r,quantitative-finance,performanceanalytics

The subsetting of the data should be done prior to passing to the charts.RollingRegression function. The mighty xts provides this functionality: charts.RollingRegression(Ret["2009-04::",], Rb["2009-04::",2, drop = FALSE], Rf = 0.001, na.pad = TRUE) You can read more about how to subset with xts by looking at the help page in R...

There isn't a Drawdowns function. Depending on what you're doing, you may be interested in using either findDrawdowns or chart.Drawdown. Also you can simplify your code a bit for calculating returns as shown below: require(quantmod) require(PerformanceAnalytics) getSymbols("AAPL") # calculate returns based on Adjusted Close prices AAPL.DF <- Return.calculate(AAPL[,6])[-1,] dailyDD <-...

r,xts,quantmod,performanceanalytics

This does not directly answer your question (see the comment), but wouldn't this be easier: library(quantmod) ticker_symbol <- c("zzzz","IVW","JKE","QQQ","SPYG","VUG" ) sDate <- as.Date("2007-09-04") #Start Date eDate <- as.Date("2014-09-02") #End Date get.symbol <- function(ticker) { tryCatch(temp <- Ad(getSymbols(ticker, auto.assign=FALSE, from = sDate, to = eDate, warning = FALSE)), error = function(e)...

r,finance,performanceanalytics

The 'historical' method is not a 'simulation' method. It is a measure of the realized historical loss quantile. I have added historical contribution to PerformanceAnaltytics in v 1.4.3574 on R-Forge. Your example now produces: > VaR(edhec, p=.95, method="historical", portfolio_method="component") no weights passed in, assuming equal weighted portfolio $hVaR hVaR 95%...