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I think you are having it backwards: Optimising your lookback period is a sure recipe for disaster because it introduces data snooping bias. To develop a robust trading strategy you have to check whether it is sufficiently stable with different lookback periods (e.g. in a certain range). If results differ significantly that is a good sign that your system ...


@vanguard2k and @Theja provide useful information. In my experience, unequal starting points is most common, so I'll try to focus on that. The technique that @vanguard2k mentioned for unequal starting points can be thought of like a regression. You start with the longest available data and get the covariance matrix of that. For the next set of available ...


One really nice book that comes to my mind is Little, Rubin, Statistical Analysis with Missing Data I read part of it but probably it is too much information in your case. For your application, i think you can categorize the problem into two possible subproblems: First, time series that have unequal starting points (when some stocks' history is ...


A simpler question would be the following: suppose you want to find the covaraince between the returns of two stocks and each of their time series has missing values at different places. What is the best way to compute covariance here? One very sensible way to approach this is to throw away the observations where ony one of the stocks has a return value. Of ...


One could use a GARCH of his choice to estimate the volatility. A mean over your period would be a good indicator, otherwise the instant conditional sd is as good as it gets. Another way could be via an exponential smoothing of the risk-metrics type. Your question is not so clear is to be honest.


You can use the zoo package: library(zoo) W <- matrix(rep(0.03225806,66),nrow=11) T <- seq(as.Date("2001-12-01"), as.Date("2011-12-01 "), "years") M <- read.zoo(data.frame(T,W)) colnames(M) <- c(LETTERS[1:6]) plot.zoo(M) plot.zoo(M,plot.type = "single")

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