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May 30, 2026Journal of risk and financial managementOpen Access

Modelling Asymmetric Volatility and Sentiment Effects: Forecasting Accuracy in the Crypto Market

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Authors

AGArdit GjeçiAKAndromahi KufoRTRovena Vangjel Troplini

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Overview

Randomized trial examines forecasting accuracy of asymmetric GARCH models in cryptocurrencies, indicating varying performance across assets.

Key Points

  • This research aims to evaluate the effectiveness of asymmetric GARCH models in forecasting cryptocurrency volatility.
  • Analyzed the returns of seven major cryptocurrencies: BTC, ETH, ADA, XRP, LTC, XLM, DASH.
  • Utilized the Crypto Fear & Greed Index as a dummy variable over a simultaneous active period for all cryptocurrencies.
  • Compared EGARCH and GJR-GARCH models based on in-sample and out-of-sample metrics.
  • EGARCH model consistently outperformed GJR-GARCH in in-sample metrics for all assets.
  • The use of CFDI improved results for only three cryptocurrencies, indicating potential noise in the model for others.
  • Out-of-sample metrics showed better performance for normal and GJR-GARCH models on specific cryptocurrencies.

Cite This Study

Gjeçi et al. (2026) studied this question.

synapsesocial.com/papers/6a1a827f0307b785094341fehttps://doi.org/10.3390/jrfm19060390
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