Estimators · regimes · monitoring
6M Volatility Profile: Measure the Distribution
One average can describe neither the quiet majority nor the rare tail that dominates risk. Two 6M samples may share the same mean range while one is tightly clustered and the other alternates between stillness and jumps. A volatility profile must therefore report a distribution, its clock and event composition, and the data choices that created it.
- Center
- Not enough
- Tails
- Required
- Direction
- Separate
- Measured results
- None
Description before prediction
One Average Erases the Shape of Risk
A defensible profile reports the median and other quantiles, dispersion, extreme observations, missingness, sample count, and the exact interval measured. It preserves signed returns separately so a movement statistic is never presented as directional evidence.
| Output | Question answered | What it cannot establish |
|---|---|---|
| Median | Where the middle eligible observation lies | Tail risk or a fixed “normal” range |
| Quantiles | How the empirical distribution spreads into lower and upper tails | Probability outside the observed sample without a model |
| Mean | Arithmetic center, sensitive to extremes | Typical experience when the distribution is skewed or heavy-tailed |
| Maximum | Largest observed value in the declared sample | A future worst case or stable bound |
| Signed return | Direction over the same interval | Path, spread, depth, or fill quality |
| Missingness | Where the estimator could not be computed | Zero movement or zero volatility |
This article acquired and analyzed no 6M market sample. Any numerical daily-range, tick-range, ATR, percentile, regime, or transition value would therefore be manufactured. The page defines how to produce and falsify such measurements.
Choose for the question
Different Estimators Measure Different Features of the Path
Freeze one primary estimator and a limited set of robustness alternatives before inspecting results. Use consistent units and windows. The estimator must be computed from dated 6M contracts with enough data quality for its inputs.
Close-to-close
Absolute or squared return
Simple and reproducible from declared endpoints, but it ignores movement that reverses inside the interval.
Range based
High minus low
Captures intraperiod extent but depends on bar boundaries, outlier cleaning, and valid extremes.
Intraday path
Realized variance
Sum squared intraday log returns over a fixed window. Sampling frequency and market microstructure noise require sensitivity checks.
Execution state
Spread and slippage tails
Not price volatility estimators, but essential companion distributions when the profile will inform order risk.
intraday returns = log price change at fixed intervals→realized variance = sum of squared returnsAndersen, Bollerslev, Diebold, and Labys develop realized-volatility measurement using high-frequency intraday returns. Citing the method does not establish a particular estimator, sampling frequency, or result for 6M; those remain study choices.
Condition before pooling
Decompose the Distribution by Clock, Event, and Market Quality
A full-day profile blends very different information and liquidity conditions. Predeclare DST-safe windows, scheduled-event labels, holidays, maintenance, and market-quality states. Show both unconditional and conditional distributions so readers can see composition rather than a single pooled statistic.
UTC clock window
Define half-open intervals in UTC and attach date-aware Mexico City and Chicago labels. Do not use ambiguous “CST.”
Mexico events
Use official Banxico and INEGI calendars and actual release records. Event timing does not imply response sign.
U.S. events
Use Federal Reserve, BLS, and BEA records; separate scheduled release windows from ordinary dates.
Holiday state
Version CME trading hours and shortened sessions. Equal clock length does not guarantee equal opportunity or liquidity.
Execution state
Report spread, depth, staleness, missing-book intervals and sample attrition alongside price movement.
One observation per date-window-contract
Intraday returns and quote updates form dependent paths. First summarize each declared window for one dated contract, then estimate uncertainty with date- or episode-level blocks. Do not treat every high-frequency update as an independent volatility observation.
- Clock
- DST-safe
- Events
- Official records
- Path
- Preserved
- Uncertainty
- Blocked
Recurring calendar contamination
Prevent Expiry Migration From Creating a False Volatility Spike
An unadjusted continuous series can insert the price difference between outgoing and incoming contracts as if it were a market return. A back-adjusted history can remove the visual gap while rewriting past levels. Neither is a substitute for measuring the dated contract actually trading.
Primary measurement
Dated-contract returns
Compute movement within one expiry. If the interval changes contracts, separate the two legs and the roll transaction.
Migration context
Outgoing and incoming liquidity
Preserve both expiries around the switch. Report volume, spread, depth and missingness by contract.
Robustness
Two ex-ante roll rules
Compare a frozen primary rule with another reasonable rule based only on information available then.
| Check | Required record | Reject when |
|---|---|---|
| Contract identity | Delivery month on every trade, quote and bar | Generic front-month symbol cannot be reconstructed |
| Switch decision | Rule, input availability time and selected date | Same-day final information chooses an earlier switch |
| Adjustment | Method, factor, direction, version and dated mapping | Historical levels change without a recorded transform |
| Roll interval | Outgoing return, incoming return, basis change and execution cost separately | Contract gap is counted as underlying movement |
CME Rulebook Chapter 256 defines a dated, physically delivered futures contract and its termination framework. Detailed current mechanics belong to the canonical 6M specification article.
Past-only classification
Define Volatility States Without Hindsight
“Calm,” “normal,” and “high” need training-sample thresholds and an availability time. Estimate thresholds on past eligible data, freeze the update schedule, preserve the continuous percentile, and assign a state only after the measurement window is complete.
Unavailable
Required contract, timestamp, price, quote, or coverage evidence is missing.
Eligible
Data and calendar checks pass for the declared estimator window.
Measured
The completed-window estimator is calculated with frozen rules.
Classified
A past-fitted threshold maps the measure to a named state.
Monitored
Later outcomes and transition error are recorded without relabeling history.
Relative state
Past-only percentile
Map the current completed measure into a training distribution matched by clock and contract condition. Freeze lookback, minimum sample, ties and update cadence.
Absolute state
Risk-relevant threshold
A threshold tied to execution or loss constraints may be useful, but it must use verified contract mechanics, size, costs and a declared decision purpose.
NIST’s stationarity guidance describes stable location, variance, and autocorrelation as assumptions in many time-series methods. A rolling percentile does not prove stationarity; monitor whether its calibration and state frequencies drift.
Description is not a forecast
Estimate Transition Uncertainty Before Predicting the Next State
A current high-volatility label describes the completed window. It does not imply continuation, mean reversion, or direction. Those are separate hypotheses requiring frozen horizons, matched baselines, chronological validation, and enough independent transitions.
| Transition record | Required output | Interpretation limit |
|---|---|---|
| Origin state | Past-only measure, threshold version, classification time | State is not knowable before its window closes |
| Destination | Separate forward horizon and estimator with no overlap leakage | Later direction remains a different outcome |
| Counts | Independent episodes and attrition for every state pair | Adjacent bars in one episode are not independent transitions |
| Uncertainty | Transition estimate with block-aware interval and later-period calibration | Point estimate is not a stable probability |
| Baseline | Unconditional and clock/event-matched destination distribution | Persistence cannot be inferred from regime frequency alone |
Use adjacent percentiles fixed before testing. Reject a result isolated to one cut point.
Publish all declared forward windows and their overlap controls.
Repeat across DST-safe session definitions and event-matched samples.
Use alternate ex-ante contract selection and remove splice artifacts.
Assess calibration and state frequency on untouched chronological data.
If used for trading, charge spread, slippage, fees, rolls, latency and missed fills.
Ending specification
6M Volatility Monitoring Specification
This record separates a current description from a prediction and forces every state to carry its data and threshold provenance.
| Field | Required content | Fail-closed response |
|---|---|---|
| Observation | UTC window, local labels, CME trade date, dated contract, source and coverage | Unavailable when any required field is absent |
| Estimator | Formula, sampling interval, units, outlier policy, missingness and code version | No substitution by a different range or vendor indicator |
| Context | Session, official event, holiday, roll proximity, spread and depth state | Unknown context remains unknown |
| Classification | Continuous value, training window, threshold version, fit time and named state | No state if threshold uses future data |
| Prediction claim | Separate registered model, horizon, baseline, holdout evidence and interval | Absent by default; description cannot imply it |
| Monitoring | Calibration error, drift, state frequency, missingness, tail breaches and retirement rules | Retire or revalidate when a limit fails |
No original study is reported. No original 6M volatility distribution, percentile, state, session difference, event effect, transition probability, forecast, range, or trading result is reported here. The monitoring table is a specification for future measurement, not evidence that a particular level is typical or predictive.
Sources, methods and editorial disclosure — reviewed August 13, 2026
- CME Group Mexican Peso futures contract page, 2026 FX Product Guide, and CME Rulebook Chapter 256 for current product, quotation and dated-contract context.
- CME DataMine historical-data catalog and CME Market by Order FAQ for available trade, top-of-book, depth and order-level evidence. No market dataset was purchased or downloaded.
- Andersen, Bollerslev, Diebold, and Labys, “Modeling and Forecasting Realized Volatility” for realized-volatility construction from high-frequency intraday returns.
- NIST/SEMATECH guidance on stationarity and time-series analysis for stability and temporal-dependence context.
- CME Group trading-hours and holiday notices for session, holiday and schedule-version controls.
- Banco de México 2026 publication calendar, INEGI 2026 statistical release calendar, Federal Reserve FOMC calendars, BLS release calendar, and BEA release schedule for event-control design.
Sources and methods were reviewed August 13, 2026. This page is unsponsored editorial analysis and presents a volatility-measurement and monitoring protocol, not original 6M findings.