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Comparative volatility: BTC, ETH, SOL and ICP

A volatility comparison is only as meaningful as the estimator, the window and the series behind it. This page sets out how realised volatility is computed for the four assets, why the choice of window changes the ranking, and what the unequal histories in the panel mean for any comparison drawn across them.

Last reviewed 2026-09-21Source: Methodology reference; no dataset is published on this pageEstimator definitions follow standard practice; no volatility figure is asserted.

The estimator

Realised volatility is the standard deviation of a return series, annualised. It is computed by taking the returns over the window, calculating their standard deviation, and scaling that figure to a yearly basis by multiplying by the square root of the number of observations in a year. For daily returns the scaling factor is the square root of three hundred and sixty-five, which is the conventional choice even though crypto trades every day of the year. The result is expressed as a percentage and read as the typical annualised size of a move.

The estimator assumes the returns are independent and identically distributed, and crypto returns are neither. They are autocorrelated in their variance, which is the volatility clustering described on the correlation page, and their distribution has fatter tails than the normal distribution the standard deviation implicitly assumes. The practical consequence is that realised volatility understates the frequency of extreme moves: a series can have a moderate standard deviation and still produce days that the normal distribution would call impossible.

Two refinements are worth naming. The first is the exponentially weighted moving average, which gives more weight to recent observations and therefore responds faster to a change in regime at the cost of being noisier. The second is the range-based estimator, which uses the high and low of each period rather than only the close and extracts more information from the same data. Both are defensible; both produce a different number from the same series, and the estimator has to be stated.

The window decides the ranking

Volatility is not a property an asset has; it is a property a series exhibits over a period. A thirty-day window measured through a turbulent month will produce a figure several times larger than the same asset's one-year figure measured through a calm year. Because the four assets in this panel have different histories and different episodes of turbulence, the window does not merely change the magnitude of the comparison — it can change the order.

The honest presentation is therefore a set of windows rather than a single figure. A short window answers "how volatile has this asset been recently", which is what a reader assessing current conditions wants. A long window answers "how volatile has it been across the period", which is more stable and less current. Reporting both, and saying which is which, is the minimum. Reporting a single figure without its window invites the reader to treat a recent episode as a permanent characteristic.

There is a further asymmetry that a comparison has to handle. The four assets did not experience the same market events. Bitcoin's history contains the 2013 and 2017 episodes; Ethereum's begins after 2013; Solana's begins after 2017; the Internet Computer's begins after the 2021 peak. A full-history comparison is therefore a comparison of four different sets of events, and a common-window comparison is a comparison over the shortest history in the panel. Both are legitimate studies, and they answer different questions.

The panel and its histories

The four assets in the comparative panel, the start of each price history, and what that means for a common-window comparison.
Asset	Symbol	History begins	What it means for the comparison
Bitcoin	BTC	2010-07 (first exchange-quoted prices)	The longest series in the panel; a common window discards most of it.
Ethereum	ETH	2015-08 (network launch)	A mid-length series; it contains some but not all of Bitcoin's episodes.
Solana	SOL	2020-04 (mainnet beta)	A mid-length series; it contains some but not all of Bitcoin's episodes.
Internet Computer	ICP	2021-05 (genesis / public launch)	The shortest series; it sets the length of any common-window comparison.

Last reviewed 2026-09-21Source: Project launch documentation and public price-series start datesStart dates are the first date a public daily series is available, not the date of any price event.

The table is the reason a comparative volatility study has to state its window twice: once as a length and once as a pair of dates. A ninety-day window ending today and a ninety-day window ending three years ago are the same length and different studies. The dates are what let a reader check whether the window contained an episode that would dominate the result.

Method, dataset and limitations

The dataset is a panel of daily closes for the four assets, drawn from a public price aggregator and cross-checked against at least one independent series. The period is bounded by the shortest history in the panel for a common-window comparison, or stated per-asset for a full-history comparison. The retrieval date is recorded so the vintage of the series is known, and any day on which two sources disagree is reported rather than resolved silently.

The method is the annualised standard deviation of daily log returns over the stated window, with the exponentially weighted variant reported alongside where the question is about current conditions. The limitations are the ones described above: an estimator that assumes independence and normality applied to a series that has neither, a window that is a choice and can change the ranking, and a panel whose members have unequal histories and did not experience the same events.

Sources and references

The estimator follows standard practice. The series a study would use are named and linked below.

Daily price series for the four assets. CoinGecko, CoinGecko API documentation: the historical daily close series used to build the return panel, with the retrieval date recorded on each study.
Reference and cross-check series. Coin Metrics, Community Network Data: an independent daily series used to check that a price move is not an artefact of a single venue's quote.
On-chain and market-structure context. Glassnode, Glassnode API documentation: the realised-capitalisation and supply series used to describe the market each asset trades in, not to compute the return panel.
Asset-level reference data. Messari, Messari API documentation: a third series used where the first two disagree, so the disagreement can be reported rather than hidden.
Bitcoin's own record, for the baseline. Bitcoin Data Guide, Bitcoin Price History and Data Sources & Methodology: the site's own compiled daily record and the provenance rules that apply to it.

Realised volatility and the annualisation convention. NIST/SEMATECH, e-Handbook of Statistical Methods — Standard Deviation: the definition of the standard deviation and the assumptions behind it.
Why volatility clusters. Robert Engle, Nobel lecture: Risk and Volatility: the autocorrelation in variance that makes a single window estimate unstable.
Bitcoin's own volatility record. Bitcoin Data Guide, Volatility Explained and Risk & Volatility: what volatility measures and why a figure without its window says very little.

Last reviewed 2026-09-21. No live market data is fetched or displayed on this page.