The Sultonov Regression Model (SRM) - claiming to be a mathematical model of the market. - page 26
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Well don't :)
To be or not to be, that is the question!
;)
To be or not to be, that is the question!
;)
To be or not to be. Budmo!
Everything is to be. Budmo!
What is the problem? The conversation was about a normally distributed price, not random straying, which are two different things.
Random price straying will give you a normally distributed price in the end - that's for sure.
;)
Randomly wandering the price will give you a normally distributed price in the end - that's for sure.
;)
figure... if you look more closely, the price has more of a Laplace distribution... - Admittedly :)
Incidentally, the parapmeter t in (18) is nothing but a representation of time t in the Laplace transform, so, as shown earlier https://c.mql4.com/forum/2012/07/qwzi3.JPG, RMS perfectly describes the Laplace distribution http://dic.academic.ru/dic.nsf/enc_mathematics/2650/ЛАПЛАСА.
P(t) = P0 +D*Hammasp(t/t;n+1;1;1), as interpreted by Microsoft.
figure... if you look more closely, the price has more of a Laplace distribution... - Adnazno :)
The real price distribution, the Laplace distribution and the normal distribution are three different things... )
And SB and normal distribution are the same thing. and as I wrote:
Random price straying will give you a normally distributed price in the end....
Or do you think otherwise?
Good thing the price does not wander randomly.
;)
The real price distribution, the Laplace distribution and the normal distribution are three different things... )
And SB and the normal distribution are the same thing. and as written:
Or do you think otherwise?
It's a good thing that the price does not wander randomly.
;)
The random walk has price increments described by a normal distribution, not the price itself. Two different things. SB has no tendency to return to the mean and may trend away from its original value. A normally distributed price has a 100% guaranteed return to the mean.
A normally distributed price has a 100% guaranteed return to the average.
By the way, the parapmeter t in (18) is nothing but a representation of time t in the Laplace transform, so, as shown earlier https://c.mql4.com/forum/2012/07/qwzi3.JPG, RMS perfectly describes the Laplace distribution http://dic.academic.ru/dic.nsf/enc_mathematics/2650/ЛАПЛАСА.
P(t) = P0 +D*Hammasp(t/t;n+1;1;1), as interpreted by Microsoft.
Yes, the Gammarasp included in (18) describes the functions of the Laplace and many other distributions, but not the random variables themselves. This is a big difference that you apparently do not understand. Similarly, one could argue that Exp(-x^2/sigma) is the best white noise regression function because it describes its statistical (Gaussian) distribution. Bullshit!