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Predicting Bitcoin Implied Volatility from Market Data and Sentiment

Article arXiv papers · Author: Faizaan Pervaiz et al.
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Summary

The paper examines whether Bitcoin implied volatility over a five-minute horizon can be predicted from recent market behavior and alternative data. Its model uses lagged Bitcoin index prices and volatility movements, capturing both price information and volatility momentum. It also incorporates sentiment and engagement measures, including Google Trends, to investigate whether attention-related signals contain information about subsequent implied volatility.

The reported finding is modest predictability, with markets often responding to Google Trends signals several hours later. This suggests that the relationship between online attention and market volatility may involve a delay rather than an immediate response. The document offers a brief description of the approach and headline result, but provides no model specification, sample period, validation design, quantitative performance measures, or trading evaluation. The finding therefore does not establish that the signals are profitable or that the observed predictive relationship will persist across periods or market conditions.

Key ideas

  • Bitcoin implied volatility at a five-minute horizon is reported to be modestly predictable.
  • The model uses lagged Bitcoin prices and volatility movements, including volatility momentum.
  • Google Trends and other sentiment or engagement data are included as alternative predictors.
  • The reported market response to Google Trends often occurs several hours later.
  • The brief description gives no validation details or evidence of trading profitability.

Tags

CryptoVolatilitySentimentStatistics
Full text
# Fear and Volatility in Digital Assets


# Fear and Volatility in Digital Assets









We show Bitcoin implied volatility on a 5 minute time horizon is modestly predictable from price, volatility momentum and alternative data including sentiment and engagement. Lagged Bitcoin index price and volatility movements contribute to the model alongside Google Trends with markets responding often several hours later. The code and datasets used in this paper can be found at https://github.com/Globe-Research/bitfear.

Shown in full with attribution under the source's licence. Licence: abstract CC0

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