Skip to content
All library documents

Testing CBOE Euro and Gold Volatility Indexes for XAUEUR Forecasting

Article MQL5 articles
Read the original

Summary

The article evaluates whether the CBOE Euro and Gold volatility indexes (EVZ and GVZ) add forecasting value for XAUEUR. It describes merging the indexes with daily XAUEUR OHLC data, imputing missing values with column means, and examining correlations, scatter plots, dimensionality-reduction visualizations, and autocorrelation. The authors report weak correlations between each volatility index and XAUEUR, a 0.4 correlation between the two indexes, and apparent clusters and volatility thresholds in visualizations.

Three deep neural networks compare market-only inputs, volatility-index inputs, and their combined set. Time-series cross-validation and a held-out test comparison favor the combined predictors, while ALE and SHAP do not exclude the alternative data. The article also describes model tuning and deployment in MetaTrader 5. Evidence is preliminary: the merged sample has about 3,000 rows, patterns may be misleading, and the reported modeling comparisons do not establish that the indexes will improve live trading results. The strategy trades predicted direction with weekly price confirmation and exits when the forecast changes.

Key ideas

  • The study tests whether EVZ and GVZ help forecast XAUEUR closing prices.
  • The indexes have weak individual correlations with XAUEUR but correlate 0.4 with each other.
  • Visualizations suggest possible clusters and volatility thresholds, though the sample is small.
  • A combined predictor set performs best in the reported model comparison.
  • The proposed EA confirms entries with weekly price direction and closes when the prediction reverses.

Tags

ForexCommoditiesVolatilityMachine learningBacktesting

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.