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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →Ethereum (ETH) was more volatile than Bitcoin (BTC) in the most recent like-for-like snapshot available. Derivasys measured 20-day annualized realized volatility at 37.79% for ETH versus 34.89% for BTC, and 30-day at-the-money options-implied volatility at 46.02% versus 34.25%, respectively, in a benchmark dated October 9, 2026. Those are dated observations from one provider, not permanent rankings or forecasts. Earlier CME Group analysis also found higher Ether volatility, but over a different historical period and with a different method.
The short answer: Ether has recently moved more than Bitcoin
Volatility measures how widely an asset’s returns vary, not whether its price is going up or down. On both measures in the Derivasys comparison, ETH showed the larger expected or observed movement. The result answers the practical question—“Which is more volatile, Bitcoin or Ethereum?”—only when the measurement window, price series and volatility definition are kept alongside the result.
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| Measure | Bitcoin (BTC) | Ethereum (ETH) | What the comparison means |
|---|---|---|---|
| 20-day annualized realized volatility | 34.89% | 37.79% | ETH had the larger variation in recent observed returns |
| 30-day at-the-money implied volatility | 34.25% | 46.02% | Options prices implied a substantially wider near-term ETH range |
| Historical annualized volatility of daily price movements in CME’s 2023 analysis | 42% | 59% | ETH was also higher in that earlier, non-comparable sample |
| Bitcoin one-year annualized spot-volatility range during 2025, according to Fidelity Digital Assets | 40–50% | Not provided in the cited passage | This is BTC-only context, not a paired ranking |
The first two rows are from Derivasys’ “BTC, ETH & SOL Options Volatility Report — 12 October 2026,” using a benchmark snapshot dated October 9, 2026 at 20:00 UTC. The CME figures come from its 2023 Economic Research article and cover that article’s historical comparison period. Because the periods and methods differ, the historical figures should not be blended into a single time series or presented as today’s quote.
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Realized and implied volatility answer different questions
Realized volatility looks backward
Realized volatility is calculated from actual price returns over a stated lookback. A 20-day annualized reading summarizes how variable the recent daily moves were and expresses that variability on an annualized scale. Annualization makes figures easier to compare, but it does not mean the asset will repeat the same pattern for a year.
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Important details include the exact close or intraday series, the currency used, the sampling frequency, the number of observations and the provider’s annualization convention. Changing any of those choices can change the result. A 20-day reading is therefore a description of one recent window, not a permanent property of a coin.
Implied volatility looks forward through options prices
Implied volatility is inferred from option premiums. The 30-day at-the-money number in the Derivasys snapshot represents the movement options traders were pricing for an approximately one-month horizon around the current price, after accounting for the option’s strike and other inputs. It is not a measurement of what prices actually did during the preceding 30 days.
Implied volatility can rise because traders expect a catalyst, because they are paying more for protection, or because market liquidity and positioning have changed. It is a market price for uncertainty rather than a guaranteed forecast. Comparing realized and implied readings without labeling them can create a false impression that one asset has a single, definitive volatility percentage.
What the recent numbers do—and do not—prove
The October 2026 snapshot establishes that ETH had both higher recent realized variation and higher option-implied variation than BTC on the provider’s selected measures. It does not establish that ETH is always more volatile. Rankings can reverse when the lookback moves through a market shock, a quiet period or an asset-specific event.
CME’s earlier comparison points in the same direction: its annualized daily-move figures were higher for Ether than Bitcoin. That is useful as a historical cross-check, but it is not a current reading. The Fidelity Digital Assets figure adds context about Bitcoin alone: its 2026 outlook says one-year annualized spot volatility for BTC ranged from 40% to 50% during 2025. Since the cited passage supplies no same-window ETH value, it cannot rank the two assets.
A defensible statement always carries the metric and date: “ETH exceeded BTC on Derivasys’ 20-day realized and 30-day at-the-money implied measures in the October 9, 2026 snapshot.” A statement such as “Ethereum is 10% more volatile” drops the window, the measure and the fact that the percentage-point gap differs between realized and implied volatility.
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Why Bitcoin and Ethereum can have different volatility
Different network economics and uses
Bitcoin is designed around an algorithmically constrained supply and scheduled issuance reductions. Ether is the native asset of a smart-contract network whose demand is tied to transactions, applications, settlement and the network’s changing supply mechanics. These differences produce different sources of buying, selling and expectations.
Bitcoin’s monetary narrative can concentrate attention on macroeconomic conditions, institutional allocation and supply events. Ether can respond to activity and sentiment around decentralized applications, smart-contract usage, network upgrades and competing platforms. A change in any of those expectations can alter positioning in ETH without an equivalent change in BTC.
These are plausible structural contributors, not a proof that one feature caused the volatility spread in a particular week. The same network can also experience periods in which its supply or usage story matters less than a broad market liquidation.
Technology stocks and the U.S. dollar
CME’s analysis found a modest positive association between technology-share performance and ETH relative to BTC. In practical terms, when technology shares were stronger in the historical relationship it examined, ETH tended to perform better relative to Bitcoin. The analysis also found that a stronger U.S. dollar tended to weigh more on ETH relative to BTC. CME economist Erik Norland summarized the dollar discussion with the sentence, “The USD appears to have the opposite effect.”
Those are observed relationships, not causal laws or reliable forecasts. Equity correlations can change, and a dollar move can coincide with rates, liquidity or risk-aversion changes that affect both tokens. Use the relationships as hypotheses to monitor, not as a trading rule that predicts the next ETH/BTC move.
Bitcoin’s halving and supply expectations
CME reported that the April 20, 2024 Bitcoin halving reduced estimated annual new issuance from roughly 330,000 BTC to about 165,000 BTC. A lower flow of newly created coins can affect the balance between new supply and demand, but markets may anticipate a scheduled event well before it occurs.
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CME’s later discussion suggested that earlier price moves may have incorporated the halving expectation and that a more mature market could help explain the comparatively modest response it described after the event. This matters for volatility analysis: a supply event can be economically important while producing less additional turbulence than traders expected, especially when positioning has already adjusted.
Shared crypto shocks, different-sized swings
BTC and ETH often respond to the same news: changes in global liquidity, regulation, exchange conditions, risk appetite or forced deleveraging. CME reported historically high BTC/ETH correlation in its 2023 analysis. High correlation means the assets often move in the same direction; it does not mean they move by the same percentage.
CME’s later market discussion also described positive but imperfect relationships between crypto assets and Nasdaq 100 returns. A broad sell-off can therefore push both tokens lower while ETH falls farther, or a crypto-specific event can separate their paths. Correlation describes co-movement, whereas volatility describes each asset’s own swing size. A portfolio can have highly correlated assets with very different individual volatility.
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Trading activity, derivatives and market structure
Volatility depends partly on where and how prices are formed. Spot markets, perpetual contracts, dated futures and options can have different liquidity, leverage and participant mixes. A thin order book can amplify a market order; crowded leverage can turn a modest price move into liquidations; options hedging can add buying or selling pressure as prices move.
Fidelity Digital Assets notes that derivatives and hedging have become important in institutional digital-asset markets. That context does not establish that derivatives caused the BTC/ETH gap in the cited snapshot. It does explain why an implied-volatility reading can diverge from recent spot behavior and why the same asset can show different volatility across venues or instruments.
How to make a fair Bitcoin-versus-Ethereum volatility comparison
A useful comparison is an exercise in matching definitions before comparing numbers. Use this checklist when reading a chart or building your own:
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- Same price source: use the same type of spot index or the same derivatives market for both assets.
- Same currency: compare BTC/USD with ETH/USD, or use another single quote currency consistently.
- Same window: align the start and end dates and the lookback length.
- Same sampling frequency: daily closes and hourly returns measure different behavior.
- Same annualization convention: confirm how the provider scales the return dispersion.
- Same volatility type: keep realized and implied columns separate.
- Same option definition: for implied volatility, match maturity, moneyness and contract conventions.
- Separate drawdown from routine variation: a maximum loss from a peak is not the same statistic as the standard deviation of returns.
- Track correlation separately: co-movement is not a substitute for either asset’s volatility.
When these conditions are not aligned, a larger number may reflect methodology rather than a genuine difference in market behavior. The Derivasys report supplies one 20-day realized comparison and a separate 30-day implied comparison; the CME statistics use another period and should remain separate.
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How to interpret a higher volatility reading
It means a wider distribution of outcomes, not a guaranteed loss
Higher volatility increases the size of potential moves in either direction. ETH can rise faster than BTC during a favorable period and fall faster during a stressed period. Volatility alone does not reveal the direction, the probability of a gain, or the maximum possible loss.
It changes position sizing and risk limits
If two positions have the same dollar value, the one with higher expected variability contributes more uncertainty to a portfolio. A risk-aware investor can respond by using a smaller position, wider or more carefully chosen risk limits, less leverage, or a longer observation period. Those are portfolio decisions, not conclusions that ETH or BTC will outperform.
It can change quickly
Volatility is state-dependent. A scheduled network event, a macroeconomic announcement, a sharp move in the dollar, a technology-stock sell-off, a major liquidation or a change in options demand can move the measure rapidly. A reading from October 9, 2026 should be dated whenever it is quoted and should not be presented as a live value later.
A practical workflow for comparing BTC and ETH volatility
- Define the question. Decide whether you need recent realized movement, forward-looking options pricing, drawdown risk or co-movement. Do not use one statistic to answer all four questions.
- Choose the observation window. Short windows react quickly to events; longer windows smooth them. Record the exact dates rather than describing a number as “current.”
- Align the data. Use matching price sources, quote currencies, timestamps and sampling intervals. Check for missing observations and unusual venue-specific spikes.
- Calculate or obtain the metric consistently. For realized volatility, apply the same return definition and annualization to both assets. For implied volatility, match option maturity and at-the-money conventions.
- Compare the spread and the level. Record both assets’ values, the percentage-point difference and the relative ratio, while keeping the metric’s units clear.
- Inspect the surrounding market. Note major moves in technology shares, the U.S. dollar, rates, crypto-wide liquidity and derivatives positioning. Treat these as possible contributors, not proven causes.
- Check robustness. Repeat the comparison over another window or with another reputable price source. If the ranking changes, report that instability instead of forcing a single conclusion.
- Translate the result into a decision. Use the volatility information to set exposure and risk controls. Do not turn a historical ranking into a forecast of returns.
Common mistakes that produce misleading answers
Calling implied volatility “what the asset actually moved”
Options-implied volatility is forward-looking and market-priced. It can be high even after a quiet realized period, or low before an unexpected shock. Always label it as implied and include the option horizon.
Combining mismatched periods
Putting CME’s historical 59% ETH and 42% BTC beside Derivasys’ October 2026 readings as though they were consecutive observations creates a false trend. They come from different windows, providers and methods.
Using a BTC-only statistic to rank BTC and ETH
Fidelity’s 40–50% Bitcoin range for 2025 provides useful context for BTC’s own risk, but no ETH comparator in the cited passage. It cannot answer which asset was more volatile during that period.
Treating correlation as equal risk
BTC and ETH can move together while ETH’s individual moves are larger. Correlation belongs in a separate column from each asset’s volatility.
Assuming an explanation is a proven cause
Supply schedules, technology-share sensitivity, dollar relationships and derivatives activity can help explain differences. Historical associations do not demonstrate that one factor caused a particular volatility episode, and they do not guarantee the same relationship in the future.
Ignoring market microstructure
Venue selection, liquidity, leverage, contract design and timestamp conventions can alter both returns and volatility estimates. A chart without those definitions is difficult to audit.
What the evidence supports for different reader questions
- Which was more volatile in the recent matched snapshot? ETH, on both the 20-day realized and 30-day at-the-money implied measures reported by Derivasys.
- Has ETH always been more volatile? No such permanent rule is established. The historical CME comparison and the recent snapshot point the same way, but each covers a specific period.
- Why might ETH swing more? Its smart-contract-network uses, different supply mechanics, observed sensitivity to technology shares and the dollar, and distinct derivatives positioning are plausible contributors.
- Does higher volatility mean ETH is a worse investment? Not by itself. It means larger potential moves and a need to match exposure and risk controls to the investor’s tolerance.
- Can Bitcoin still be highly volatile? Yes. Fidelity’s 2026 outlook placed BTC’s one-year annualized spot volatility between 40% and 50% during 2025, even though that figure does not rank BTC against ETH.
Bottom line for readers comparing the two assets
Use ETH’s higher reading as a dated observation, not a permanent label. In the October 9, 2026 Derivasys snapshot, Ether had the larger recent realized movement and the larger options-implied movement. CME’s earlier analysis found the same historical ordering, while also showing why period and method matter. Bitcoin’s constrained issuance, Ether’s smart-contract economy, their different responses to technology shares and the U.S. dollar, shared crypto shocks, and evolving derivatives markets can all influence the gap. None supplies a single mechanical explanation or a reliable forecast. The sound comparison is always metric-specific, date-specific and based on matched data.
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