Re: [Quantlib-users] [EXT] Re: Forward CMT rates from G2++ calibrated model for OAS model

[Philippe H. <pha...@ma...>]

I'm interested in that. Is the LMM only in C++? Is t in the production release or only in a branch? Anyone on this forum knows about it? Best regards Philippe Hatstadt 203-252-0408 https://www.linkedin.com/in/philippe-hatstadt/ On Feb 6, 2024, at 10:59 AM, Luigi Ballabio <lui...@gm...> wrote: No, I'm afraid G2 is the only two-factor model we have in that framework. There's also an implementation of LMM from the late Mark Joshi, but I'm not familiar with it. Luigi On Tue, Feb 6, 2024 at 1:29 PM Philippe Hatstadt < phi...@ex... > wrote: Thanks Luigi. Wow 18 years… Are there other models that can de-correlate CMS2Y versus CMS10Y and longer? Working in OAS model where slope of the curve is a principal component. Regards Philippe Hatstadt +1-203-252-0408 On Feb 6, 2024, at 7:00 AM, Luigi Ballabio < lui...@gm... > wrote: Hmm—you're right, it looks like the G2 processes are only half-done. From the git logs of the C++ library, it looks like they were added 18 years ago and never updated (and probably never used either). They should be fixed there. I've opened https://github.com/lballabio/QuantLib/issues/1904 but I don't know when someone will pick it up. Luigi On Mon, Feb 5, 2024 at 8:44 PM Philippe Hatstadt < phi...@ex... > wrote: @Luigi Ballabio trying to follow up on this discussion. Here is what I found at the Python level. A. Apparently, there is a well-defined way of generating short rate paths for HW1F as follows, per cookbook, and post calibration of a and sigma: hw_process = HullWhiteProcess(spot_curve_handle, a, sigma) rng = GaussianRandomSequenceGenerator( UniformRandomSequenceGenerator(timestep, UniformRandomGenerator())) seq = GaussianPathGenerator(hw_process, length, timestep, rng, False) B. For G2 model, I was able to find this: g2pp_fprocess = G2ForwardProcess(a, sigma, b, eta, rho) or g2pp_process = G2Process(a, sigma, b, eta, rho). What is puzzling is that neither call takes spot_curve_handle as a parameter, which is confirmed by the SWIG extract below. Does it mean that either of G2Process() / G2ForwardProcess() classes are not "finished" products, and/or am I supposed to pass the term structure handle in a different way? More generally, how am I supposed to use this class, if at all? # Register G2Process in _QuantLib: _QuantLib.G2Process_swigregister(G2Process) class G2ForwardProcess(StochasticProcess): r"""Proxy of C++ G2ForwardProcess class.""" thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") __repr__ = _swig_repr def __init__(self, a, sigma, b, eta, rho): r"""__init__(G2ForwardProcess self, Real a, Real sigma, Real b, Real eta, Real rho) -> G2ForwardProcess""" _QuantLib.G2ForwardProcess_swiginit(self, _QuantLib.new_G2ForwardProcess(a, sigma, b, eta, rho)) Philippe Hatstadt On Tue, Jan 30, 2024 at 11:29 AM philippe hatstadt < pha...@ma... > wrote: Yeah that’s what I was thinking. At least I’d be making indirect C++ calls. I might try. Regards Philippe Hatstadt +1-203-252-0408 On Jan 30, 2024, at 11:15 AM, Luigi Ballabio < lui...@gm... > wrote: Hmm, I'm not sure. The only thing that comes to mind is that, given a path for interest rates, one could create some sort of interest-rate term structure and then extract CMT rates from it. It's not something I tried, though. Luigi On Tue, Jan 30, 2024 at 1:39 PM Philippe Hatstadt < phi...@ex... > wrote: Except I need to generate CMT rates along each path for my prepayment model. That would have be to be a pure Python Ioop? How would you then advise to calculate CMT rates from a path of short rates with the goal to try and use as many wrapped Python calls to underlying C++? Regards Philippe Hatstadt +1-203-252-0408 On Jan 30, 2024, at 7:33 AM, Luigi Ballabio < lui...@gm... > wrote: It might not be so bad. The generation of the paths is driven from Python but performed by the GaussianPathGenerator class in C++. I'd give it a try. Luigi On Tue, Jan 30, 2024 at 1:28 PM Philippe Hatstadt < phi...@ex... > wrote: Thank you. I looked at it and it’s great. His post-calibration simulation on HW is done in Python though so performance will be an issue. Thence my asking if there were routines existing in C++. Thank you. Regards Philippe Hatstadt +1-203-252-0408 On Jan 30, 2024, at 7:24 AM, Luigi Ballabio < lui...@gm... > wrote: Hi, not much is already existing, I'm afraid. You can have a look at Goutham's post at https://gouthamanbalaraman.com/blog/hull-white-simulation-quantlib-python.html (also in the cookbook if you have it) for a few ideas; he generates interest-rate paths based on a Hull/White model, but something similar should work for G2 as well. Hope this helps, Luigi On Wed, Jan 24, 2024 at 8:03 PM Philippe Hatstadt < phi...@ex... > wrote: I was able to calibrate a G2++ model to normal UST swaption volatilities (heroically using SOFR swaptions and re-scaling by rates ratio to generate so-called Treasury Swaption vol surface). Code is below: model = G2(term_structure); # engine = TreeSwaptionEngine(model, 25) # engine = ql.G2SwaptionEngine(model, 10, 400) engine = ql.FdG2SwaptionEngine(model) swaptions = create_swaption_helpers_normal(data, index, term_structure, engine) optimization_method = LevenbergMarquardt(1.0e-8,1.0e-8,1.0e-8) end_criteria = EndCriteria(1000, 100, 1e-6, 1e-8, 1e-8) model.calibrate(swaptions, optimization_method, end_criteria) a, sigma, b, eta, rho = model.params()\ The question now is as follows: I want to use this model towards a GNMA OAS model for which I would need monte-carlo paths of 2y/5y/10y forward CMT rates spaced say monthly. I assume that I would first need to use the 5 G2++ parameters calibrated above and then generate paths of the short rate, then somehow compute forward CMT at each forward monthly epoch Ti by computing the break-even coupon C10(Ti) such that PV(Ti, bond(cpn=C10(Ti)) == 100? Are there existing QL classes or modules that do all that from a given calibrated model like above? By the same token, I would also need stochastic pathwise discount factor vectors DF(Ti, path j), i=0 to 30y monthly. Is there also a QL module that generates those? I can obviously do it manually, but I am on the python side, so I want to re-use as much of existing libraries as I can to use efficient C++ code indirectly via SWIG. Regards Philippe Hatstadt 1370 Broadway, Suite 1450 | New York, NY | 10018 Broker-Dealer services offered through Exos Securities LLC, Member SIPC, FINRA. For important disclosures including Form CRS and Regulation BI click here . Confidentiality Notice : The information contained in this email (including attachments) is only for the personal and confidential use of the sender and recipient named above. If the reader is not the intended recipient, you are notified that you have received this message in error and that any review, dissemination, copying or distribution is prohibited. If you have received this communication in error, please notify the sender immediately by e-mail and delete or destroy the original message and all copies. _______________________________________________ QuantLib-users mailing list Qua...@li... https://lists.sourceforge.net/lists/listinfo/quantlib-users 1370 Broadway, Suite 1450 | New York, NY | 10018 Broker-Dealer services offered through Exos Securities LLC, Member SIPC, FINRA. For important disclosures including Form CRS and Regulation BI click here . Confidentiality Notice : The information contained in this email (including attachments) is only for the personal and confidential use of the sender and recipient named above. If the reader is not the intended recipient, you are notified that you have received this message in error and that any review, dissemination, copying or distribution is prohibited. If you have received this communication in error, please notify the sender immediately by e-mail and delete or destroy the original message and all copies. 1370 Broadway, Suite 1450 | New York, NY | 10018 Broker-Dealer services offered through Exos Securities LLC, Member SIPC, FINRA. For important disclosures including Form CRS and Regulation BI click here . Confidentiality Notice : The information contained in this email (including attachments) is only for the personal and confidential use of the sender and recipient named above. If the reader is not the intended recipient, you are notified that you have received this message in error and that any review, dissemination, copying or distribution is prohibited. If you have received this communication in error, please notify the sender immediately by e-mail and delete or destroy the original message and all copies. 31 East 32nd Street, 3rd Floor | New York, NY | 10016 Broker-Dealer services offered through Exos Securities LLC, Member SIPC, FINRA. For important disclosures including Form CRS and Regulation BI click here . Confidentiality Notice : The information contained in this email (including attachments) is only for the personal and confidential use of the sender and recipient named above. If the reader is not the intended recipient, you are notified that you have received this message in error and that any review, dissemination, copying or distribution is prohibited. If you have received this communication in error, please notify the sender immediately by e-mail and delete or destroy the original message and all copies. 31 East 32nd Street, 3rd Floor | New York, NY | 10016 Broker-Dealer services offered through Exos Securities LLC, Member SIPC, FINRA. For important disclosures including Form CRS and Regulation BI click here . Confidentiality Notice : The information contained in this email (including attachments) is only for the personal and confidential use of the sender and recipient named above. If the reader is not the intended recipient, you are notified that you have received this message in error and that any review, dissemination, copying or distribution is prohibited. If you have received this communication in error, please notify the sender immediately by e-mail and delete or destroy the original message and all copies.