A Framework for Cryptocurrency Volatility Prediction Based on Cross-Correlation Analysis Using Deep Learning

The popularity of cryptocurrencies has intensified the need for accurate volatility prediction models. This research proposes a novel approach to enhance conditional variance predictions for cryptocurrencies. By leveraging a feature selection technique that selects strong features based on price, return, and volatility cross-correlation analysis, we effectively select the most relevant features for an LSTM-based prediction model. To obtain initial volatility estimates, various GARCH family models (GARCH, EGARCH, and GJR-GARCH) were fitted to the dataset, with the best-fitting model selected based on minimum MSE and RMSE. Subsequently, the proposed MGF-LSTM model was applied to the top eight cryptocurrencies by market capitalization. Experimental results demonstrate that our model significantly reduces prediction errors, providing valuable insights for risk management and investment decision-making in the cryptocurrency market.