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There are two types of dispute - a dispute that gives rise to the truth and a dispute (betting) for material gain.
In the second case, it is important to define the subject of the dispute so that it is the same for both parties. The most common mistake is to argue with each side's "undeniable" truth, and the truths do not coincide. If there is one truth and it is defined, then the criteria for "truthfulness" must be agreed upon. This is the most important stage of the dispute, because after this preparatory storm, the subject of the dispute disappears (it is obvious - the parties come to a consensus). If a dispute nevertheless takes place, it indicates the apparent dementia of one of the parties with all the ensuing unpleasant consequences.
On this basis, I never argue "for money".
Who will carry out a comparative analysis of the returns series suggested in the file and eurusd series?
Immediately after the analysis, I will post how the synthetic series was obtained (not for the sake of luring, but just for objectivity).
Attached is a file with 20000 data
In my haste I forgot to upload. :о)
PS series is presented in pips i.e. in whole numbers
Well nevertheless, who will carry out a comparative analysis of this series and the real first difference?
there is a contaminated .csv
The difference between the two processes is that synthetic has constant volatility, like white noise, while the euro has volatility autocorrelation.
The difference between these two processes is that the synthetic one has constant volatility, like white noise, while the Euro has a volatility autocorrelation.
It doesn't seem to be a constant, but it's too little different, or is it still a constant?
Okay, as promised, I'll show you my cards:
HGS produces two numbers check for even odd and assign accordingly 0 even, 1 odd
Then if both numbers are 11, we add 0.
______________ if 10 then 1
______________ if 01 then -1 and
______________ if 00, then the previous number *2
thus firstly it is skewed in the direction of overshoot, hence long tails and in the direction of zero, since three outcomes will add up to zero.
This example is a rough illustration of how a market maker works. If 11, then people agree to trade at this price; if 10, then it moves in the direction of the order; if 01, then it moves in the direction of the order; if 00, then no order, then it moves two steps in the direction of the previous price.
Plot the tangent of the slope of the straight line drawn using the method of least squares through the cloud of data where the increments of the price series are plotted along the axes. If there is no dependence between increments, the line lies horizontally and its slope angle tangent equals (nearly) 0. If the dependence is 100%, then it is 1 (or 45 degrees of slope). If the dependence is counter-directional, the sign of the coefficient is negative.
Let's construct the RRR for minutes and find the correlation coefficient (CC). For the EURCHF series it is -0.4. There is a dependence (of some kind). Let's carry out the same for two-minute bars. Then for 3-minute bars and so on. We obtain the following dependence:
You can see at once that there is dependence between adjacent samples on all TFs up to TF=500 min. and this dependence decreases with increasing TF.
Now let's speak separately about statistical significance of the obtained result. What is statistical significance is available here.
That's all for now - my wife is driving me to bed!
(pensive) Where's the "bath time" button?
Friends, just don't react, they'll get off. I've done it.
(pensive) Where's the "bath time" button?
Friends, just don't react, they'll get off. I'm telling you.
And the button is sorely lacking. >> I agree.
Neutron писал(а) >>
...
it is -0.4. There is a correlation (of some kind).
...Ultima ratio regum
:о)And the button is sorely lacking. I agree.
Yeah, make a button and give it to everyone.
on the GARCHsub-heading for modelling stock index volatility