Menu
▾
▴
Re: [Quantlib-users] [<<EXT>>] Re: Transition from version 1:30 to 1.37+
|
From: Ioannis R. <qua...@de...> - 2025-03-07 22:31:32
|
One question related to the dividend topic. Does anyone know if discrete dividends can be handled in MonteCarlo in QuantLib? I know continuous dividend yields do, but I could not find anything that supports discrete jumps in the equity price paths. Thanks Ioannis On 3/7/2025 6:51 PM, Martin Adamec via QuantLib-users wrote: > Hi Luigi, > > Thank you for your response and useful hints. It it very helpful > summary and it it matches our general understanding that we formed > after checking of few previous posts in the mailing list as well as > some other resources that we could find online. > > The only thing on which I couldn’t find more clarity is the way of > “packaging” discrete dividends vs. dividend yield before sending it to > either ql.AnalyticDividendEuropeanEngine or > ql.FdBlackScholesVanillaEngine exercise engines. I noticed that some > shifts in data preparation paradigm happened specifically in that area > and that is actually the main reason why I sent this question to the > mailing list. I know I should be more specific in my first email, but > I hope I clarified my concern now. > > Please send me a few hints as how to proceed in either case when > preparing dividend information to be passed to the exercise engine. > > Once more, thank you very much > > Martin > > Martin Adamec CTO, DataDock Solutions datadocksolutions.com > <https://datadocksolutions.com/> > 862-221-1246|ma...@da... 40 Wall St, FL 43 | New > York, NY 10005 | USA > <https://maps.google.com/?q=40%20Wall%20St,%20FL%2042%20%7C%20New%20York,%20NY%2010005%20%7C%20USA> > > On Mar 7, 2025 at 10:09 AM -0500, Luigi Ballabio > <lui...@gm...>, wrote: >> Hi Martin, >> your excerpt seems to miss some possible combinations (European >> option with actual dividend data), or to allow some that wouldn't >> work correctly (DividendVanillaOption with BinomialVanillaEngine) but >> that might be because it's an excerpt, and you have other checks >> elsewhere. Anyway, trying to summarize the current status: >> >> a) always use ql.VanillaOption(payoff, exercise), because >> ql.DividendVanillaOption has been absorbed into it; >> b) choose the engine depending on exercise and whether you have >> actual dividend data: >> - for European options without dividend data, use >> ql.AnalyticEuropeanEngine(process); >> - for European options with dividend data, use >> ql.AnalyticDividendEuropeanEngine(process, dividends); >> - for American options without dividend data, use >> either ql.FdBlackScholesVanillaEngine(process) >> or ql.BinomialVanillaEngine(process, ...); >> - for American options with dividend data, use >> ql.FdBlackScholesVanillaEngine(process, dividends). The binomial >> engine doesn't support dividends. >> >> In short, if you have actual dividend data, pass them to an engine >> that can take care of them. >> >> Hope this helps, >> Luigi >> >> >> >> >> >> On Tue, Mar 4, 2025 at 6:00 PM Martin Adamec via QuantLib-users >> <qua...@li...> wrote: >> >> Hi quantlib-users, >> >> I am reaching out to ask you guys for some hints on proper >> transitioning to the newly refactored classes and methods related >> to equity option valuation calcs. >> >> We have in our code the older and now apparently outdated >> construct based on previous organization of the code. It is >> semi-clear to us how to proceed with the code available in >> version 1.37 (and most probably and hopefully up for quite a long >> time). >> >> I attached an excerpt of the code that is dealing with the >> instantiation and proper setup of the option object depending on >> the exercise style, preselection of the engine, and finally the >> distinction between the presence of discrete dividend data vs. >> dividend yield. >> >> My understanding is that the new code should be used in somehow >> simpler fashion but cannot quit clearly figure out the proper way >> (and maybe order) of setup steps to do to hit all of the input >> data variations we are considering in our current code. >> >> Any hint or advice is going to be highly appreciated, >> >> Thank you >> >> Martin Adamec CTO, DataDock Solutions datadocksolutions.com >> <https://datadocksolutions.com/> >> 862-221-1246|ma...@da... 40 Wall St, FL 43 | New >> York, NY 10005 | USA >> <https://maps.google.com/?q=40%20Wall%20St,%20FL%2042%20%7C%20New%20York,%20NY%2010005%20%7C%20USA> >> >> >> _______________________________________________ >> QuantLib-users mailing list >> Qua...@li... >> https://lists.sourceforge.net/lists/listinfo/quantlib-users >> > > > _______________________________________________ > QuantLib-users mailing list > Qua...@li... > https://lists.sourceforge.net/lists/listinfo/quantlib-users -- This email has been checked for viruses by Avast antivirus software. www.avast.com |
