Re: [Quantlib-users] Discount Method

[Luigi B. <lui...@gm...>]

Hi Michael,

1. a very interesting question but I'm not sure of the answer.  I'm
guessing you could use a root solver for the spread given the prices
implied by the new curves, but I'm probably missing a lot of details.
2. I think the closest we have is the possibility to generate future
interest-rate paths via Monte Carlo given a Hull-White process (which in
turn would be calibrated from a volatility surface).
3. At the moment, I guess the best way would be to extend the swig wrappers
and recompile the wheel to add a call taking a list of dates and this
moving the loop to C++.  I don't know how feasible that is for you,
though...

Luigi


On Fri, May 3, 2024 at 3:53 PM Michael (DataDriven portal) <
mi...@da...> wrote:

> Hi Luigi:
>
> Thanks for getting back to me!
>
> To be specific, we use QuantLib for mortgage bond pricing e.g. calculate
> yields, spreads, durations given mortgage performance inputs (e.g.
> prepayments or default vectors). This works quite well for static metrics
> that rely on a single yield curve input. But we also would like to
> calculate option adjusted measures (e.g. OAS - option adjusted spread) that
> require inputs of many yield curves (e.g. with high/lower rates leves,
> flatter/steeper curve shapes).
>
> So a few specific questions I have are as follows:
>
> 1. What is the best way to generate option adjusted metrics (e.g. OAS) in
> QuantLib if user inputs yield curves with associated probabilities?
> 2. Can QuantLIb generate future yield curves with associated probabilities
> based on current structure of term structure of interest rates and
> volatility surface?
> 3. What is the best way to retrieve the yield curve details for cash flows
> calculations (e.g. monthly discount factors for 360 months which is a
> typical number of cash flows for a mortgage bond)? We tried a *discount*
> method for each month separately but it takes too long.
>
> Thanks,
>
> Michael
>
> On Fri, May 3, 2024 at 4:06 AM Luigi Ballabio <lui...@gm...>
> wrote:
>
>> Hi Michael, apologies for the delay. I'm not sure what's the most
>> effective way to do this.  Well, no, scratch that—the most efficient way
>> would be to do it in C++.  From Python, if you have a process available
>> (such as HullWhiteProcess, for instance) you can use the available
>> PathGenerator class.  What kind of simulation do you have in mind?
>>
>> Luigi
>>
>>
>> On Sat, Apr 20, 2024 at 5:49 PM Michael (DataDriven portal) <
>> mi...@da...> wrote:
>>
>>> Hi Luigi:
>>>
>>> Thank you for getting back to me!
>>>
>>> What is the most efficient way to do Monte Carlo simulations in QuantLib
>>> when I need to obtain multiple paths of yield curves for different
>>> scenarios (they do not have to be vectors of DFs but could be FRAs or any
>>> other rate metrics)?
>>>
>>> Thanks,
>>>
>>> Michael
>>>
>>> On Sat, Apr 20, 2024 at 10:59 AM Luigi Ballabio <
>>> lui...@gm...> wrote:
>>>
>>>> Hello Michael,
>>>>     no, I'm afraid vector methods are not available.
>>>>
>>>> Luigi
>>>>
>>>>
>>>> On Sun, Apr 14, 2024 at 8:30 PM Michael (DataDriven portal) <
>>>> mi...@da...> wrote:
>>>>
>>>>> Hi All,
>>>>>
>>>>> I am using a *discount* method on a curve to get a discount factor
>>>>> for a given maturity (like in shown in the Cookbook below). But I need to
>>>>> output discount factors for all monthly cash flows of a bond (in my case
>>>>> 360 cash flows) so it is time consuming to call the *discount* method
>>>>> 360 times.
>>>>>
>>>>> Is there a way to get all discount factors (for all cash flows - 360
>>>>> in my case) in a single call to a curve by passing all maturity dates (e.g.
>>>>> in a list) to speed up the calculations?
>>>>>
>>>>> Thanks,
>>>>>
>>>>> Michael
>>>>>
>>>>> [image: image.png]
>>>>> _______________________________________________
>>>>> QuantLib-users mailing list
>>>>> Qua...@li...
>>>>> https://lists.sourceforge.net/lists/listinfo/quantlib-users
>>>>>
>>>>