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S Sep 9, 2019 at 15:45 history suggested Mats Lind CC BY-SA 4.0
ugly misspelling in headline
Sep 9, 2019 at 13:54 review Suggested edits
S Sep 9, 2019 at 15:45
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May 13, 2019 at 10:46 comment added Confounded @ilovevolatility No, I am not aware of that paper. The issue I have encountered when performing a numerical minimisation of the Lagrangian is that the resulting coefficients mean that the term in the exponential can grow to positive infinity, so the tails of the real world distribution also have to fall off at an exponential rate or the resulting risk-neutral "distribution" will blow up.
May 9, 2019 at 15:00 history tweeted twitter.com/StackQuant/status/1126502231136776192
May 9, 2019 at 14:01 comment added Lisa Ann @ilovevolatility, just read the paper. Yes, it's like what I am suggesting to do. Nonetheless, forward price constraints can be already imposed when you estimate a log-normal mixture on the underlying (returns) distribution. Maybe there's no need to minimize the relative entropy with such constraint My two cents.
May 9, 2019 at 12:30 comment added user34971 I suppose you have tried / are aware of this approach: papers.ssrn.com/sol3/papers.cfm?abstract_id=170629
May 9, 2019 at 12:19 comment added Lisa Ann What if you had two parametric density functions, the first one (1) from the physical world and the second one (2) from the risk-neutral world? Would you be able to get the Radon-Nikodym derivative? You can get (1) by FHS and then by fitting a log-normal mixture, then you can do the same for (2) by constraining the mean to the forward price. You'll get two parametric densities, which you can even fit with a spline to have a smooth derivative of both. How does that sound?
May 9, 2019 at 12:03 history bumped CommunityBot This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
Apr 9, 2019 at 11:33 answer added Antoine Conze timeline score: 1
Apr 9, 2019 at 10:15 history edited Confounded CC BY-SA 4.0
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Apr 9, 2019 at 10:09 history asked Confounded CC BY-SA 4.0