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You could just go with a straight confidence interval. I'll explain it in terms of Gaussian/normal distribution, however, for professional use I'd take the extra steps to do bootstrapping and fitted some fat tail distribution. Select some time lag for your data. Calculate the rate of returns for each time step. Calculate the standard deviation and mean of ...

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As far as I know, technical analysis won't work to predict intraday Forex movement. I've done so many backtest using technical analysis but it doesn't have any predictive power. The best way to predict FOREX is to find the difference of interest rates issued by both government of that currency pair. $$Pn = P_0 . e^{(r_{jpy}-r_{usd}) \Delta t }$$  ...

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PYTHON I have found this class from the statsmodels library for calculating Garch models. Unfortunately, I have not seen MGARCH class/library. Below you can see the basic information about the garch models in mentioned class from the statsmodels. Probably you have to implement it by your own in python, so this class might be used as a starting point. ...

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Volatility is a difficult object and it is not always clear what we mean when we use the word volatility. I would make the following distinction as a first step: historical volatility: measuring the ex-post volatility of an asset/market/sector. You pick an observation period of interest (e.g. 3 months up to 3 years). You pick a frequency (often daily or ...

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In the very begining I advice you to model always linear effects in the time series (ARMA models). Then you add a model which investigate ARCH effects (GARCH family). When you have done the models estimation part It is advised to check if residuals of the models do not show any dependiencies ( close to normal distribution, independent). In another step you ...

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