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Ethereum (ETH) was more volatile than Bitcoin (BTC) in the latest like-for-like snapshot available for this comparison. Derivasys measured 20-day annualized realized volatility at 37.79% for ETH and 34.89% for BTC on October 9, 2026, at 20:00 UTC. Its separate 30-day, at-the-money options measure showed 46.02% implied volatility for ETH versus 34.25% for BTC.

Those are dated readings, not permanent rankings or live quotes. An earlier CME Group comparison also found higher annualized volatility for Ether—59% versus 42% for Bitcoin over its historical sample. The difference changes with the date, lookback window, price series, annualization convention and whether you measure observed movements or option-market expectations.

The short answer: ETH has been more volatile, but not always

In both measurements in the October 9, 2026 Derivasys snapshot, ETH moved more than BTC on an annualized basis:

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Measure ETH BTC What the figures mean
20-day realized volatility 37.79% 34.89% Annualized variation calculated from the preceding 20 days of observed prices; benchmark timestamp October 9, 2026, 20:00 UTC.
30-day at-the-money implied volatility 46.02% 34.25% Option-market expectation for movement over roughly 30 days, annualized; it is not a record of the movement that already occurred.

The readings support a clear answer for that snapshot: ETH was more volatile than BTC on both a realized and an implied basis. They do not show that ETH will always be more volatile. Volatility is a time-window statistic, and the result can reverse when market conditions, liquidity, positioning or the selected data source changes.

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For historical context, CME Group’s 2023 analysis reported annualized volatility of daily price movements at 59% for ETH and 42% for BTC over the period examined in that article. Those numbers belong to that earlier sample and method; they should not be combined with the 2026 readings as though they were one continuous index. Fidelity Digital Assets separately reported that Bitcoin’s one-year annualized spot volatility ranged from 40% to 50% during 2025. That Bitcoin-only range cannot establish an ETH-versus-BTC ranking because the cited passage does not provide a same-window ETH value.

What volatility measures—and what it does not

Volatility measures the size of price fluctuations, normally through the dispersion of returns over a stated period. It counts large moves in either direction. A high-volatility asset can produce a sharp gain, a sharp loss or alternating moves that leave the price near where it started. Volatility is therefore not a forecast of direction, a guaranteed loss rate or a promise of higher returns.

Realized volatility

Realized, or historical, volatility is calculated from returns that have already occurred. A provider chooses a sampling frequency—such as daily or hourly prices—computes the standard deviation of those returns, and commonly annualizes it by multiplying by the square root of the number of periods in a year. A 20-day annualized figure is thus a way of expressing recent variation on a yearly scale; it does not mean the asset is expected to move by that percentage every year.

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The exact result depends on details that are easy to overlook: the exchange or composite price, the currency denomination, the closing-time convention, the number of observations, whether returns are simple or logarithmic, and the annualization factor. Two reputable providers can report different realized-volatility figures for the same asset and dates if those inputs differ.

Implied volatility

Implied volatility is backed out of option prices using an option-pricing model. It represents the movement that option traders are pricing for the option’s remaining life, given the strike, maturity, interest-rate assumptions and other inputs. The Derivasys figure is a 30-day at-the-money reading, so it describes the market’s priced expectation for an option near the current underlying price, annualized for comparison.

Implied volatility includes compensation for uncertainty, supply and demand for particular options, hedging pressure and risk premia. It can rise when traders pay more for protection even before large spot moves occur. It should never be described as realized price movement, and it is not a guaranteed forecast.

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Why annualization can mislead

Annualization puts different windows on a common scale, but it does not make them equivalent. A 20-day realized number, a 30-day implied number and a multi-year daily-return statistic answer different questions. Short windows can be dominated by one liquidation, regulatory headline or protocol event. Longer windows blend calm and turbulent regimes and usually produce a different ranking or spread.

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The evidence in one view

Source and date ETH observation BTC observation Metric and qualification
Derivasys, October 9, 2026, 20:00 UTC 37.79% 34.89% 20-day annualized realized volatility; recent benchmark snapshot, not a live quote.
Derivasys, October 9, 2026, 20:00 UTC 46.02% 34.25% 30-day at-the-money annualized implied volatility; option-market measure, not realized movement.
CME Group Economic Research, 2023 59% 42% Annualized volatility of daily price movements over the historical comparison period used in the article; not current.
Fidelity Digital Assets, 2026 Not stated 40–50% Range for Bitcoin one-year annualized spot volatility during 2025; no paired ETH comparator in the cited passage.

The most defensible conclusion is narrow: ETH had the higher reading in the paired Derivasys snapshot, and Ether also had the higher value in the older CME comparison. The evidence does not support saying ETH is intrinsically or permanently more volatile.

Why ETH and BTC can have different volatility

1. Different monetary designs and network purposes

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 connected to transactions, applications and the operation of that network. Its supply mechanics and use cases differ from Bitcoin’s monetary design.

Those differences can change how investors value each asset, how they react to news and how much speculative demand is present. They are structural contributors, not a proof that one specific feature caused the spread in a particular month. Network usage, fee conditions, staking economics, token supply changes and expectations about future applications can all alter the balance between buyers and sellers.

2. Greater sensitivity to technology shares and the dollar

CME’s analysis found a modest positive association between technology-share performance and ETH relative to BTC. In that historical relationship, stronger technology-stock performance tended to coincide with ETH outperforming BTC, while weakness in technology shares could weigh more heavily on ETH relative to BTC.

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The same analysis found that a stronger U.S. dollar tended to weigh more on ETH relative to BTC. CME economist Erik Norland summarized the relationship in the sentence, “The USD appears to have the opposite effect.” The statement concerns movements in the ETH/BTC relationship, not a rule that every dollar rally makes ETH fall or that BTC must rise.

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These are observed associations, not causal laws or dependable forecasts. The relationship can change as crypto markets mature, investor bases shift and macroeconomic regimes change.

3. Bitcoin’s halving changes supply expectations

CME reported that Bitcoin’s April 20, 2024 halving reduced estimated annual new issuance from roughly 330,000 BTC to about 165,000 BTC. A smaller flow of newly issued coins can affect the balance between new supply and demand, but the market may anticipate a scheduled halving well in advance.

CME’s discussion suggests that prices can move before the event as traders position for the expected supply change. It also argued that market maturation may help explain the comparatively modest subsequent response it described. The halving is therefore an important part of Bitcoin’s volatility narrative, but it is not a mechanical trigger that determines the direction or size of every move.

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4. Shared crypto and macro shocks, with different amplitudes

BTC and ETH often respond to the same broad forces: changes in global liquidity, interest-rate expectations, risk appetite, regulation, exchange failures, leverage unwinds and large flows into or out of digital-asset markets. Shared news can make their prices move together while leaving one asset with a larger percentage swing.

CME’s 2023 work described historically high BTC/ETH correlation. Its later discussion also described positive but imperfect relationships between crypto assets and Nasdaq 100 returns. Correlation tells you whether returns tend to move in the same direction; it does not tell you that the assets have equal volatility or that one asset’s risk has disappeared through diversification.

5. Derivatives, hedging and market structure

Volatility depends partly on where and how prices are formed. Spot markets, perpetual contracts, futures and options have different liquidity, leverage and liquidation mechanics. A period with crowded leverage can produce forced buying or selling that amplifies a move. Options dealers may hedge changing exposures, affecting short-term demand for the underlying asset.

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Fidelity Digital Assets noted that derivatives and hedging have become important in institutional digital-asset markets. That context shows why positioning matters, but it does not by itself prove that derivatives caused the ETH/BTC volatility difference in the Derivasys snapshot. To establish a causal effect, an analysis would need aligned position, flow and price data for the same period.

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6. Different investor narratives and use cases

Bitcoin is commonly treated as a scarce monetary asset, while Ethereum is also valued for the activity and optionality of a programmable network. Investors may therefore react differently to application growth, transaction demand, upgrades, staking changes, technology-stock valuations or shifts in the dollar. When expectations are uncertain, the asset with more sensitive or rapidly changing narratives can experience larger repricing.

This explanation should remain qualified. A narrative can attract buying or selling without being measurable as a single cause, and the same news can have opposite effects in different market regimes.

How to make a fair Bitcoin-versus-Ethereum comparison

If you are building a chart, investment memo or risk report, align the inputs before comparing the percentages. The following process prevents most false conclusions.

  1. Choose one price source. Use the same exchange, composite index or institutional benchmark for BTC and ETH. Mixing an exchange-specific BTC series with an aggregated ETH series can create an artificial spread.
  2. Use the same currency. Compare BTC/USD with ETH/USD, or compare both in another explicitly stated currency. A currency conversion can add its own movement.
  3. Set identical start and end times. State the time zone and whether the window ends at a daily close, an hourly bar or a precise timestamp. Crypto trades continuously, so an arbitrary close can materially affect a short window.
  4. Match the sampling frequency. Daily returns and hourly returns capture different jumps and microstructure noise. Do not compare daily BTC volatility with hourly ETH volatility.
  5. Match the lookback period. If BTC uses 20 observations, ETH must use the same 20 observations. Label the period in both calendar dates and number of observations.
  6. Apply one annualization convention. Use the same factor and return definition. If you report non-annualized volatility, say so rather than implying the values are directly comparable with annualized statistics.
  7. Keep realized and implied columns separate. Realized volatility describes past returns; implied volatility describes option prices and expectations. A 20-day realized value should not be ranked directly against a 30-day implied value as one series.
  8. Record the option slice when using implied volatility. Include maturity, strike or moneyness, settlement currency and whether the value is a midpoint, index or tradable quote. At-the-money and deep-out-of-the-money options can show different implied volatility.
  9. Show the date beside every percentage. A number without its observation date invites readers to treat a historical reading as current.
  10. Inspect drawdowns separately. Routine return variation and maximum peak-to-trough loss answer different risk questions. Add an absolute drawdown column instead of treating volatility as a substitute for crash analysis.
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Volatility versus correlation, drawdown and risk

Volatility is not drawdown

Volatility summarizes the dispersion of periodic returns. Drawdown measures the decline from a previous peak to a later trough. An asset can have high volatility with rapid recoveries, or moderate measured volatility followed by a prolonged drawdown. A risk review should show both metrics and state the measurement window.

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Correlation is not relative volatility

If BTC and ETH have a high positive correlation, they often move in the same direction, but ETH can still move farther on each day. Correlation is calculated from paired returns; volatility is calculated separately for each return series. A portfolio containing both assets may receive less diversification benefit than expected when correlation rises during a sell-off.

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Implied volatility is not a promise

Option prices reflect supply and demand, hedging costs and risk premia as well as expected movement. Implied volatility can be too high or too low after the fact. It is useful for understanding what the options market is pricing, not for guaranteeing a future range.

What the higher ETH reading means in practice

For a spot holder

A higher ETH volatility reading means a wider distribution of potential short-term outcomes, not a directional call. Position size, time horizon and the ability to tolerate interim losses matter more than choosing the asset with the larger historical percentage. A holder comparing allocations should model both ordinary daily variation and a severe drawdown rather than relying on a single annualized statistic.

For a trader using leverage

Higher volatility can increase both opportunity and liquidation risk. Leverage magnifies a percentage move, while perpetual-futures funding, margin rules and exchange liquidation engines can add costs or force a position to close. A volatility figure alone does not tell you the appropriate leverage; that decision requires the contract’s margin schedule, stop policy and a loss amount you can afford.

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For an options user

Compare the implied volatility of the exact maturity and strike you intend to trade with the realized volatility of a comparable historical window. A high implied reading may reflect expensive protection or strong demand for optionality. A low implied reading can still precede a large move. Examine the bid-ask spread, liquidity, skew and the risk of early or forced adjustments before treating a headline percentage as a trade signal.

For a portfolio analyst

Report BTC and ETH in separate columns for realized volatility, implied volatility, correlation and drawdown. Include the data source, timestamp, lookback and annualization method in the chart subtitle or table footnote. This prevents a historical CME statistic, a 2025 Bitcoin-only range and an October 2026 options snapshot from being read as one synchronized dataset.

Common mistakes and how to avoid them

  • Calling the Derivasys snapshot live. It is benchmarked to October 9, 2026, 20:00 UTC. Label it as a dated observation.
  • Mixing realized and implied volatility. Put the 20-day realized and 30-day implied readings in different rows or charts.
  • Presenting the CME figures as current. The 59% ETH and 42% BTC values describe the historical period in CME’s 2023 article.
  • Using Fidelity’s Bitcoin range as a paired comparison. The 40–50% one-year Bitcoin range for 2025 has no same-window ETH figure in the cited passage.
  • Claiming that ETH is always more volatile. The available observations are consistent with ETH often being more reactive, but they do not establish an invariant ranking.
  • Turning associations into causes. Technology shares, the dollar, supply expectations and derivatives are plausible contributors identified in historical analysis; none alone proves why a particular episode occurred.
  • Ignoring the price series. Exchange selection, time zone, liquidity and sampling frequency can change a short-window result.
  • Equating higher volatility with a guaranteed higher return. Volatility measures movement size in both directions.
  • Assuming correlation provides full diversification. BTC and ETH can move together during stress even when their individual volatility differs.

A practical checklist for reading the next volatility report

  • Is the number realized or implied?
  • What are the exact start and end dates and the time zone?
  • How many observations and what sampling frequency were used?
  • Is the percentage annualized, and which convention was applied?
  • Are BTC and ETH measured from the same price source and currency?
  • For options, what maturity, strike and moneyness are represented?
  • Does the comparison include correlation and drawdown, or only volatility?
  • Are the figures historical observations, a current quote or a forward-looking market price?
  • Could a one-off liquidation, protocol event, macro announcement or supply event dominate the selected window?
  • Are structural explanations being presented as contributors rather than proven single causes?

Bottom line for Bitcoin versus Ethereum volatility

ETH was more volatile than BTC in the most recent paired evidence cited here: 37.79% versus 34.89% for 20-day realized volatility, and 46.02% versus 34.25% for 30-day at-the-money implied volatility, both measured in the Derivasys snapshot dated October 9, 2026. CME’s older historical comparison points in the same direction, at 59% for ETH versus 42% for BTC, but it uses a different period and method.

The spread can reflect different supply rules and network uses, ETH’s observed sensitivity to technology shares and the dollar, Bitcoin’s halving-driven supply expectations, shared macro shocks, and changing spot and derivatives market structure. These factors help explain why the assets can move together while ETH swings more, but they do not make the ranking permanent. Any serious comparison should attach the metric, lookback, source, timestamp and annualization method to every percentage.

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