Machine learning in trading: theory, models, practice and algo-trading - page 999
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And they said quantum mechanics.(( When was R Fourier?
Um... I fell for the hat... Shit, Yuri, enough with the exams. Why do I have to attach a scan of my diploma? You have to trust people.
What is it?
EE....
The radius-vector operator of a quantum mechanical particle?
Uh...
Do you really use these things in algorithms?
What happened to the ARCH effects? Of which there are over a hundred? And of which there is no end in sight?
I don't really get it.
Apparently, the point is that in addition to univariate distributions, there are reciprocal multivariate distributions that do not always wish to decompose into products of univariate ones (stochastic dependence). This leads to some additional effects. Numerous x-ARCHs help to account for these effects.
I don't really get it.
The standard GARCH model consists of three parts:
1. Detrending. Ideally using fractional differentiation to encompass Hearst
2. Dispersion modeling. This is not only the shape of the variance, but also the clumping, the behavior after jumps...
3. distribution modeling. This makes it possible to account for long tails.
The standard GARCH model consists of three parts:
1. Detrending. Ideally using fractional differentiation to encompass Hearst
2. Dispersion modeling. This is not only the shape of the variance, but also the clumping, the behavior after jumps...
3. distribution modeling. This makes it possible to account for long tails.
SanSanych, can you tell me where I can see the implementation of this algorithm?
Why don't any of the old-timers (Warlock, Toxic, etc., including, of course, you) do this? Even just reports from MT on the actual trade never seen.
If you ask the most trivial suggestion - maybe because they are not trading in MT?
I don't know what they're doing here, it's harder to answer. Habit. Earlier here (in MT community) was a really cool bunch of researchers who were always looking for interesting tasks, and just to read them was interesting. Now it has been replaced by blabbermouths like Asaulenko, like Rena or Nikitin. Useful content is generated literally a couple of people. But there are some who out of habit remain on the forum.
1. Detrending. Ideally, using fractional differentiation to encompass Hearst
I'm not very familiar with the subject. I would like to understand - is it possible that detrending with ARFIMA would be useful when there is a sharp trend change (top or bottom)?
SanSanych tell me where you can see the implementation of this algorithm?
Package rugarch. One of the ...