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Let's take 4 assets, whose values are known during a period of time of 2 years. Then I calculate the expected returns for each of these 4 assets thanks to these 2 years - historical data. I deduce the optimal weights that maximizes the expected return of the entire portfolio under a given risk (so I calculated the Makowitz's portfolio).

Now I want to test my algorithm dynamically. I want that the algorithm readjusts the optimal weights for each trading day (because until now I calculated my Markowitz's portfolio for a single period of time)

So my question is : if I am a trader who wants to calculate these optimal weights day after day, how to calculate the expected returns for each of these assets dynamically, day after day ?

Suppose I know their expected returns for the period [1:n], if I take into account the new datas at time n+1 to calculate the new expected return, is is the good procedure ?

Many thanks !

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    $\begingroup$ Considered using the kalman filter? $\endgroup$ – Kian Feb 8 '16 at 18:57
  • $\begingroup$ hmm... I will to think about it I don't well remember about it ! $\endgroup$ – glork Feb 8 '16 at 19:13
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    $\begingroup$ You could also consider using a ccc /dcc estimator for the covariance matrix of asset returns $\endgroup$ – Kian Feb 9 '16 at 18:49
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    $\begingroup$ For the newbies: ccc = constant conditional correlation, dcc = dynamic conditional correlation $\endgroup$ – noob2 Feb 9 '16 at 19:20
  • $\begingroup$ Many thx , I don't know this techniques but I will see it on Internet ! $\endgroup$ – glork Feb 9 '16 at 19:30
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Out-of-sample is basically impossible to predict means. Second moments are much easier. You can take a look at this post: Estimating $\mu$ - only increasing $T$ improves estimate?

Only with infinite $T$ you would be able to correctly estimate $\mu$. So theoretically your procedure could be correct if means are time-varying, but out of sample I bet your Markowitz strategy will perform poorly.

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  • $\begingroup$ Yes it's sure than I can 't have a correct estimate of the mean , but only an approximation. I was thinking of accumulating enough datas to have a first mean estimate that will use the trader, and then the trader will take into account the new datas to have new estimates of the mean: it's like a rolling window used for a dynamic portfolio $\endgroup$ – glork Feb 7 '16 at 17:24
  • $\begingroup$ Yap, as I said, that's how you usually do it. $\endgroup$ – phdstudent Feb 7 '16 at 17:37
  • $\begingroup$ Ok I will try to implement it but why would it perform poorly ? $\endgroup$ – glork Feb 7 '16 at 17:40
  • $\begingroup$ As I said, it is almost impossible to feed a Markowitz model out of sample. You might get some improvement using option free implied moments. Check table 2 and 3 of this paper: papers.ssrn.com/sol3/papers.cfm?abstract_id=1474212 $\endgroup$ – phdstudent Feb 7 '16 at 17:54
  • $\begingroup$ Ahh ok ! Besides it takes a lot of time to calculate at every step ... Many thanks for your answers and reference ! $\endgroup$ – glork Feb 7 '16 at 18:01

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