[R-SIG-Finance] solnp Problem Inverting Hessian
Paul Gilbert
pgilbert902 at gmail.com
Tue Dec 15 16:42:45 CET 2015
Just to be clear, when I said "difference between the largest and
smallest elements" I am think of the absolute value. It is the scale not
the sign that causes problems in optimization. And I cannot commenting
on whether you have the correct objective function, just pointing out
what would happen numerically with the one you specified.
Paul
On 12/15/2015 10:17 AM, Michael Ashton wrote:
> That is very interesting. Actually some of the projected returns are
> negative, some are very close to zero. None is exactly zero, which I
> guess is why it's not singular, but that's why it's ill-conditioned.
> Very interesting. And hard to solve...if I add 5% to all returns will
> I still get the same portfolio? Seems to me intuitively I shouldn't
> since the proportional risk/return tradeoff then is greater for the
> assets on the low end of the spectrum, but I might be wrong.
>
> -----Original Message----- From: Paul Gilbert
> [mailto:pgilbert902 at gmail.com] Sent: Tuesday, December 15, 2015 9:58
> AM To: Michael Ashton Cc: Michael Weylandt;
> r-sig-finance at r-project.org Subject: Re: [R-SIG-Finance] solnp
> Problem Inverting Hessian
>
> I have not been paying close attention and I've missed something in
> this thread, but ...
>
> I think the hessian in the optimization will be the second derivative
> of this function
>
>>>>> opt.fun <- function(wgt.vect)
>>>>> {-crossprod(wgt.vect,t(Ret.vect))}
>
> WRT wgt.vect. If there are zero elements in Ret.vect then that
> hessian will be singular. And if there are orders of magnitude
> difference between the largest and smallest elements in Ret.vect then
> the hessian will be ill-conditioned.
>
> The ill-conditioned case is a numerical problem and you might solve
> it with a different algorithm, or by better scaling. The zero case is
> a theoretical problem, there is not a unique optimal point but rather
> whole continuum of optimal points (your parameters corresponding to
> the zero elements will not make any difference in the function
> value).
>
> HTH, Paul
>
> On 12/14/2015 11:39 PM, Michael Weylandt wrote:
>> (+list -- I'm not a numerical linear algebra expert or portfolio
>> optimization expert, but there are a number on this list who might
>> chime in)
>>
>> That is quite high, and almost certainly problematic.
>>
>> I'm not sure what an acceptable bound is (will depend on your
>> solver), but that's almost certainly above it for any algorithm
>> which will require inverting cov.mat.
>>
>> Two things you could try: - use a different (non-Hessian-using)
>> optimization algorithm; - use some form of shrinkage/regularization
>> (Ledoit-Wolf is a common choice) to tame your covariance matrix.
>>
>> Michael
>>
>> On Mon, Dec 14, 2015 at 10:10 PM, Michael Ashton
>> <m.ashton at enduringinvestments.com> wrote:
>>> Well, not sure whether this makes any sense but kappa(cov.mat)
>>> gives me 11148245007.
>>>
>>> That seems large. But I am not sure what it is supposed to be.
>>>
>>> -----Original Message----- From: Michael Weylandt
>>> [mailto:michael.weylandt at gmail.com] Sent: Monday, December 14,
>>> 2015 9:29 PM To: Michael Ashton Cc: r-sig-finance at r-project.org
>>> Subject: Re: [R-SIG-Finance] solnp Problem Inverting Hessian
>>>
>>> What's the condition number of your correlation/covariance
>>> matrix? (?kappa)
>>>
>>> I think I've used quadprog for portfolio optimization with
>>> success, but it's been a while.
>>>
>>> MW
>>>
>>> On Mon, Dec 14, 2015 at 8:06 PM, Michael Ashton
>>> <m.ashton at enduringinvestments.com> wrote:
>>>> Do you have a suggestion for such? I have in the past tried
>>>> fPortfolio, but it would not allow me to specify my own
>>>> projected return vectors rather than the historical returns of
>>>> the series (which is exactly backwards).
>>>>
>>>> As for whether the matrix is comfortably non-singular...I
>>>> suppose it depends in a Clintonian way on the meaning of
>>>> "comfortably," but I can create a Cholesky decomposition
>>>> without it blowing up, which is usually how I can tell if I
>>>> have done something stupid. Well, stupider than normal.
>>>>
>>>> -----Original Message----- From: Michael Weylandt
>>>> [mailto:michael.weylandt at gmail.com] Sent: Monday, December 14,
>>>> 2015 8:32 PM To: Michael Ashton Cc:
>>>> r-sig-finance at r-project.org Subject: Re: [R-SIG-Finance] solnp
>>>> Problem Inverting Hessian
>>>>
>>>> The Hessian is the matrix of second derivatives
>>>> (https://en.wikipedia.org/wiki/Hessian_matrix) -- in scalar
>>>> terms, you're finding a point where the second derivative is
>>>> zero and then trying to divide by the second derivative to
>>>> calculate the step size.
>>>>
>>>> I haven't gone through your code in any detail, but I'd start
>>>> by checking the covariance matrix since that's proportional to
>>>> the Hessian of your objective function. Is it (comfortably)
>>>> non-singular?
>>>>
>>>> Since you're just solving the standard Markowitz problem, you
>>>> might try a simpler (quadratic/convex) solver instead of a
>>>> general non-linear solver. Should behave a bit better.
>>>>
>>>> Michael
>>>>
>>>>
>>>> On Mon, Dec 14, 2015 at 6:13 PM, Michael Ashton
>>>> <m.ashton at enduringinvestments.com> wrote:
>>>>> I must admit to being flummoxed here, mainly because my
>>>>> linear algebra was 25 years ago and I can't remember what a
>>>>> Hessian is.
>>>>>
>>>>> I have a matrix of 60 securities' weekly returns, along with
>>>>> 60 projected returns. The returns are in a vector called
>>>>> Ret.vect and the covariance matrix of weekly returns in
>>>>> cov.mat . I have the minConstraints and maxConstraints that
>>>>> the parameters are permitted to take. I cycle through
>>>>> targeted risks and get the same error for each risk
>>>>> targeted...below I have removed the loop to focus on the
>>>>> risk=0.002.
>>>>>
>>>>> wgt.vect=c(rep(1/60, 60)) constr.fun <- function(wgt.vect)
>>>>> {; c1 = sqrt(crossprod(t(wgt.vect %*% cov.mat),wgt.vect)); c2
>>>>> = sum(wgt.vect); return(c(c1,c2)); } ineqconstr.fun <-
>>>>> function(wgt.vect) { wgt.vect[1:60]; } opt.fun <-
>>>>> function(wgt.vect) {-crossprod(wgt.vect,t(Ret.vect))}
>>>>>
>>>>> OptimSolution <-
>>>>> solnp(wgt.vect,opt.fun,constr.fun,eqB=c(0.002,1),ineqconstr.fun,ine
>>>>>
>>>>>
qL
>>>>> B =minConstraints,ineqUB=maxConstraints)
>>>>>
>>>>> I get the following error: solnp--> Solution not
>>>>> reliable....Problem Inverting Hessian.
>>>>>
>>>>> Well, that doesn't tell me very much! The parameters
>>>>> (weights) that are output for each run, as I cycle through
>>>>> the weights, are very scrambled...lots of little allocations,
>>>>> rather than clumping as you would expect to happen especially
>>>>> at the risky and riskless ends of the spectrum.
>>>>>
>>>>> Can anyone with more math than me give me a helping hand on
>>>>> the Hessian?
>>>>>
>>>>> Thanks,
>>>>>
>>>>>
>>>>> Mike
>>>>>
>>>>> Michael Ashton, CFA Managing Principal
>>>>>
>>>>> Enduring Investments LLC W: 973.457.4602 C: 551.655.8006
>>>>>
>>>>>
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