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Apr
17
comment Monte Carlo simulation returns not normal distributed
Geometric Brownian motion is lognormally distributed in levels. en.wikipedia.org/wiki/Geometric_Brownian_motion
Apr
15
answered Is a stationary process necessarily mean-reverting?
Apr
15
comment Is a stationary process necessarily mean-reverting?
Seems weakly stationary to me.
Apr
9
comment Inferences with non-normal data
I understood what you meant, and I answered all of your questions. In regression, the assumptions are more related to the errors than the actual data. You can use Newey-West if you're worried about heteroskedasticity or autocorrelation. You don't need to make any adjustments for normality to make inferences, but there are techniques you can use regardless.
Apr
8
revised Inferences with non-normal data
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Apr
8
answered Inferences with non-normal data
Apr
7
comment Inferences with non-normal data
Do you mean percent changes of index closing values?
Apr
7
awarded  Necromancer
Apr
6
comment Please give a step-by-step explanation on how to build a factor model
You're asking for links after not asking for links....All the references the writer used are to classic papers or one of the most well-known finance textbooks.
Mar
30
answered Calculate efficient frontier using fPortfolio with incomplete set of returns
Mar
12
comment MLE estimate of normal distribution
The MLE variance estimator for normal distributions is biased because it divides by $n$ rather than $n-1$, see ee.columbia.edu/~dliang/files/mle_biased.pdf. Not sure how much that relates to the above quote.
Mar
4
comment What to use as portfolio diversification measure?
Thanks @vanguard2k
Mar
4
comment What to use as portfolio diversification measure?
@Richard Meucci seems to have moved on past that paper to his one on minimum torsion bets. I've been playing around with it the past couple of days and have found it to be MUCH better.
Feb
28
comment The future language of quant programming?
My sense is that what you're more concerned with what language to learn than the actual future for quant programming languages. The popular languages won't change enough over 10 years to worry about it. Regardless, if you learn a popular language, e.g. C, then you'll pick up the skills necessary to learn other languages if you need them. Moreover, you don't have to re-invent the wheel. C++ may have a bloated syntax, but it's fast and there's a library for everything. Or learn python, it's slow, but the syntax is great and there's a library for everything.
Feb
27
comment The future language of quant programming?
Relevant: quant.stackexchange.com/questions/306/…
Feb
23
answered Variable Selection in factor models
Feb
19
revised Computing the minimum variance portfolio
Fixing typo
Feb
18
comment Why do we usually model returns and not prices?
@RndmSymbl I think Richard's answer covers that. However, I think the discussion of log returns vs. arithmetic returns is not particularly relevant to why to use prices versus returns. Not that it's unimportant, but just not the first thing I think of.
Feb
12
comment What Matlab packages to I need as a Risk Analyst?
@SRKX If your programming skills are good enough, you don't need any toolboxes. :)
Feb
11
revised How do I use BIC (Bayesian Information Criterion) to estimated model AR (auto regressive) lag?
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