Fear Moves Markets: Sentiment-Augmented POMP for Volatility Modeling of Bitcoin Returns
Abstract
Cryptocurrency markets exhibit extreme price swings and sentiment-driven regime shifts, which traditional volatility models often fail to capture. To address this, we develop a partially observed Markov processes (POMP) model augmented with sentiment and heavy-tailed distributions. Specifically, we extend Breto's framework by including the Fear and Greed Index (FGI) as an exogenous regressor in the latent volatility dynamics and replacing Gaussian measurement noise with a Student's t distribution. We fit the model via simulation-based inference using daily Bitcoin returns and FGI data from January 2020 to April 2025. Our approach significantly outperforms three benchmark models in log-likelihood and filter stability. Moreover, our model yields interpretable parameters consistent with known features of financial volatility. These results demonstrate that incorporating sentiment signals and heavy-tailed noise improves the modeling of volatility and regime shifts in cryptocurrency markets.
Related
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BibTeX
@misc{giridharan2026fear,
title = {{Fear Moves Markets: Sentiment-Augmented POMP for Volatility Modeling of Bitcoin Returns}},
author = {Abeyankar Giridharan and Chang Li and Suvrorup Mukherjee and Xinhe Wu},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.23250},
eprint = {2609.23250},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.23250},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Fear Moves Markets: Sentiment-Augmented POMP for Volatility Modeling of Bitcoin Returns
AU - Abeyankar Giridharan
AU - Chang Li
AU - Suvrorup Mukherjee
AU - Xinhe Wu
PY - 2026
DA - 2026/09/22
PB - arXiv
DO - 10.48550/arxiv.2609.23250
UR - https://arxiv.org/abs/2609.23250
AB - Cryptocurrency markets exhibit extreme price swings and sentiment-driven regime shifts, which traditional volatility models often fail to capture. To address this, we develop a partially observed Markov processes (POMP) model augmented with sentiment and heavy-tailed distributions. Specifically, we extend Breto's framework by including the Fear and Greed Index (FGI) as an exogenous regressor in the latent volatility dynamics and replacing Gaussian measurement noise with a Student's t distribution. We fit the model via simulation-based inference using daily Bitcoin returns and FGI data from January 2020 to April 2025. Our approach significantly outperforms three benchmark models in log-likelihood and filter stability. Moreover, our model yields interpretable parameters consistent with known features of financial volatility. These results demonstrate that incorporating sentiment signals and heavy-tailed noise improves the modeling of volatility and regime shifts in cryptocurrency markets.
ER -