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

Tomiwa Sunday Adebayo and Dervis Kirikkaleli

Journal of Applied Mathematics, 2026, vol. 2026, 1-15

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.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnljam:7926803

DOI: 10.1155/jama/7926803

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