try using a different method=.

> VaR(R = edhec[seq(25, length=60), 5], p = .95, method = "gaussian", invert = TRUE)
    Equity Market Neutral
VaR          -0.001499347

2) With "gaussian" I still got warnings on the real data set but no errors. Try experimenting with the other "method" argument values that are available as well. See ?VaR .

3) Note that by.column = TRUE can be omitted as it is the default.

Answer 2

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.