First let's import the data into R. I'm using a small function, to import .csv which returns an .xts object named ahl.
#Monthly NAV MAN AHL
loadahl<-function(){
a=read.table("ahl_trend.csv",sep = ",",dec = ",")
a$date = paste(substr(a$V1,1,2),substr(a$V1,4,5),substr(a$V1,7,10),sep="-")
ahl=a$date
ahl=cbind(ahl,substr(a$V2,1,5))
a=as.POSIXct(ahl[,1],format="%d-%m-%Y")
ahl=as.xts(as.numeric(ahl[,2]),order.by=a)
rm(a)
return(ahl)
}
next we would like to have the monthly returns
monthlyReturn(x, subset=NULL, type='arithmetic', leading=TRUE, ...)
which we store in retahl.
retahl=monthlyReturn(ahl,type="log")
Next, I usually plot the chart.Drawdown to get a visual idea, if the product is designed for my risk appetite.
chart.Drawdown(retahl)
table.AnnualizedReturns(retahl)
monthly.returns
Annualized Return 0.0212
Annualized Std Dev 0.1246
Annualized Sharpe (Rf=0%) 0.1702
table.DownsideRisk(retahl)
monthly.returns
Semi Deviation 0.0254
Gain Deviation 0.0222
Loss Deviation 0.0222
Downside Deviation (MAR=10%) 0.0289
Downside Deviation (Rf=0%) 0.0241
Downside Deviation (0%) 0.0241
Maximum Drawdown 0.2478
Historical VaR (95%) -0.0521
Historical ES (95%) -0.0748
Modified VaR (95%) -0.0573
Modified ES (95%) -0.0730
No comments :
Post a Comment