Quartz
Reputation
998
Top tag
Next privilege 1,000 Rep.
 Jul 28 comment How to deal with extreme cases in normal random numbers generation? Btw what is your $\mathcal U(0,1)$ generator? Jul 28 answered How to deal with extreme cases in normal random numbers generation? Jul 21 revised Comparison of multicurve calibration methods added issue Jul 21 comment Comparison of multicurve calibration methods I don't even get the comment: it is bootstrapping that fits datapoints perfectly giving awkward curves... and no-arbitrage conditions are rarely satisfied by either approach. Jul 21 asked Comparison of multicurve calibration methods Jul 2 awarded Curious Apr 30 comment Usage of Brownian Bridge? Yes, sorry for being brief while at work... Another nice related paper is Quasi-Monte Carlo Methods in Financial Engineering: An Equivalence Principle and Dimension Reduction Apr 29 answered Usage of Brownian Bridge? Apr 7 comment Graduating Quantitative Finance (please don't move it to meta immidiately) Could you please do some spell checking of the linked question? :) Anyway I personally think it would demotivate us, I'd give it a few more months of spicy beta... Apr 1 answered What are the merits of pseudo random numbers over quasi random numbers in monte-carlo simulation? Mar 28 comment Effects of random-generator-choice on derivative's price There's a lot of discussion about PRNGs in finace forums or some books, but given modern generators quality the issue is now kinda solved. Roughly said one samples from AES (or any other good hash function) by feeding in successive numbers, their encrypted values are then pseudorandom; check the so called "counter mode" of AES. Mar 26 comment An alternative to the Gaussian distribution to describe/fit market stock returns Of course S can be larger than all the money out there: we live in a fractional reserve system. :) Anyway that would be an ex ante fixed boundary, so that one is not really solving that integral but a variant; it's not the same as having freedom of truncation. Mar 26 comment An alternative to the Gaussian distribution to describe/fit market stock returns @Aksakal: I don't get it: if the integral explodes you can clip arbitrarily and get any desired pseudoresult, or not? What's the meaning and use? Unless of course the boundary is fixed ex ante. Anyway I'm still not convinced, need to work out that integral: t-s approximate a normal arbitrarily. Thanks for the link! Mar 26 comment An alternative to the Gaussian distribution to describe/fit market stock returns @Aksakal: right, I should have checked first... what a silly cheat! Mar 26 comment An alternative to the Gaussian distribution to describe/fit market stock returns @Aksakal: Student t pricing. Mar 26 revised Effects of random-generator-choice on derivative's price added 136 characters in body Mar 26 comment Effects of random-generator-choice on derivative's price For qMC you can check the book from Lemieux, it also deals with various finance examples. Or the shorter introductions also by Lemieux&L'Ecuyer or by Larcher&Leobacher, or the chapter in Glasserman. Anyway it's quite a tricky topic, don't expect to use it as a black-box without surprises. Mar 26 answered Effects of random-generator-choice on derivative's price Mar 13 comment Normally Distributed Returns Become Leptokurtic Due to Compounding This effect will vanish if you use log returns. Be aware of the difference between the two and when each is appropriate. See also the papers by Meucci on this. Mar 7 comment An alternative to the Gaussian distribution to describe/fit market stock returns @Aksakal: yes, SV is certainly better, also as a fit to data, but can be unpractical for certain tasks. Anyway one can circumvent the Student t aggregation "problem" quite easily in practice. PS: for most dof-s Student t log-returns shall also have finite mean, or not?