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I am not sure what you are exactly asking. But usually even a simple Garch(1,1) would be the naive approach of forecasting variance using last period's variance. A very good survey of volatility modelling on the Arch/garch family is the Hansen and Lunde 2005. They show that hardly one can beat a garch(1,1), so that is a good first guess.


There is one minor mistake: If you compute sum(mean.var) you'll obtain $-1$ instead of $1$. So it should be mean.var<-xt/sum(xt) in order to ensure that the weights sum up to one. The remainder is correct. Incorporating a risk aversion parameter into the framework requires the solution to the minVar problem (See for example here). Therefore, dividing ...


I had the problem of creating a portfolio from 10000 time series. So I used greedy optimisation principle. 1. select best Sharpe ratio time series 2. select next time series that in combination creates best Sharpe ratio 3. add one more time series that creates best portfolio Sharpe ratio 4. continue adding one by one till you reach 100 or so 5. divide ...

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