Why is the normal distribution not normal? - page 6
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EURUSD M15 chart built in Excel.
Row1 - based on Open-Close data. Row2 - normal distribution with the same variance and mo
Why should the measured distribution be close to a normal distribution?
Why do so many people here use the "returns" distribution? It is almost impossible to use it afterwards. What good is it if it resembles a normal and stationary distribution?
Why isn't the price distribution used? After all, this is the main thing that is interesting.
What's so interesting about the price? Price is the sum of returns. The sum of normal distributions is a normal distribution.
In fact, returns is closer to a stable distribution than a normal distribution, but the rule works exactly the same way: the sum of the stable distributions is a stable distribution. The normal distribution is only a special case of the stable distribution.
I have the following idea. Let's go back to the mathematics: for a transaction the buyer and the seller must agree, let's assume that both parties agree 1 or disagree 0 in a uniform distribution then the sum of the two uniform distributions will result in a normal distribution,
but while the ticks are traded there is no need to move the quotes if people trade at this price, i.e. a tick appears when there are no deals, so the tick is opposite in probability to the deal and consequently the law of tick distribution is opposite to the normal distribution.
ps that by the way can be seen above on the diagram in Avals post on the intersection of functions.
pps Avals I wonder what you get if you subtract one from the other? It seems to me it would be a wavelet-like attenuation.
I have the following idea. Let's go back to the mathematics: for a transaction the buyer and the seller must agree, let's assume that both parties agree 1 or disagree 0 in a uniform distribution then the sum of the two uniform distributions will result in a normal distribution,
but while the ticks are traded there is no need to move the quotes if people trade at this price, i.e. a tick appears when there are no deals, so the tick is opposite in probability to the deal, i.e. the law of tick distribution is opposite to the normal distribution.
If I were a Japanese businessman and you were my employee, I'd give you a bonus for the very act of thinking.
// Put it on your tab. We'll get even in the next life. :)
But something is still missing in this logic. Arbitrage is not considered, and in vain. It seems to me that it is he who determines the picture.
No paper can move without a response from other papers. And the feedbacks are negative and multiplicative.
Hence the pictures.
If I were a Japanese businessman and you were my employee, I would give you a bonus for the very fact that your mind was wiggling.
// Put it on your tab. We'll get even in the next life. :)
However, there is something else missing from this logic. Arbitration has been left out of the equation, and for good reason. I think it's the arbitrage that defines the picture.
No piece of paper can move without a response from other pieces of paper. And the feedbacks are negative and multiplicative.
Hence the pictures.
You think it's time to move from complete randomness to interdependencies,
they certainly exist, but how do they manifest themselves? I don't think that the elementary model is very complicated.
well, you're asking a lot of questions :o)
I almost agree, but please explain how to get a parabola in the way suggested.
What's there to explain. If you remember the Gaussian probability density function, you just need to logarithm it and look at the graph of the logarithm of the probability density function. It's a pure parabola.
The price series is not stationary.
That is, its expectation is known only in Sochi. There they use just the price distribution. Only they do not write about the results here, the bastards.
// Make the travel arrangements. You can tell us about it later.
In other cities, as well as in our rural areas, they are content with what it costs - the first differences.
I like the term "contentment". Well, actually, you could live in Moscow and be content with the weather forecast in Vladivostok. But what use is it to you?
Some time ago there was no analysis and stationary noise processes. When the need arose, the apparatus for such analysis was created. More precisely, for a number of special cases.
How can you use first difference statistics without having MO prices?
In my opinion, the price is identical to and fully recoverable from its first difference, so whatever is convenient for research is used,
I don't think there is any useful information in the cumulative sum (price) distribution.
The price is certainly recoverable from its first differences. Why would you want to reconstruct it, you already know it.
But what is the relationship between first difference statistics and price series statistics?
The price is certainly recoverable from its first differences. Why would you want to reconstruct it, you already know it.
But what is the relationship between first difference statistics and price series statistics?
The same as between a derivative and its function .
What is there to explain? If you remember the Gaussian probability density function, all you have to do is logarithm it and look at the graph of the logarithm of the probability density function. It's a pure parabola.
Why people bothered so much with "forex distribution" :) In my memory so much bewilderment about its abnormality....
Why are people getting so steamed up about the "forex distribution" :) In my memory so much bewilderment about its abnormality....
I think, that all see that it is not normal and all understand it, but what follows from it nobody can understand, for example, how to recreate this kind of distribution, and from here we can dance to the filters.