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Monte Carlo, risk, QMC, statistical efficiency, high dimensional approximation...


Sep
28
awarded  Yearling
Sep
24
awarded  Autobiographer
Aug
22
comment Comparison of multicurve calibration methods
Thanks a lot, I definitely underappreciated some of these points. Nevertheless what do you think of the bootstrap drawbacks?
Jul
28
revised How to deal with extreme cases in normal random numbers generation?
deleted 1 character in body
Jul
28
revised How to deal with extreme cases in normal random numbers generation?
added 9 characters in body
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.