Discussing the article: "Beyond GARCH (Part VII): Monte Carlo Volatility Forecasting in MQL5"

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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