[Quantlib-dev] selective model calibration

[Peter C. <pca...@gm...>]

Hi,

   I would like to be able to calibrate only a subset of the parameters
of a CalibratedModel instance. Even more I would like to fix a subset of
elements within one multidimensional parameter in some cases. With this
one could e.g.

- calibrate both reversion and volatilities or fix the reversion and
  calibrate only volatilities in a Hull White model

- calibrate the model volatilities in a Hull White model iteratively to
  single interest rate options with ascending option maturities instead
  of having to do a global calibration to the whole set (which can be
  much slower when you have many options in the calibration basket)

- easily avoid the redundancy in the piecewise volatlities of a markov
  model (multiplying the volatilies by a (non zero) scalar factor does
  not change the model up to numeraire recalibration)

There are probably more use cases. I guess there are workarounds for
most of these cases, but I would like to propose a solution in the
CalibratedModel class itself. Since I am not sure about the design I am
not just sending a pull request but would like to discuss the approach
first. Here is what I would try:

Add a parameter to the calibrate method specifying the free parameters
in a vector<bool> and defaulting to an empty vector meaning all
parameters are free. Within the method one can make use of the
ProjectedCostFunction then to easily get what we want. One could also
make this calibrate method protected and leave the public interface as
it is, just invoking the new method with the default for the new
parameter. That is probably better because on the level of the
CalibratedModel one does not know the meaning of the parameters and
of their components.

Again in the protected section, provide an inner class that allows to
construct the above vector<bool> conveniently from specifying parameters
to include or exclude or indices within a parameter to include or
exclude in or from the set of free parameters.

This does not change anything so far. However derived models can now
easily overwrite the calibrate method (which we would have to make
virtual) and do a specialized calibration as needed / specified in the
concrete models.

What do you think ?

regards
   Peter