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Apr
18
comment Monte Carlo simulation returns not normal distributed
If you divide a lognormally distributed variable by a constant, then it will still be lognormally distributed.
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
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
7
comment Inferences with non-normal data
Do you mean percent changes of index closing values?
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
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
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
comment What Matlab packages to I need as a Risk Analyst?
@SRKX I'm not sure how helpful the whole "it can be avoided by writing a little bit of code" is. If I absolutely needed to query a database in Matlab, I'd be hard pressed to do it without the database toolbox. Maybe create a mex file to call ODB or call python's sqlalchemy. These aren't exactly trivial things though.
Feb
10
comment Why do we usually model returns and not prices?
@emcor I tried to pick my words carefully on purpose. I wrote that returns can be assumed to be stationary rather than that they were actually stationary. Of course, if there is volatility clustering or some other effect, then returns aren't technically stationary (variance changing over time). Nevertheless, stock prices almost always reject the Dickey Fuller test and returns almost always do not reject the Dickey Fuller test. Thus, it is useful to operate on the theory that the prices will have a unit root and returns do not.
Feb
7
comment Why do we usually model returns and not prices?
Related question: quant.stackexchange.com/questions/8875/…
Feb
3
comment On a source for a mean-variance portfolio optimization result
I think Markowitz' 1959 book does, but it's a straightforward optimization that is easy if you look up the relevant matrix derivatives. I think I went through the math in another question here, but can't find it now.
Jan
28
comment Why random walk Metropolis Hasting algorithm works bad on GARCH(1,1) parameters estimation
I would distinguish between Gibbs sampled MCMC and Metropolis-Hastings MCMC. Gibbs sampled MCMC (what I assumed you meant by random walk) does not do a rejection step the way that Metrpolis-Hastings does.
Jan
28
comment Why random walk Metropolis Hasting algorithm works bad on GARCH(1,1) parameters estimation
Rejecting the negative ones is Metropolis-Hastings. For MLE, you might look at the source code for Kevin Sheppard's MFE toolbox for Matlab. You can look at his implementation of multivariate Garch there as well. Alternately, fGarch or rugarch for R.
Jan
28
comment Why random walk Metropolis Hasting algorithm works bad on GARCH(1,1) parameters estimation
The Metropolis-Hastings step is that they have to ensure that alpha and beta are positive. I can't speak much more to this particular paper. I usually fit Garch with MLE because I have sufficient data. MC Stan has a good example on fitting Stochastic Volatility models in its manual that you might check out.