Hi there,
I would be interested in using the MonteCarlo framework to valuate path-dependent options. I assume to have a payoff which evaluates to -let's say- 3 fixing dates for expiry 1 year, but I'd like to integrate the stochastic differential equation (SDE, for short) for the underlying using a denser temporal grid, e.g. using 1000 temporal steps.
Hence I generate the path of interest (as a collection of times and drift-diffusion couples) "integrating" the corresponding DiffusionProcess over the 1000 elements TimeGrid. Then I pass this generated Path as argument of the operator() function in my Pricer class.
As far as I can understand, however, the current Path implementation in QuantLib does not make any difference between fixing dates (i.e. "mandatory times") and times in the Path only used to integrate the SDE. So if I need to evaluate the payoff over my 3 fixing dates I have to "re-integrate" the underlying SDE also in my Pricer::operator( ) function (to get the underlying values corresponding to the 3 fixing dates of interest). This seems to me a bit computationally inefficient.
What I kindly ask is then:
1) am I getting a correct interpretation of the problem?
2) if so, are there any available solutions?
3) and, is this an issue already considered by QuantLib developers?
Thanks in advance.
Luca Berardi
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