A Comparative Analysis of Volatility Predictions via EGARCH, Transformer and Hybrid EGARCH-Transformer Models: Changes in Forecasting Ability after Introducing Sentiment Factors

The application of cryptocurrency asset volatility forecasting is relevant for real‑world risk‑management procedures and derivatives pricing in the digital‑asset market. This paper assesses out-of-sample forecast performance for three model families: standalone EGARCH, vanilla Transformer, and an EGARCH‑Transformer combined hybrid architecture for forecasting Bitcoin volatility. Another goal is to measure whether there is any actual marginal forecasting benefit from investor sentiment obtained from the Reddit posts. We use information from Reddit with the subreddit r/Bitcoin from 25th March 2021 to 30th December 2024. We collaborate with the open-source ProsusAI/finbert language model that has been trained for financial text tasks to construct a daily sentiment score based on the number of upvotes given to the post‑comment. In the hybrid model we constructed, the one-step-ahead conditional-variance values produced by EGARCH are used as other features as input to the Transformer network. The results from controlled feature-ablation tests show that, in general, using structured volatility inputs (volatility realized from volatility inputs) and EGARCH conditional volatility outputs yield better forecasts. Social media sentiment data of investors, by comparison, has mixed results. Under specific combinations of features, sentiment boosts the accuracy of predictions, but in other experimental settings, it hinders the accuracy of predictions. We have problems consistently filtering subjective signals from forum text by retail investors with our hybrid EGARCH‑Transformer implementation. Results indicate that the performance of hybrid models is much more influenced by the composition of the input features and quality of the raw data than by adding additional predictor variables to the network.