Bitcoin Comovement With AI Equities Beyond Equity Risk Sentiment: Evidence From Multiscale Quantile‐on‐Quantile Partial Correlation

This study assesses whether Bitcoin’s linkage with AI equities remains robust after accounting for equity risk sentiment. To this end, the study employs the multiscale quantile‐on‐quantile correlation (MSQQC) and multiscale quantile‐on‐quantile partial correlation (MSQQPC) approaches, using daily data covering 02/01/2019–16/06/2025. The results indicate that BTC–AI comovement is strongly state‐ and frequency‐dependent rather than stable across the joint distribution or across horizons. In the high‐frequency band, dependence is weak and only intermittently significant, with localised negative regions around BTC ≈ 0.20 with AI ≈ 0.30–0.50 and BTC ≈ 0.30 with AI ≈ 0.70. In the mid‐frequency band, significance concentrates in the tails, showing negative dependence under downside stress conditions such as BTC ≈ 0.10–0.30 with AI ≈ 0.10, alongside sign changes when BTC is in upper‐tail states. In the low‐frequency band, dependence becomes broadly positive and significant across most quantile combinations, with limited decoupling when AI is highly elevated (≈ 0.80–0.90) and BTC is also in upper quantiles (≈ 0.70–0.90). Importantly, conditioning on VIX and VVIX does not materially alter these patterns, suggesting that sentiment influences segments of short‐run dependence but does not overturn the longer‐run BTC–AI linkage. The study derives policy recommendations from these findings.

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Publication Details

Journal
Journal of Applied Mathematics
Published
2026-01-01
DOI
https://doi.org/10.1155/jama/7926803
Primary Topic
Blockchain Technology Applications and Security
Type
article
Field-Weighted Citation Impact
0.00
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article

Bitcoin Comovement With AI Equities Beyond Equity Risk Sentiment: Evidence From Multiscale Quantile‐on‐Quantile Partial Correlation

Tomiwa Sunday Adebayo, Dervis Kirikkaleli
Journal of Applied Mathematics
Blockchain Technology Applications and Security
article

Bitcoin Comovement With AI Equities Beyond Equity Risk Sentiment: Evidence From Multiscale Quantile‐on‐Quantile Partial Correlation

Tomiwa Sunday Adebayo, Dervis Kirikkaleli
article en

Abstract

This study assesses whether Bitcoin’s linkage with AI equities remains robust after accounting for equity risk sentiment. To this end, the study employs the multiscale quantile‐on‐quantile correlation (MSQQC) and multiscale quantile‐on‐quantile partial correlation (MSQQPC) approaches, using daily data covering 02/01/2019–16/06/2025. The results indicate that BTC–AI comovement is strongly state‐ and frequency‐dependent rather than stable across the joint distribution or across horizons. In the high‐frequency band, dependence is weak and only intermittently significant, with localised negative regions around BTC ≈ 0.20 with AI ≈ 0.30–0.50 and BTC ≈ 0.30 with AI ≈ 0.70. In the mid‐frequency band, significance concentrates in the tails, showing negative dependence under downside stress conditions such as BTC ≈ 0.10–0.30 with AI ≈ 0.10, alongside sign changes when BTC is in upper‐tail states. In the low‐frequency band, dependence becomes broadly positive and significant across most quantile combinations, with limited decoupling when AI is highly elevated (≈ 0.80–0.90) and BTC is also in upper quantiles (≈ 0.70–0.90). Importantly, conditioning on VIX and VVIX does not materially alter these patterns, suggesting that sentiment influences segments of short‐run dependence but does not overturn the longer‐run BTC–AI linkage. The study derives policy recommendations from these findings.

Journal of Applied MathematicsVol. 2026(1)
European University of Lefke (TR), Azerbaijan State University of Economics (AZ), Vigo (Poland) (PL), Lebanese American University (LB), Near East University (CY)
Openalex Percentile: Top 11%
Blockchain Technology Applications and Security
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Bitcoin Comovement With AI Equities Beyond Equity Risk Sentiment: Evidence From Multiscale Quantile‐on‐Quantile Partial Correlation — Tomiwa Sunday Adebayo, Dervis Kirikkaleli · Journal of Applied Mathematics (2026) | TGRS Research Map | TGRS