Is that a good way to work with the ARMA model?

External question — Quantitative Finance Stack Exchange Author: David Hoareau Original post: https://quant.stackexchange.com/questions/22435 License: CC BY-SA 3.0 — https://creativecommons.org/licenses/by-sa/3.0/ Adaptation: HTML converted to plain text; contact email addresses removed. I would like to share with you what I am doing to get your point of view, and to make a better trading system in collaboration. I am working on EURUSD forex, and I am trying to find a way to place order based on ARMA modelling. Collecting Data, Data transformation, and Model fitting: I am collecting each EOD Close of EURUSD instrument, and I calculate the log differencing to transform this time series in a stationary process. Then, using Box Jenkins, I fit the parameters of the ARMA Model. Residuals Analysis: After the model fitted, I analyze the residuals of the model. The process of the model residuals is a stationary process and follows a normal distribution. Trading Strategy: Like the model residuals is a normal distribution, I calculate the cumulative probability. Then I am able to display on the chart with a minor timeframe H4 or H1 what will be the tomorrow position with their respecting probability based on volatility max of 2 standards deviations: Is that a good way to work with the ARMA model? What do you think about this strategy? Can we improve it together? Thanks David

External answer — Quantitative Finance Stack Exchange Author: Malick Original post: https://quant.stackexchange.com/a/22456 License: CC BY-SA 3.0 — https://creativecommons.org/licenses/by-sa/3.0/ Adaptation: HTML converted to plain text; contact email addresses removed. To improve your model I would recommend you to take into acount the intraday periodicity : ie the fluctuation of the exchange rate over the daily cycle. For instance we observe strong increase on the volatility around 07:00 GMT (opening of European Market.) The following image taken from Andersen, T. G., & Bollerslev, T. (1997) illustrates it. It is the average (across several days) absolute returns of the 5 min interval for DM-$ . The drop between intervals 40 and 60 corresponds to the lunch hour in the Tokyo and Hong Kong markets. So we know that at some periodic time of day the volatility will decrease or increase for sure . You need to take it into account to improve your model. If you do not your forecast will be poor because in some way you are assuming a constant volatility over the day which is not true. Obviously the observed periodicity will depends of your timeframe. As a basic strategy to tackle this fact, you can apply your ARMA model on a "de-periodicitized " returns serie...

Is that a good way to work with the ARMA model?

Is that a good way to work with the ARMA model?

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