Re: [Quantlib-users] 🚀 QuantLib with Fast Risks in Python 🚀

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

Hi Jörg,

thanks a lot, that's interesting. I have a couple of questions:

- I think the approach you take in the example is not entirely
correct. IterativeBootstrap uses Brent as the "first solver" which is
not differentiable on all branches. It uses bisection which has a zero
derivative everywhere. Of course you might be lucky and get correct
results anyhow in the specific run.
- More generally, how do you ensure correct treatment of control-flow
in your tool?
- Coming back to the bootstrap: even if you would ensure
differentiability, I think you usually don't want to record the
calibration itself to get market rate sensitivities - it's more
efficient to compute the the matrix d par / d zero (with AAD or just
bump-revalue) and invert that.

On the technical side:

- Last time I looked at XAD I noticed a slowdown of about 10x (I
think) in some QuantLib unit tests. How do you address that? I asked
this question many times (also Matlogica, Compatibl) but never seem to
get a straight answer. I still think instrumenting the whole library
is not a good approach, albeit very easy to to. It might also be good
enough for specific use cases, admittedly.
- If we are honest, the low AAD-overhead around 2x that you see is
actually due to poorly optimised pricing of vanilla swaps in QuantLib.
In this sense, QuantLib is an "easy target" for  "proof of concepts"
like the one in your blog. It might create wrong expectations though!

Thank you
Peter

On Fri, 5 Apr 2024 at 19:01, Jorg Lotze <jor...@xc...> wrote:
>
> Dear Community,
>
> Exciting news for QuantLib users! QuantLib-Risks, now available for Python, supercharges QuantLib with automatic differentiation. This new addition streamlines risk assessments and derivative pricing, making complex analyses more accessible than ever.
>
> Get started effortlessly with: pip install QuantLib-Risks
>
> Curious about the impact? QuantLib-Risks dramatically improves efficiency, achieving sensitivities calculation in nearly the same timeframe as standard pricing. Discover the full performance story with a real-world application here:
>
> https://auto-differentiation.github.io/quantlib-risks
>
> Kind regards,
> Jorg
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