Brian G. Peterson
ph: +1.773.459.4973
im: bgpbraverock
On Thu, 2022-02-03 at 16:56 +0100, Jarno Bergmeier wrote:
> I explicitly wanted to combine both at the same time, that was what I
> read in the study and what I was so far not able to code myself. But
> I do not know, whether the authors used R or some other software, so
> it might very well be possible?that this cannot be implemented
> simultaneously with R/PortfolioAnalytics. I will continue to try it,
> or just use the risk aversion coefficient to distinguish between
> investor types.?
> ?
> Nevertheless, I want to thank you again!
> Jarno
> ?
> Gesendet:?Donnerstag, 03. Februar 2022 um 15:59 Uhr
> Von:?"Brian G. Peterson" <brian at braverock.com>
> An:?"Jarno Bergmeier" <jarno.bergmeier at web.de>
> Cc:?r-sig-finance at r-project.org
> Betreff:?Re: Aw: Re: [R-SIG-Finance] PortfolioAnalytics: "Upper
> volatility bound"-constraint
> I think you might want:
>
> port <- add.objective(port, type = "risk", name = "StdDev", ?max =
> 0.0225)
>
> I'm not sure that you'll be able to combine risk_aversion and a max
> bound.
> ?
> Brian
> ?
> --
> Brian G. Peterson
> ph: +1.773.459.4973
> im: bgpbraverock
> ?
> On Thu, 2022-02-03 at 15:33 +0100, Jarno Bergmeier wrote:
> > Hello Brian,
> > thank you so much for your response and for offering me to follow
> > up on it. Setting an upper limit on the risk objective function is
> > exactly what I want to do, you?re right. Basically, I would like to
> > implement something like the ?max_prisk?-argument from the risk
> > budget constraint, but for the portfolio variance in the risk
> > objective function.
> > I tried to implement it, so that I could use it like this e.g., in
> > the portfolio specification:
> > port <- add.objective(port, type = "risk", name = "var",
> > risk_aversion = 2, max_portfvar = 0.0225)
> > But so far I was not able to come up with a function like that,
> > that I could still use to maximize quadratic utility and supply
> > with a custom moment function in the
> > optimize.portfolio/optimize.portfolio.rebalancing function.
> > I would therefore be extremely appreciative if you would be able to
> > provide me with a worked example.
> > ?
> > Kind regards,
> > Jarno
> > ?
> > Gesendet:?Mittwoch, 02. Februar 2022 um 16:57 Uhr
> > Von:?"Brian G. Peterson" <brian at braverock.com>
> > An:?"Jarno Bergmeier" <jarno.bergmeier at web.de>, r-sig-finance at r-
> > project.org
> > Betreff:?Re: [R-SIG-Finance] PortfolioAnalytics: "Upper volatility
> > bound"-constraint
> > You are correct that adding this type of constraint would break
> > most convex solvers.
> > ?
> > You can do this today with any of the global solvers by setting an
> > upper limit on the risk objective function.
> > ?
> > I believe that you could do this with a conical solver constraint
> > as well. ?You could access some conical solvers today via ROI in
> > PortfolioAnalytics, and we hope to add explicit conical solver
> > support via CVXR this summer in a Google Summer of Code project.
> > ?so maybe look into using ROI as well for now.
> > ?
> > Hopefully this helps. ?Please follow up if you need a worked
> > example and I'll try to find some time to put one together.
> > ?
> > Regards,
> > ?
> > Brian
> > ?
> > --
> > Brian G. Peterson
> > ph: +1.773.459.4973
> > im: bgpbraverock
> > ?
> > On Wed, 2022-02-02 at 16:48 +0100, Jarno Bergmeier wrote:
> > > Hello everyone,
> > > I wanted to ask my first-ever question via R-SIG-Finance, with
> > > regard to portfolio constraints and the ?PortfolioAnalytics?-
> > > package in R.
> > > When distinguishing between certain types of investors, the
> > > empirical literature often employs certain values for the risk
> > > aversion coefficient. I now read a study that additionally makes
> > > use of upper volatility bounds (?less than or equal to??) and was
> > > asking myself whether it would be possible to implement something
> > > like that for out-of-sample portfolio optimization with
> > > PortfolioAnalytics.
> > > So, a code similar to this:
> > > ?
> > > port <- portfolio.spec(assets = colnames(returns_monthly))
> > > port <- add.constraint(port, "long_only")
> > > port <- add.constraint(port, "weight_sum", min_weight = 0.99,
> > > max_weight = 1.01)
> > > port <- add.objective(port, type = "risk", name = "var",
> > > risk_aversion = 2)
> > > port <- add.objective(port, type = "return", name = "mean")
> > > opt <- optimize.portfolio.rebalancing(R = returns_monthly,
> > > portfolio = port, optimize_method = "ROI", rebalance_on =
> > > "months", training_period = 60, rolling_window = 60)
> > > ?
> > > that also includes another constraint, which gives an upper
> > > volatility bound for the portfolio?
> > > I recently read a dissertation, that stated that such a
> > > constraint would be a convex one, which could not be expressed
> > > linearly, and therefore not solvable for many packages/solvers.
> > > Therefore, I wanted to ask if anyone here might have already
> > > implemented an upper volatility/variance constraint with
> > > PortfolioAnalytics or knows, whether it would be possible to
> > > successfully do this with the package?
> > > ?
> > > Kind regards
> > > Jarno
> > > ?
> > > _______________________________________________
> > > R-SIG-Finance at r-project.org?mailing list
> > > https://stat.ethz.ch/mailman/listinfo/r-sig-finance
> > > -- Subscriber-posting only. If you want to post, subscribe first.
> > > -- Also note that this is not the r-help list where general R
> > > questions should go.
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