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@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 ...


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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 ...


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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 ...


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Most portfolios offer positive returns, and minimum variance portfolios are not exceptions to this rule. But by offering "minimum variance," they also offer the lowest possibility of a negative deviation large enough to pull the actual return (expected return minus deviation), into negative territory.


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It depends on the exact nature of the risk in question as well as the mandate of the options desk at the bank. Generally such products are "created" and hedged at exotic option sell-side desks. There are a myriad of different kinds of risk the bank and hence the insurance company may offer their clients insurance against. It could range from inflation risk, ...


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Backtesting on a past realization does not provide any meaningful "estimate", as the variance of the "estimate" would be undefined. More meaningful would be to make distributional assumptions and get estimates through extensive Monte Carlo simulations. Clearly, the estimates that you get would be "meaningful" under your specific distributional assumption, ...


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It's really important to vectorize operations as much as possible when working with big data in R when speed is a consideration. The code below is an example of multiple regression performed on a matrix with 1000 rows and 10000 columns with the independent variables of interest in each column. The same 5 covariates are also controlled for in every model. It ...


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Your need to also specify the frequency at which you are trading. For long to medium u can also consider ITG For Day trading you can consider Flextrade, Portware If u are solo then interactive brokers. If you are adventurous then quantstrat


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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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