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I'm going to separate your question in two. The key thing you're asking is that how does Return.rebalancing treat your different frequencied and number of asset return and weight objects. Data munging: It subsets the first ncol(weight) columns of R (as ncol(edhec) > ncol(weights) ncol R is now 11. Checks if the first date in R is less than the first date ...


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Both approaches can be useful. For stocks, sorting into quantiles is popular because it's easy to understand and explain it's a simple matter to build factor portfolios and track or backtest their performance, while the translation from expected returns to a portfolio is a bit more involved more robust than a single-stock regression, because it is less ...


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I found out that the upper time series is the result of a call > tail(Return.rebalancing(edhec,weights)) portfolio.returns 2009-03-31 0.005082048 2009-04-30 0.022982981 2009-05-31 0.037432398 2009-06-30 0.011107189 2009-07-31 0.025580507 2009-08-31 0.017983519 (by optical comparison. ;-) ) A glance ...


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There are a lot of ways of doing this and what a good way of doing this will be driven by your needs as well. Criteria such as whether the method needs to be (in)sensitive to outliers and whether or not your groups need to be of the same size will influence this. One way to do this would be sorting the volatilities and group them: in groups of equal size ...



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