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Bitcoin does not reliably rise when rates fall or fall when rates rise. Its measured response depends on which rate you use, whether the move was expected, the return horizon, and what other market forces are controlled for. Some studies find weak or insignificant reactions to monetary-policy news; others find nonlinear or time-varying relationships.
To evaluate sensitivity properly, define a specific estimand—such as Bitcoin’s 30-minute return after an unexpected 25-basis-point change in the expected policy path—then use aligned returns, a credible surprise measure, relevant controls such as equity performance and VIX, uncertainty estimates, and multiple robustness checks.
What “interest-rate sensitivity” should mean
The phrase can describe several different relationships. They are not interchangeable, and combining them can produce a misleading conclusion.
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The federal-funds target is the central bank’s administered policy rate. A model might ask whether Bitcoin returns move when that target changes. Because target changes are infrequent and widely anticipated, a simple before-and-after comparison mixes the policy decision with everything else happening in markets.
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Expected policy path
Investors also price the expected path of future policy. An announcement can leave the current target unchanged while materially changing expectations for later meetings. A Bitcoin response to that path revision is a different exposure from a response to the current target.
Market yields and bond returns
A 10-year Treasury yield or Treasury-bond return reflects growth, inflation, term-premium and policy expectations over a long horizon. It is not the same variable as the federal-funds target and cannot be treated as a direct measure of a monetary-policy shock.
Policy surprise
A surprise is the portion of an announcement that markets did not already expect. A useful estimand states all of its ingredients: the Bitcoin return, the rate concept, the unexpected component, the time window and the unit of the shock. For example: the change in Bitcoin’s return associated with an unexpected 25-basis-point increase in the expected policy path during the 30 minutes after an announcement.
Why an announced rate move is not the same as a surprise
Markets can price a hike or cut well before the meeting. If the central bank delivers exactly what futures markets implied, the announcement may contain little new information even though the target rate changes.
In a foundational 2000 New York Fed study, Kuttner separated expected and unexpected federal-funds moves with federal-funds futures. Bond yields reacted little to anticipated changes but moved strongly and significantly when the policy move differed from expectations. The implication for Bitcoin analysis is direct: a regression on the announced target change can estimate a mixture of anticipation and surprise, not a clean causal response to new information.
For an event study, obtain or construct a market-implied surprise measure and timestamp it to the announcement. If only the realized target change is available, label the result as an association with the realized change rather than an identified policy shock.
What published studies actually show
The studies below answer different questions. Their estimates should be compared by rate variable, identification, horizon, sample and controls—not reduced to a single statement that Bitcoin is or is not rate-sensitive.
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| Study | Rate exposure and design | Horizon and sample | Reported result | Status or limitation |
|---|---|---|---|---|
| Benigno and Rosa, New York Fed Staff Report No. 1052 (2023) | Intraday responses to monetary-policy and macroeconomic surprises: Target, Path and LSAP dimensions | Bitcoin sample beginning in 2017; narrow announcement windows | Muted Bitcoin response to monetary and macro news in the reported specifications | Short sample; authors say more evidence is needed |
| Drexler, Guettler and Sun, Chicago Fed Working Paper 2026-16 | Time-varying beta to NASDAQ, Dow Jones and 10-year Treasury-bond returns | Long sample with regime comparisons around 2020 | Equity exposure rises and becomes statistically positive around 2020; Treasury-return beta is not distinguishable from zero | Working paper; Treasury returns are not a direct federal-funds surprise |
| Savrul, Journal of Economic Policy Researches (2026) | Returns around scheduled FOMC announcements, with realized rate moves, hike/cut/hold categories, VIX and dollar controls | 43 announcements from 2021 through 2026 | Little systematic difference from zero around announcements; VIX changes are more consistently related to crypto returns in several windows | Available surprise measure has only two nonzero observations |
| A note on the Bitcoin and Fed Funds rate, PLOS ONE (2022) | Longer-frequency nonlinear and spillover models | January 2015 through February 2021 | Nonlinear effects and temporarily strong spillovers are reported | Different horizon and model from the intraday event study |
New York Fed intraday evidence
Gianluca Benigno and Carlo Rosa’s February 2023 New York Fed work examined whether Bitcoin moved when macroeconomic information arrived. For monetary policy, the authors separated unexpected changes in the current federal-funds target (Target), revisions to the future policy path (Path) and announcements of future large-scale asset purchases (LSAP).
In the reported specifications, Bitcoin’s monetary-news responses were never significant at the 1 percent level. The authors’ explainer describes the response as muted and says Bitcoin was orthogonal to the monetary and macroeconomic news in the studied sample. Their examples include the June 2016 employment report, when Bitcoin moved sideways while other assets reacted, and the June 2021 FOMC meeting, when officials signaled rates might need to rise sooner and faster than expected without a systematic Bitcoin response.
“Given the short sample used in the analysis, the jury is still out on this one, and more evidence is needed to put the case to rest.”
That qualification matters. A statistically insignificant estimate means the study did not identify a reliably nonzero response under its model and sample; it does not prove that Bitcoin’s true response is exactly zero or that a later sample will behave the same way.
Chicago Fed evidence on broad market exposures
An August 2026 working paper by Alejandro H. Drexler, Andre Guettler and Angela Sun studies time-varying Bitcoin betas to the NASDAQ, Dow Jones index and 10-year Treasury-bond returns. The authors report that Bitcoin’s equity exposure rises substantially and becomes statistically larger than zero around 2020. After controlling for Dow Jones exposure, the NASDAQ exposure is small and statistically insignificant in their sample. Bitcoin’s beta to Treasury-bond returns is not distinguishable from zero.
This is useful evidence about broad asset exposure, not a direct test of an unexpected federal-funds decision. The paper is also a working paper. The Chicago Fed notes that working papers are not edited and that opinions and errors belong to the authors, not necessarily to the Bank or the Federal Reserve System.
FOMC-window evidence from 2021–2026
Mesut Savrul’s 2026 article examines 43 scheduled FOMC announcements from 2021 through 2026. It reports little evidence that crypto returns differ systematically from zero around those announcements, and it finds no robust explanation from realized rate changes, hike/cut/hold labels or asymmetric move sizes.
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Changes in the Cboe Volatility Index (VIX) are negatively associated with crypto returns in several event windows, while dollar-index effects are weak and unstable. The study’s available surprise measure contains only two nonzero observations, so its results do not establish a robust test of policy surprises. This design illustrates why a rate-only interpretation can be wrong: the market’s risk repricing may matter more than the realized target move.
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Contrasting nonlinear evidence
The 2022 PLOS ONE paper A note on the Bitcoin and Fed Funds rate covers January 2015 through February 2021. It reports nonlinear effects and temporarily strong spillovers between federal-funds rates and Bitcoin. Those findings do not contradict the New York Fed’s intraday result because the papers estimate different objects at different frequencies. A relationship can be weak immediately after announcements yet appear in slower-moving or regime-dependent data, or the reverse.
A practical framework for evaluating sensitivity
1. State the estimand before collecting data
Write one sentence specifying the asset, rate exposure, surprise concept, horizon and unit. For example: “I estimate the percentage-point change in Bitcoin’s 30-minute return associated with an unexpected 25-basis-point increase in the expected policy path.”
- Asset: define the Bitcoin price series, venue or index and return convention.
- Rate concept: identify the current target, expected path, Treasury yield, bond return or another shock.
- Shock: state whether the variable is realized, market-implied or otherwise identified as unexpected.
- Horizon: specify minutes, hours, days, weeks or a longer interval.
- Unit: say whether the coefficient is a Bitcoin percentage return per basis point, per standard-deviation shock or a beta.
2. Select a rate measure that matches the question
Do not substitute a convenient series for the exposure you intend to study. A federal-funds target change answers a different question from a 10-year Treasury return. A path surprise captures future-policy information that a current-target series can miss. If your data do not support the intended measure, narrow the claim rather than relabeling the available variable.
3. Choose the horizon and design
A narrow intraday event window can limit contamination from unrelated news when the announcement time and surprise measure are credible. Daily or weekly models capture slower co-movement but expose the estimate to earnings news, macro releases, market stress, serial dependence and regime changes.
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4. Work with aligned returns, not price levels
For ordinary sensitivity estimates, calculate Bitcoin returns and changes or surprises in the selected rate variable over matching intervals. Trending price levels can create spurious correlation. Align timestamps and time zones, account for the fact that Bitcoin trades continuously while many policy markets have opening hours, and document how you handle missing, stale or asynchronous observations.
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A simple specification can be written as r_BTC,t = α + βS_t + γ′X_t + ε_t, where S_t is the rate surprise and X_t contains controls. The coefficient β is meaningful only with the sampling interval, units and identification stated beside it.
5. Add plausible competing drivers
At minimum, consider broad equity returns and a risk-sentiment measure. The Chicago Fed results show that Bitcoin’s equity exposure has changed over time, while Savrul finds VIX changes associated with crypto returns in several windows. Depending on the design, the dollar and scheduled macroeconomic releases may also belong in the control set.
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Controls should reflect a causal story, not a long list added mechanically. A short event sample cannot support dozens of regressors without unstable estimates. Explain why each control is included and whether it is measured before or after the announcement.
6. Report magnitude and uncertainty together
Publish the coefficient or event-window return, standard error or confidence interval, sample dates, number of observations or events and the significance threshold. “Not statistically significant” means the data and model did not distinguish the estimate from zero with the stated precision; it is not proof of no effect.
Also report economic size. A statistically detectable coefficient may be too small to matter after normal Bitcoin volatility and trading costs. Conversely, a wide confidence interval can include economically important moves even when the point estimate is not significant.
7. Run robustness checks
- Replace the rate measure with a credible alternative, such as current-target, path or Treasury exposure, while keeping the estimand explicit.
- Compare several windows, such as 30 minutes, two hours and one day, to test whether the reaction is immediate or delayed.
- Re-estimate with and without equity, VIX and dollar controls.
- Split the sample into pre-2020 and post-2020 regimes or other economically justified periods.
- Test whether a few extreme Bitcoin observations drive the result.
- Compare hikes, cuts and holds, but separate the realized direction from the surprise relative to expectations.
- Use appropriate standard errors for serial dependence or overlapping observations in slower-frequency models.
If conclusions change materially across these checks, report the instability instead of selecting the most favorable specification.
8. Keep the conclusion proportional to identification
Use “in this sample, under this specification” when describing an estimate. A raw correlation does not establish that policy caused the Bitcoin move. An event-window estimate tied to a credible surprise is closer to a causal response, but it can still be affected by simultaneous information or an imperfect surprise measure. A beta to Treasury returns describes co-movement, not a guaranteed reaction to a central-bank decision.
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How to diagnose a claimed Bitcoin–rate relationship
Check the rate variable
Ask whether the author used a target rate, expected path, surprise, yield or bond return. If the label changes between the methods and the conclusion, the claim is not precise enough to evaluate.
Check what was known at the time
Determine whether the move was anticipated. A scheduled hike that matches futures pricing may contain little new information, whereas a guidance change can surprise markets without changing the current target.
Check the event count and sample regimes
Small event samples produce imprecise estimates. Bitcoin’s market structure and institutional participation have changed over time, so a result spanning multiple regimes may hide a time-varying beta. Savrul’s 43-announcement sample and the New York Fed’s short Bitcoin sample should not be treated as equally informative for every question.
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Check competing risk channels
If VIX or equity returns explain the Bitcoin move and the rate coefficient disappears, the initial rate relationship may have been a proxy for broad risk sentiment. If the rate coefficient remains after controls, that is stronger evidence of a distinct association, though not automatically proof of causality.
Check uncertainty, not just the sign
A positive point estimate with a confidence interval spanning zero does not establish that Bitcoin rises after rate news. A negative estimate with a narrow interval supports a more precise sample-specific statement, but still does not create a universal trading rule.
Common analytical mistakes
- Using levels: correlating Bitcoin’s price level with a trending rate level can create a spurious relationship.
- Ignoring expectations: treating every hike or cut as new information confuses anticipation with surprise.
- Equating Treasury returns with policy shocks: long-term bond returns include many forces beyond the current policy target.
- Omitting risk controls: a risk-on or risk-off move can be misattributed to rates.
- Overfitting a short event sample: too many controls can make estimates unstable.
- Generalizing one regime: an estimate from one period may not apply after Bitcoin’s market exposure changes.
- Reading insignificance as immunity: failure to reject zero is not evidence that the true effect is exactly zero.
- Turning association into a forecast: even a well-identified historical response does not guarantee the direction of the next announcement move.
What a defensible conclusion looks like
A careful write-up might say: “In the specified intraday sample, Bitcoin’s return was not statistically distinguishable from zero after the identified monetary-policy surprises. The estimate is uncertain and does not rule out slower, nonlinear or regime-dependent relationships. Equity and risk-sentiment controls are necessary because Bitcoin’s broader market exposures can move with the same announcements.”
It should not say that Bitcoin is immune to interest rates, that every hike causes a loss, or that every cut causes a gain. The evidence spans an intraday New York Fed event study, a Chicago Fed working-paper beta analysis, FOMC-window results and a nonlinear PLOS ONE model. Those findings are informative precisely because they test different horizons and exposures; they cannot be collapsed into a universal percentage move.
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No cited study establishes a general-population statistic for how many percentage points Bitcoin moves after a given rate increase. The Chicago Fed paper does not provide one headline numerical beta in its abstract, and its Treasury comparison is not a direct federal-funds shock. The New York Fed authors explicitly caution that their Bitcoin sample is short. Savrul’s surprise series contains only two nonzero observations, limiting what can be learned about unexpected policy changes from that design.
The strongest practical answer is therefore conditional: Bitcoin may co-move with rates, bonds, equities or risk sentiment in particular periods, but the direction and size are not stable enough to serve as a rule of thumb. A credible evaluation names the exposure, identifies what was unexpected, aligns the horizon, controls for competing risks, reports uncertainty and tests whether the result survives reasonable alternative specifications.
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