Those cartoons again—people simply can’t take in important technical information that quickly. If you really want to make it more like a presentation, you need control buttons so you can view each slide, not a pointless GIF.
Stanislav, you really don’t need to comment on every article about this. This series was produced ages ago, and they’re only just being translated into Russian.
You posted a comment in English in the Russian section of the forum. This causes problems for the automatic translator, which creates duplicates of your comments in other language sections of the forum.
There is no need to fix anything, let's leave everything as is this time. However, in the future, please be more careful and ensure that the language of your comment matches the forum section you have selected.
You posted a comment in English in the Russian section of the forum. This causes problems for the automatic translator, which creates duplicates of your comments in other language sections of the forum.
There is no need to fix anything, let's leave everything as is this time. However, in the future, please be more careful and ensure that the language of your comment matches the forum section you have selected.
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Check out the new article: Beyond GARCH (Part VII): Monte Carlo Volatility Forecasting in MQL5.
We implement the CMonteCarlo module that turns the fitted MMAR parameters into a volatility forecast via Monte Carlo. It runs N independent simulations over a chosen horizon and reports mean, median, standard deviation, and a percentile-based 95% confidence interval, with access to per-run values if needed. Adaptive cascade depth selects the minimal k such that b^k covers the horizon, keeping the run fast and consistent.
In Part 6, we built the simulation engine that generates a single MMAR price path. One path is interesting for visualization but useless for forecasting. A single random draw tells you nothing about the distribution of possible futures. To produce a volatility forecast — a point estimate with uncertainty bounds — we need to run the simulation engine many times and aggregate the results. This is the Monte Carlo method: run N independent simulations, measure the volatility of each, and extract statistics from the resulting distribution.
This article builds the CMonteCarlo class, the final computational module before the top-level facade. It takes the fitted model parameters, runs hundreds or thousands of independent MMAR simulations over a specified forecast horizon, and produces a complete forecast result: mean volatility, standard deviation, median, and a 95% confidence interval. The output answers the question that matters for trading: what is the expected volatility over the next N bars, and how certain are we?
Author: Muhammad Minhas Qamar