Discussing the article: "Forecasting Realized Volatility with Option-Implied Variance for Multiple Instruments in MQL5"

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Check out the new article: Forecasting Realized Volatility with Option-Implied Variance for Multiple Instruments in MQL5.

A published daily history of option-implied variance is combined with a broker symbol's daily bars to fit a HAR model of realized volatility in MQL5; the implied term enters the regression as a predictor, not as a ready-made forecast. Out-of-sample tests on four funds paired with their Cboe indices, ten US equities, and two broker symbols show where it improves the forecast. A background service refits on the broker's bars and draws a five-session price range on the chart, and a self-test matches independent C++ and Python implementations.

Most volatility models in a trading terminal use only the price history of the symbol on the chart. The options market adds a second view every day: the price it charges for protection over the next month. This article tests whether that view improves forecasts of realized volatility. It does: across four markets with Cboe volatility indices and ten US equities with option chains, a HAR model with an implied term beat plain HAR out of sample. In the markets and stocks tested, the gain was largest where price history alone forecast worst.

The design follows one line. Python supplies only what the terminal cannot measure, a daily history of option-implied variance. MQL5 fits the model on the broker's own bars and shows the scale of the expected move on the chart. None of it trades.

Author: Eugenio Gustavo Guilarte Homayden