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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallBitcoin does not reliably rise when rates fall or fall when rates rise. Its apparent rate sensitivity depends on which interest-rate measure you use, whether the move was expected, the return horizon, and how well you control for equity-market and risk-sentiment shocks. A credible evaluation therefore tests a clearly defined rate exposure rather than relying on a chart of Bitcoin and the federal-funds rate.
What “interest-rate sensitivity” should mean
Before calculating anything, define the response you want to measure. “How Bitcoin reacts to rates” can refer to several different questions:
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- Does Bitcoin’s intraday return change after an unexpected Federal Reserve announcement?
- Does Bitcoin move with Treasury yields over days or weeks?
- Is Bitcoin’s average return different during hiking, cutting, and holding cycles?
- Does a revision to the expected future policy path matter more than a change in today’s target rate?
These are different estimands. A useful statement might be: “the change in Bitcoin’s return associated with an unexpected 25-basis-point increase in the expected policy path over a 30-minute window.” That sentence identifies the asset, rate concept, surprise, size and horizon. Without those details, a reported “Bitcoin rate beta” is difficult to interpret.
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Rate changes, rate surprises and bond returns are not interchangeable
Observed policy changes
The Federal Reserve may raise or lower its current federal-funds target. The announced change is an observed policy move, but markets may have expected it. A fully anticipated hike can produce little new information when the announcement arrives.
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Unexpected policy news
An event study usually seeks the unexpected component of an announcement: news that changes the market’s estimate of the current target or the future policy path. Federal-funds futures can be used to separate anticipated and unanticipated movements. Kuttner’s New York Fed study found that anticipated policy changes produced essentially no bond-yield response, while unanticipated movements produced large, highly significant responses. The same distinction is essential when testing Bitcoin.
Treasury yields and bond returns
A 10-year Treasury yield or bond return reflects long-term growth, inflation, term-premium and policy expectations. It is not the same exposure as a surprise in the federal-funds target. A regression of Bitcoin on Treasury bond returns can describe co-movement with the bond market, but it does not by itself identify a causal response to a Fed decision.
What published evidence actually finds
| Study | Rate or shock measure | Horizon and sample | Main reported result | How to interpret it |
|---|---|---|---|---|
| Benigno and Rosa, New York Fed Staff Report No. 1052 (February 2023) | Intraday monetary-policy and macroeconomic news; Target, Path and LSAP surprises | Bitcoin sample beginning in 2017; high-frequency event study | Bitcoin was described as orthogonal to monetary and macroeconomic news; responses were muted and never significant at the 1% level in the reported specifications. | Evidence about immediate announcement reactions in a relatively short sample, not proof that every rate effect is zero. |
| Drexler, Guettler and Sun, Chicago Fed Working Paper 2026-16 (August 2026) | Betas to NASDAQ, Dow Jones and 10-year Treasury bond returns | Time-varying beta analysis across market regimes | Bitcoin’s equity exposure rose substantially and became statistically larger than zero around 2020. Its beta to Treasury bond returns was not distinguishable from zero; NASDAQ exposure became small and insignificant after controlling for Dow Jones exposure. | Broad-market exposure can explain apparent rate co-movement. This is not a direct test of a federal-funds surprise. |
| Savrul, Federal Reserve Rate Decisions, Risk Sentiment, and Cryptocurrency Returns: Evidence from FOMC Event Windows (2026) | Realized rate changes, hike/cut/hold categories, move sizes, VIX and dollar-index changes | 43 scheduled FOMC announcements from 2021 through 2026 | Little evidence that crypto returns differed systematically from zero around announcements; realized rate variables did not robustly explain returns. VIX changes were negatively associated with crypto returns in several windows. The available surprise measure had only two nonzero observations. | Risk sentiment was more consistent than realized rate changes in several windows, but the sparse surprise measure limits claims about causal policy shocks. |
| A note on the Bitcoin and Fed Funds rate, PLOS ONE (2022) | Federal-funds rate in a longer-frequency nonlinear and spillover framework | January 2015 through February 2021 | Reported nonlinear effects and temporarily strong spillovers between federal-funds rates and Bitcoin. | A contrasting result from a different horizon and model; it does not establish a universal directional rule. |
The studies estimate different objects, so their conclusions are not direct contradictions. The New York Fed work asks about immediate reactions to identified news. The Chicago paper estimates changing market betas. The FOMC-window article examines scheduled announcements, while the PLOS ONE study models longer-frequency dependence. A weak intraday response can coexist with slower co-movement, and an association can exist without a causally identified policy effect.
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The New York Fed authors themselves caution: “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.”
Why equity markets and risk sentiment can masquerade as rate sensitivity
Interest-rate news often reprices stocks, currencies, credit and volatility at the same time. Bitcoin can move because investors become more or less willing to hold risky assets, even when the direct rate channel is weak.
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The Chicago Fed working paper reports that Bitcoin’s exposure to broad equities became statistically positive around 2020, while its exposure to 10-year Treasury bond returns was not distinguishable from zero. The paper is a working paper, not an edited journal article; the Chicago Fed states that working papers are not edited and that opinions and errors are the authors’ responsibility.
Savrul’s 2026 FOMC-window analysis likewise found VIX changes negatively associated with crypto returns in several windows, while dollar-index effects were weak and unstable. A model that omits equity returns or a risk proxy may assign a general risk-on or risk-off move to interest rates.
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1. Write the estimand before collecting data
Specify the return window, rate variable, surprise definition and unit. For example: “Bitcoin’s percentage return from 10 minutes before to 30 minutes after an FOMC announcement, regressed on the unexpected change in the expected policy path.” Decide whether you are estimating a return response, a beta or a difference between event and non-event periods.
2. Select one rate exposure at a time
- Target surprise: an unexpected change in the current federal-funds target.
- Path surprise: an unexpected revision to the expected future policy path.
- Market-yield change: a move in a Treasury yield or bond return.
- Broader monetary-policy shock: a composite measure designed to capture policy news.
Do not label all of these “the interest rate.” A target surprise and a 10-year bond return carry different information and should not be pooled without a reason.
3. Match the design to the question
Use a narrow intraday event window when the question concerns the immediate reaction to scheduled news. A tight window can reduce contamination from unrelated information, provided the surprise measure is credible and Bitcoin prices are timestamped accurately.
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Use daily or weekly data when the question concerns slower co-movement. Those frequencies provide more observations but also allow earnings, regulatory news, macroeconomic releases, market stress and crypto-specific events to enter the same observation. Account for serial dependence, changing regimes and overlapping events.
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4. Work with returns, not trending price levels
For an ordinary sensitivity estimate, pair Bitcoin returns with a rate change or surprise over the same interval. Price levels and trending rates can generate a spurious correlation. State how Bitcoin prices were sampled, which time zone was used, how exchange differences were handled and what happened to missing or asynchronous observations.
A simple specification might be written as Bitcoin return = alpha + beta × rate surprise + controls + error. The coefficient beta is meaningful only with the window, units and controls stated beside it.
5. Add controls for plausible competing drivers
At minimum, consider broad equity returns and a risk-sentiment measure such as VIX when the data and event window support them. Depending on the design, the dollar and major macroeconomic releases may also matter. Keep the model proportionate to the number of events: adding many regressors to a short sample can make estimates unstable.
6. Report uncertainty, not just a coefficient
Publish the estimated response or beta, standard error or confidence interval, sample dates, number of observations or events and significance level. “Not statistically significant” means that the study did not estimate a reliably nonzero response under that model and sample. It does not demonstrate that the true effect is exactly zero.
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7. Test robustness
- Replace the rate measure with a target, path, yield or other clearly defined exposure.
- Compare 5-minute, 30-minute, daily and weekly windows where appropriate.
- Add and remove equity, volatility and dollar controls to see whether the rate coefficient is stable.
- Split the sample into regimes, such as pre-2020 and post-2020, rather than assuming one constant beta.
- Check whether a few extreme Bitcoin moves drive the result.
- When comparing hikes, cuts and holds, separate the realized direction from the surprise relative to expectations.
How to evaluate an apparent relationship step by step
- Define the claim. Replace “Bitcoin reacts to rates” with a measurable statement naming the rate, surprise, horizon and outcome.
- Align timestamps. Use the announcement time, the Bitcoin price interval and the rate-surprise observation in the same time zone.
- Construct returns. Use percentage or log returns rather than price levels, and document the sampling and exchange or index construction.
- Estimate a baseline. Start with the rate exposure, then add controls for broad equities and risk sentiment.
- Inspect the uncertainty. Examine confidence intervals, standard errors, event counts and sensitivity to influential observations.
- Repeat across designs. Vary the event window, frequency, rate measure and sample regime without changing the conclusion after seeing each result.
- Classify the evidence. Decide whether the result is a raw association, a market-implied announcement response or a causally identified policy shock.
- State the limit. Describe exactly which sample and specification support the conclusion and what they cannot establish.
Common mistakes and how to correct them
“The Fed hiked, so Bitcoin fell because of rates”
This statement confuses an observed move with an unexpected move. If the hike was already priced in, the announcement may contain little new policy information. Check a market-implied surprise and examine the announcement window rather than the entire month.
“Bitcoin and the 10-year yield moved together, so the Fed caused it”
That comparison is an association with a Treasury return or yield. Growth expectations, inflation news, equity repricing and risk sentiment can affect both assets. Control for plausible common drivers and avoid causal language unless the identification strategy supports it.
“The coefficient was not significant, so Bitcoin is immune”
A wide confidence interval, a short sample or noisy surprise measure can make an estimate inconclusive. The New York Fed’s muted result and Savrul’s sparse surprise observations illustrate why a non-significant estimate should not be converted into an immunity claim.
“A longer sample automatically gives a better answer”
Longer samples can capture more regimes but also combine market structures that differ materially. The PLOS ONE study’s nonlinear findings through February 2021 cannot be treated as a direct replication of a high-frequency study beginning in 2017.
“Hikes, cuts and holds are clean treatment groups”
Those categories describe realized decisions, not whether the decision surprised markets. A hold can be unexpectedly hawkish or dovish; a hike can be smaller or larger than priced. Code the surprise separately.
How to compare studies without overstating the evidence
Place competing estimates side by side using the same questions:
| Comparison axis | What to record |
|---|---|
| Rate variable | Current target, expected path, policy surprise, Treasury yield or bond return, or a broader shock. |
| Identification | Raw movement, market-implied surprise, announcement event study or time-series model. |
| Horizon | Intraday, daily, weekly or longer frequency. |
| Bitcoin sample | Start and end dates, venue or index construction, and market regimes included. |
| Controls | Equities, VIX or another risk proxy, dollar and macroeconomic news. |
| Outcome and uncertainty | Return response or beta, magnitude, confidence interval or significance, and event or observation count. |
| Research status | Peer-reviewed article, central-bank staff report or unedited working paper. |
This structure prevents a Treasury-beta estimate from being presented as a Fed-surprise estimate, and prevents a nonlinear weekly result from being compared as if it were an intraday causal response.
What the current evidence allows you to say
The strongest defensible conclusion is conditional: Bitcoin’s measured relationship with interest rates varies by rate definition, horizon, sample and controls. The New York Fed’s intraday study found no robust immediate response to the monetary and macroeconomic news it examined, while the PLOS ONE study reported nonlinear and temporary spillovers in a different, longer-frequency design. Recent evidence also points to rising equity exposure and a more consistent role for risk sentiment than for realized rate changes in several FOMC windows.
None of these findings supplies a universal percentage for how much Bitcoin moves after a 25-basis-point hike or cut. Nor do they establish that a future hike will lower Bitcoin or that a cut will raise it. A useful conclusion should read “in this sample, under this specification,” followed by the rate measure, horizon, controls and uncertainty.
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