[R-SIG-Finance] Mean Variance Portfolio Optimization based on a DGP

Vivek Rao vivekr@o4 @ending from y@hoo@com
Wed Jul 11 17:39:26 CEST 2018


With a VAR your expected returns are changing at each time period. You could do a historicalsimulation where the the MVP portfolio is computed at each time step based on the expected1-period returns and the covariances. There is more evidence that covariances are predictablethan expected returns, so ideally you will have a model for time-varying covariances, maybe something as simple as an EWMA.
Transaction costs may negate the benefits of updating the portfolio often. One simple way toreduce transaction costs is to trade X% (say 10%) of the way toward the target each day.
In general, this is a dynamic programming problem. With an estimated VAR, you can predictnot just 1-period but N-period returns through iteration, and people have thought about how to optimize allocations when you have return forecasts over various time horizons. (Are thereR packages for this?)
Regards,Vivek Rao, CFABoston, MA
      From: "Fianu, Emmanuel Senyo" <emmanuel.senyo using gmail.com>
 To: r-sig-finance using r-project.org 
 Sent: Wednesday, July 11, 2018 7:16 AM
 Subject: [R-SIG-Finance] Mean Variance Portfolio Optimization based on a DGP
   
Dear All,

I am trying to employ the MVP method to determine optimal weights for a
data, which is fine for me to do.
However, I intend to use a different Data generating process (DGP) such as
Vector Autoregressive  Process (VAR), and then compute the optimal weights.
Theoretically, it looks okay but empirically, have someone carried out this
before? If yes: how did you go about it?

 I would be grateful for your constructive and helpful comments.

Many thanks,
Emmanuel Fianu

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