Direct answer
Volatility in safe haven currencies can be measured using standard statistics computed from their price changes over a defined time window. These measures describe how much prices move, not where they will go.
A key idea is to separate three parts: (1) the definition of volatility, (2) the data series and time horizon used to compute it, and (3) real-world limitations such as market conditions, execution, and spreads. Because these choices vary, volatility figures are comparable only when the inputs match.
Mechanism: defining and measuring volatility
Before discussing implications, define volatility as variation in price over time. In practice you first choose the price series (for example, an exchange rate quoted as currency A versus currency B) and the time step (daily, hourly, or another interval).
A common approach is returns-based volatility. For each step, compute a return (often the logarithm of the ratio between consecutive prices). Then estimate volatility as the standard deviation of those returns over the selected window.
Example (with explicit assumptions): assume you have one price series sampled every day for 30 consecutive days. Compute daily log returns for those 30 days. The 30-day volatility estimate is the standard deviation of the 30 daily returns. To compare different horizons, you may annualize by scaling with the square root of time—but that assumes stationarity (that the statistical behavior is similar across time), which often fails in real markets.
Another widely used measure is range-based volatility, such as average true range (ATR), which uses the distance between recent high/low levels and accounts for gaps between periods. Range measures can be sensitive to how “high” and “low” are recorded (data source differences), and they still describe movement magnitude rather than prediction.
Evidence or example: comparing measurement choices
Consider two analysts measuring “safe haven currency volatility” with different windows. Analyst A uses a short window (e.g., 10 trading days) and Analyst B uses a longer one (e.g., 90 trading days). Even if both use the same method, they can report different volatility levels because short windows respond quickly to recent events, while longer windows smooth them.
Now consider another difference: using close-to-close returns versus using returns computed from a different sampling time. If the data feed records prices with different timing conventions (for example, at different reference times), the measured volatility may change.
These examples highlight a control point: you can independently verify the calculation by replicating the steps with the same price series, the same sampling frequency, and the same formula. If inputs differ, the measurement is not the same measurement.
You can also measure volatility of correlation rather than just level, by computing how returns move together across currencies over rolling windows. This can help describe whether “safe haven” behavior is stable, but it still does not establish that future moves will follow past patterns.
Limitations and risks: what volatility can and cannot tell you
A material limitation is that volatility measures are not direction indicators. High volatility does not imply “risk off” or “risk on” outcomes; it only quantifies movement magnitude.
Another failure mode is that historical relationships do not guarantee future results. Safe haven associations can weaken or change when market regimes shift, when liquidity changes, or when trading costs and execution effects affect observed prices.
Method limitations matter. Returns-based volatility assumes that returns represent the relevant notion of “movement,” while range-based volatility depends on high/low recording quality. Annualization by square-root-of-time scaling assumes stable behavior across horizons, which may not hold.
Finally, outcomes vary with market conditions, costs, execution, and jurisdiction. Even if two observers compute the same statistic formula, they may use different data sources or contract specifications, producing different volatility estimates.
Verification or next question
To verify a volatility figure you encounter, check: (1) the exact currency pair or exchange-rate definition, (2) the data source and sampling frequency, (3) the time window length, (4) the formula used (returns standard deviation, ATR, or another method), and (5) any scaling or annualization assumptions.
A useful next question is whether the volatility measure should reflect realized movement (computed from observed prices) or another concept such as implied volatility from options markets.