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" <[email protected]>
To: [email protected]
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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