I keep getting this error: ES calculation produces unreliable result (inverse risk) for column: 1 message when using DEoptim. Maybe I am overlooking something so I need some help figuring this out. I have searched across the web but cant seem to find the answer.
I have a xts object called RETS containing 127 rows and 4 columns which have log returns:
library("quantmod")
library("PerformanceAnalytics")
library("DEoptim")
e <- new.env()
getSymbols("SPY;QCOR;CLNT;SRNE", from="2007-06-30", to="2007-12-31", env=e)
# combine the adjusted close values in one xts object
dataset1 <- do.call(merge, eapply(e, Ad))
# calculate returns
RETS <- na.omit(CalculateReturns(dataset1, method="log"))
# objective function
optRR.gt3 <- function(x, ret) {
retu <- ret %*% x
obj <- -CVaR(as.ts(-retu))/CVaR(as.ts(retu))
obj <- ifelse(obj>0,-obj,obj)
weight.penalty <- 100*(1-sum(x))^2
small.weight.penalty <- 100*sum(x[x<0.03])
return(obj + weight.penalty + small.weight.penalty)
}
# I am Trying to optimize the function: optRR.gt3, which minimizes CVaR
ctrl <- list(itermax=250, F=0.2, CR=0.8)
set.seed(21)
res <- DEoptim(optRR.gt3, lower=rep(0,ncol(RETS)), upper=rep(1,ncol(RETS)), control=ctrl, ret=RETS)
#ES calculation produces unreliable result (risk over 100%) for column: 1 : 3.01340769101382
#ES calculation produces unreliable result (inverse risk) for column: 1 : -0.239785868862194
#ES calculation produces unreliable result (inverse risk) for column: 1 : -0.11639331543788
#ES calculation produces unreliable result (risk over 100%) for column: 1 : 1.06315102355445
#ES calculation produces unreliable result (risk over 100%) for column: 1 : 1.05285415441624
#ES calculation produces unreliable result (risk over 100%) for column: 1 : 2.19356415811659
#ES calculation produces unreliable result (inverse risk) for column: 1 : -0.0384963731133424
#Error in DEoptim(optRR.gt3, lower = rep(0, ncol(RETS)), upper = rep(1, :
# NaN value of objective function!
#Perhaps adjust the bounds.
I have run this code with other series of log returns and it works, but I sometimes run it against a series and get the error such as this one.
